Agent Skills: learn

Codex compatibility note:

• Invoke repository skills with $skill-name in Codex; this mirrored copy rewrites legacy Claude /skill-name references.
• Task tracker mandate: BEFORE executing any workflow or skill step, create/update task tracking for all steps and keep it synchronized as progress changes.
• User-question prompts mean to ask the user directly in Codex.
• Ignore Claude-specific mode-switch instructions when they appear.
• Strict execution contract: when a user explicitly invokes a skill, execute that skill protocol as written.
• Subagent authorization: when a skill is user-invoked or AI-detected and its protocol requires subagents, that skill activation authorizes use of the required spawn_agent subagent(s) for that task.
• Do not skip, reorder, or merge protocol steps unless the user explicitly approves the deviation first.
• For workflow skills, execute each listed child-skill step explicitly and report step-by-step evidence.
• If a required step/tool cannot run in this environment, stop and ask the user before adapting.

Codex Project-Reference Loading (No Hooks)

Codex uses static project-reference loading instead of runtime-injected project docs. When coding, planning, debugging, testing, or reviewing, open project docs explicitly using this routing.

Always read:

• docs/project-config.json (project-specific paths, commands, modules, and workflow/test settings)
• docs/project-reference/docs-index-reference.md (routes to the full docs/project-reference/* catalog)
• docs/project-reference/lessons.md (always-on guardrails and anti-patterns)

Missing/stale context route: If docs/project-config.json, the docs index, lessons.md, CLAUDE.md, AGENTS.md, or any task-required reference doc is missing or stale, auto-run $project-init or the narrow setup route ($project-config, $docs-init, $scan-all, $scan --target=<key>, $claude-md-init) before ordinary project-specific work. If Codex mirrors or AGENTS.md are missing/stale, ask the user to run $sync-codex; do not auto-run it.

Situation-based docs:

• Project structure/architecture/tech-stack/deployment/setup (any layer — backend, frontend, or infra): project-structure-reference.md
• Backend/CQRS/API/domain/entity changes: backend-patterns-reference.md, domain-entities-reference.md
• Frontend/UI/styling/design-system: frontend-patterns-reference.md, scss-styling-guide.md, design-system/README.md
• Spec authoring, docs/specs/ pathing, or TC format: feature-spec-reference.md, spec-system-reference.md, spec-principles.md
• Behavior/public-contract changes or spec-test-code sync: workflow-spec-test-code-cycle-reference.md plus the spec docs above
• Derived spec indexes/ERDs/reimplementation guides: spec-system-reference.md and source Feature Specs under docs/specs/
• Integration test implementation/review: integration-test-reference.md
• E2E test implementation/review: e2e-test-reference.md
• Code review/audit work: code-review-rules.md plus domain docs above based on changed files

Do not read all docs blindly. Start from docs-index-reference.md, then open only relevant files for the task.

Quick Summary

Goal: Teach Claude lessons that persist across sessions by generalizing each lesson to its failure mode and saving it to the carrier a future session will actually read — the best-fit prose reference doc, or docs/project-config.json when the lesson is really a machine-readable project fact.

Summary: read-this-if-nothing-else digest of the main steps —

• Generalize before anything else — climb from the incident to the reusable failure mode; a lesson naming this ticket's files/services/tools is not a lesson yet.
• Triage (recurrence + auto-fix) BEFORE routing — a lesson a review skill already catches is noise.
• Classify the carrier, don't default to prose: a lesson stating a project FACT (path, run-command, module map, convention, tooling choice) belongs in docs/project-config.json, the machine-readable map every skill reads first; a lesson stating a RULE or pattern belongs in the matching docs/project-reference/ doc. Both can apply — write the fact to config AND the rule to prose. To learn what the config holds and its exact field names, read the file or use $project-config (it runs --describe).
• Assess prevention depth — doc/config update, prompt rule, static protocol lesson, hook, test, or skill update.
• Confirm target with the user, save, then run the 3 mandatory end tasks — Learn Review → $why-review → $prompt-enhance.

Workflow:

1. Capture -- Identify the lesson from user instruction or experience
2. Route -- Analyze lesson content against the Reference Doc Catalog AND docs/project-config.json, select the best target carrier (prose doc, config field, or both)
3. Save -- Append lesson to the selected file
4. Confirm -- Acknowledge what was saved and where
5. Learn Review -- Run the mandatory 2-step end gate (Learn Review + $why-review)
6. Enhance -- Run $prompt-enhance on modified file(s) to optimize AI attention anchoring

Key Rules:

• GENERALIZE FIRST (the #1 protocol): Extract the GENERIC lesson that applies to many cases — NEVER save the specific case as-is. The user's words describe one incident; your job is to climb from that incident to the reusable rule. Strip every project/file/tool/domain name. If the saved text only helps on this exact ticket, you failed — abstract it up a level. (Enforced by the Lesson Quality Gate below.)
• Triggers on "remember this", "always do X", "never do Y"
• Triage first: pass Recurrence gate + Auto-fix gate BEFORE routing or saving
• Smart-route to the most relevant file, NOT always docs/project-reference/lessons.md
• Consider docs/project-config.json on EVERY routing decision — a lesson that is really a project fact (path, run-command, module map, tooling choice) belongs in the machine-readable map, not in prose; read it or use $project-config to know its schema before deciding
• Use exact config schema field names (node .claude/hooks/lib/project-config-schema.cjs --describe) and prefer an existing field — NEVER invent a key, and route config writes through $project-config
• Check for existing entries before creating duplicates
• Confirm target file with user before writing

Be skeptical. Apply critical thinking, sequential thinking. Every claim needs traced proof, confidence percentages (Idea should be more than 80%).

Usage

Add a lesson

$learn always use the validation framework fluent API instead of throwing ValidationException
$learn never call external APIs in command handlers - use Entity Event Handlers
$learn prefer async/await over .then() chains

List lessons

$learn list

Remove a lesson

$learn remove 3

Clear all lessons

$learn clear

Reference Doc Catalog (READ before routing)

Each docs/project-reference/ file is auto-initialized by session-init-docs.cjs hook and populated by /scan-* skills. Understanding their roles is critical for correct routing: routing is static — read the doc whose Read Trigger matches your task.

| File | Role & Content | Read Trigger (static) | Scan Skill | | -------------------------------- | ------------------------------------------------------------------------------------------------ | ---------------------------------- | ---------------------------------- | | project-structure-reference.md | Architecture, directory tree, tech stack, module registry, service map | New area / architecture work | $scan --target=project-structure | | backend-patterns-reference.md | Backend/hook patterns: CJS modules, CQRS, repositories, validation, message bus, background jobs | Editing backend / CQRS / API files | $scan --target=backend-patterns | | seed-test-data-reference.md | Seed/dev-data patterns: environment gate, idempotency loop, DI scope safety, command-dispatch | Seeder / DataSeeder file edits | $scan --target=seed-test-data | | frontend-patterns-reference.md | Frontend patterns: components, state mgmt, API services, styling conventions, directives | Editing frontend / UI files | $scan --target=frontend-patterns | | integration-test-reference.md | Test architecture: base classes, fixtures, helpers, service-specific setup, test runners | Integration test file edits | $scan --target=integration-tests | | feature-spec-reference.md | Feature doc templates, app-to-service mapping, doc structure conventions | Authoring / reading feature specs | $scan --target=feature-spec | | code-review-rules.md | Review rules, conventions, anti-patterns, decision trees, checklists | Any review skill activation | $scan --target=code-review-rules | | lessons.md | General lessons — fallback catch-all. Read on EVERY task (per project-reference-docs gate) | Every task | Managed by $learn | | scss-styling-guide.md | SCSS/CSS: BEM methodology, mixins, variables, theming, responsive patterns | Styling / SCSS file edits | $scan --target=scss-styling | | design-system/README.md | Design system: tokens overview, component inventory, app-to-doc mapping | Design / UI file edits | $scan --target=design-system | | e2e-test-reference.md | E2E test patterns: framework, page objects, config, best practices | E2E file edits | $scan --target=e2e-tests | | domain-entities-reference.md | Domain entities, data models, DTOs, aggregate boundaries, ER diagrams, cross-service sync | Backend / frontend domain work | $scan --target=domain-entities | | docs-index-reference.md | Documentation tree, file counts, doc relationships, keyword-to-doc lookup | Doc lookup / navigation | $scan --target=docs-index |

Key insight: lessons.md and code-review-rules.md are the highest-recurrence routing targets — read them on every relevant task. Place high-recurrence lessons where the matching Read Trigger guarantees a future session opens them.

Also a routing destination: docs/project-config.json (machine-readable, NOT prose)

The catalog above is prose. docs/project-config.json is the project's machine-readable map — modules/paths, framework + search keywords, test/E2E/integration run-commands, design system, architecture rules, workflow patterns — and it is what CLAUDE.md and the docs/project-reference/** docs are generated from. Every skill is told to read it BEFORE investigating, planning, or coding, so a fact recorded there reaches more sessions than the same fact written into one prose doc.

Read Trigger: every task, ahead of the prose docs. Owned by: $project-config.

Learn its shape before routing anything into it — either read docs/project-config.json directly, or invoke $project-config, which knows the schema and its exact field names:

node .claude/hooks/lib/project-config-schema.cjs --describe   # exact field names + per-field derivation notes

| Lesson really is… | Carrier | | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------- | | A project FACT a schema field already models — a path, glob, run-command, module/service map, framework or tooling choice, doc root, test/E2E/integration setup, startup or health-check command | docs/project-config.json (via $project-config) | | A RULE, pattern, or anti-pattern an agent must reason with | the matching docs/project-reference/ doc | | Both — a new fact AND the rule for using it | write the fact to config AND the rule to prose |

Rules:

• MUST check docs/project-config.json as a candidate on EVERY routing decision — why: a project fact written only as prose is invisible to the tooling that reads the config, and is silently overwritten the next time the generated docs regenerate from it.
• MUST use exact schema field names (--describe, copy verbatim) and prefer an EXISTING field over a new one — why: unknown keys are accepted as warnings (project-config-schema.cjs:618,688), so an invented key looks like it worked while no consumer ever reads it.
• No existing field fits → do NOT invent a top-level key silently. Route the lesson to prose and surface the gap to the user as a proposed schema addition — why: a schema change is a framework decision, not a side effect of $learn.
• Prefer routing the config write through $project-config (Plan → Review → Execute + validation) over hand-editing the JSON — why: it validates after each phase and knows the field names this skill would otherwise guess.
• Config carries FACTS, never prose lessons — NEVER paste a narrative lesson into a config string field. — why: the config is consumed by tooling and by every skill's prefetch; prose there bloats every session and belongs in a reference doc.

Smart File Routing (CRITICAL)

Lesson Triage Gate (MANDATORY — run FIRST, before routing or saving)

| Gate | Question | Pass | Fail → Action | | -------------- | ---------------------------------------------------------------------------------------------------- | -------------- | ---------------------------------------------------- | | Recurrence | "Would this mistake recur in a future session WITHOUT this reminder?" | Yes → continue | No → skip $learn; mistake is situational | | Auto-fix | "Could $code-review, $code-simplifier, $security-review, or $lint catch this automatically?" | No → continue | Yes → skip $learn; update the review skill instead |

Both gates must pass. A lesson review skills already catch adds noise without value. A one-off situational mistake won't be prevented by a persisted rule.

Routing Table

Route to the most relevant file based on lesson content:

| If lesson is about... | Route to | Section hint | | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------- | --------------------------------------------------------------------------- | | Code review rules, anti-patterns, review checklists, YAGNI/KISS/DRY, naming conventions, review process | docs/project-reference/code-review-rules.md | Add to most relevant section (anti-patterns, rules, checklists) | | Backend/hook patterns: modules, CQRS, repositories, entities, validation, message bus, background jobs, migrations, configured persistence | docs/project-reference/backend-patterns-reference.md | Add to relevant section or Anti-Patterns section | | Frontend patterns: components, state stores, forms, API services, styling conventions, directives, pipes | docs/project-reference/frontend-patterns-reference.md | Add to relevant section or Anti-Patterns section | | Integration/unit tests: test base classes, fixtures, test helpers, test patterns, assertions, test runners | docs/project-reference/integration-test-reference.md | Add to relevant section | | E2E tests: Playwright, Cypress, Selenium, page objects, E2E config, browser automation, visual regression | docs/project-reference/e2e-test-reference.md | Add to relevant section | | Domain entities, data models, DTOs, aggregates, entity relationships, cross-service data sync, ER diagrams | docs/project-reference/domain-entities-reference.md | Add to Entity Catalog or Relationships section | | Project structure, directory organization, module boundaries, tech stack choices, service architecture | docs/project-reference/project-structure-reference.md | Add to relevant architecture section | | SCSS/CSS styling, BEM methodology, mixins, variables, theming, responsive design, CSS conventions | docs/project-reference/scss-styling-guide.md | Add to relevant styling section | | Design system, design tokens, component library, UI kit conventions, Figma-to-code patterns | docs/project-reference/design-system/README.md | Add to relevant design section | | Feature documentation, doc templates, doc structure conventions, app-to-service doc mapping | docs/project-reference/feature-spec-reference.md | Add to relevant conventions section | | Documentation indexing, doc organization, doc-to-code relationships, doc lookup patterns | docs/project-reference/docs-index-reference.md | Add to relevant section | | Project FACTS the config models: source/module paths, globs, service or app maps, framework + search keywords, test / E2E / integration run-commands, system startup or health-check commands, doc roots, design-system or styling locations, tooling choices | docs/project-config.json via $project-config | Existing schema field, exact name from --describe — NEVER an invented key | | General lessons, workflow tips, tooling, AI behavior, project conventions, anything not matching above | docs/project-reference/lessons.md | Append as dated list entry |

Prevention Depth Assessment (MANDATORY before saving)

Before saving any lesson, critically evaluate whether a doc update alone is sufficient or a deeper prevention mechanism is needed:

| Prevention Layer | When to use | Example | | ------------------------------------------------------ | ---------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------- | | Doc update only | One-off awareness, rare edge case, team convention | "Always use fluent validation API" → docs/project-reference/backend-patterns-reference.md | | Project config field (docs/project-config.json) | The lesson is a machine-readable project FACT every skill should ground on before acting | "Integration tests need the system started first" → integrationTestVerify.startupScript / systemCheckCommand via $project-config | | Prompt rule (development-rules.md) | Rule that ALL agents must follow on every task | "Grep after bulk edits" → .claude/docs/development-rules.md | | Static protocol lesson (sync-inline-versions.md) | Universal AI mistake, high recurrence, silent failure, any project | "Re-read files after context compaction" → .claude/skills/shared/sync-inline-versions.md | | Hook (.claude/hooks/) | Automated enforcement, must never be forgotten | "Dedup markers must match" → lib/dedup-constants.cjs + consistency test | | Test (.claude/hooks/tests/) | Regression prevention, verifiable invariant | "All hooks import from shared module" → test in test-all-hooks.cjs | | Skill update (.claude/skills/) | Workflow step that should always include this check | "Review changes must check doc staleness" → skill SKILL.md update |

Decision flow:

1. Capture the lesson
2. Ask: "Could this mistake recur if the AI forgets this lesson?" If yes → needs more than a doc update
3. Ask: "Can this be caught automatically by a test or hook?" If yes → recommend hook/test
4. Evaluate Static Protocol Lesson promotion (see below)
5. Present options to user with ask the user directly:
   • "Doc update only" — save to the best-fit reference file (default for most lessons)
   • "Doc + prompt rule" — also add to development-rules.md so all agents see it
   • "Doc + Static Protocol Lesson" — also add to shared protocol lessons (see criteria below)
   • "Full prevention" — plan a hook, test, or shared module to enforce it automatically
6. Execute the chosen option. For "Full prevention", create a plan via $plan instead of just saving.

Static Protocol Lesson Promotion (MANDATORY evaluation)

After generalizing a lesson, evaluate whether it qualifies as a Static Protocol Lesson in .claude/skills/shared/sync-inline-versions.md. Static protocol lessons are baked into CLAUDE.md, mirrored into AGENTS.md, and synced to Codex carriers through project-init/sync tooling.

Qualification criteria (ALL must be true):

1. Universal — Applies to ANY AI coding project, not just this codebase
2. High recurrence — AI agents make this mistake repeatedly across sessions without the reminder
3. Silent failure — The mistake produces no error/warning; it silently degrades output quality
4. Not already covered — No existing Static Protocol Lesson addresses the same root cause

Static Protocol Lessons — Universal AI mistake prevention rules baked into static carriers. Stored in .claude/skills/shared/sync-inline-versions.md under the ai-mistake-prevention and ai-mistake-prevention:full SYNC blocks. Each must be universal, high-recurrence, and silent-failure. READ .claude/skills/shared/sync-inline-versions.md to check for duplicates before adding.

If qualified: Recommend "Doc + Static Protocol Lesson" option. On user approval, append the lesson as a new bullet to the relevant shared SYNC blocks, then run the project-init / sync pipeline so CLAUDE.md, AGENTS.md, and Codex carriers regenerate from the shared source.

If NOT qualified: Explain why (e.g., "Too project-specific", "Already covered by existing Static Protocol Lesson about X", "Low recurrence — only happens in rare edge cases"). Proceed with doc-only or prompt-rule option.

Lesson Quality Gate (BLOCKING — generalize before you save)

CORE PROTOCOL — do not skip: A $learn request always arrives as a SPECIFIC case ("don't migrate via the bus and spam Elasticsearch"). Saving it verbatim is the default failure mode. You MUST transform specific → generic BEFORE writing: name the underlying class of mistake, drop the incident's nouns, and write a rule that fires across many future cases ("migrations write the DB directly, never via message bus — applies to all migrations"). If you cannot state the lesson without naming this ticket's files/services/tools, it is NOT generic yet — climb one more abstraction level. When in doubt, save the MORE generic version; a too-specific lesson is dead weight injected on every prompt.

Every lesson MUST be root-cause level and generic across any codebase. Apply this 3-step extraction before saving:

Step 1 — Name the FAILURE MODE, not the symptom:

The failure mode is the reasoning or assumption that broke — not what the output looked like.

| Symptom (BAD — reject this) | Failure mode (GOOD — save this) | | --------------------------------- | ---------------------------------------------------------------------------------------------------------------- | | "Used wrong enum value" | "Generated code using an assumed API without verifying it exists in source" | | "Wrong namespace/import" | "Assumed project setup from convention without reading project-specific config files first" | | "Happy-path test failed in CI" | "Wrote assertions without tracing what runtime infrastructure the code path requires" | | "Set properties that don't exist" | "Assumed all types in a hierarchy share the same interface without reading the base class" | | "Always read file X before Y" | "Assumed execution context without reading the owning layer's contract — fixed at symptom site instead of cause" |

Step 2 — Verify generality:

Does this failure mode apply to ≥3 different contexts or codebases? If only one file or one specific case → go up one abstraction level. A good lesson prevents an entire class of mistakes.

Step 3 — Write as a universal rule:

• Strip ALL project-specific names, file paths, class names, and tool names
• Must be useful on any codebase, any language, any task type
• If multiple mistakes share the same failure mode → consolidate to ONE lesson, not many
• Test: "Would an AI working in Java, Go, or Python on a different project benefit from this?" If yes → good. If no → rewrite.

Anti-pattern examples:

• BAD: "Always check lib/dedup-constants.cjs for marker strings" → project-specific path
• GOOD: "When consolidating modules, ensure shared constants are imported from a single source of truth — never define inline duplicates."
• BAD: "Update .claude/docs/hooks/README.md after deleting hooks" → project-specific file
• GOOD: "Deleting components causes documentation staleness cascades — map all referencing docs before removal."
• BAD: "Read GlobalUsings.cs before adding usings in *.IntegrationTests" → project-specific file
• GOOD: "Before generating code that uses project conventions (imports, namespaces, annotations), read the project's bootstrap/configuration files for that layer — convention files override framework defaults silently."

End-Phase Learn Review Gate (MANDATORY before marking complete)

Run these 2 tasks at the end of every $learn operation:

Task 1 — Learn Review (value + generality + recurrence):

• Keep only lessons with clear prevention value.
• Lesson must be either:
  • Universal across many projects/codebases, OR
  • A stable project-wide principle (architecture invariant, naming invariant, workflow invariant).
• Reject lessons that are:
  • Specific to the current ticket/change/file,
  • Rare edge cases with low recurrence,
  • Already covered by existing lessons or review skills.
• If target is docs/project-reference/lessons.md (injected on every prompt), apply stricter bar: high impact + high recurrence only.

Task 2 — Run $why-review (adversarial challenge):

• Use $why-review to challenge whether this lesson deserves persistent memory.
• Verify:
  • Why this lesson prevents repeated mistakes,
  • Why this should be a lesson instead of a one-time note,
  • Why auto-checks ($code-review, $code-simplifier, $security-review, $lint, hook/test) are insufficient.
• If rationale is weak, rewrite at higher abstraction or skip $learn.

Routing Decision Process

1. Run Triage Gate — recurrence + auto-fix filters; stop here if either fails
2. Read the lesson text — identify keywords and domain
3. Apply Lesson Quality Gate — analyze root cause, generalize, verify universality
4. Classify the carrier — FACT vs RULE (do this BEFORE the Routing Table). Ask: "Is this a machine-readable project fact, or a rule an agent must reason with?" Fact → docs/project-config.json; rule → a prose reference doc; both → both. To decide, read the config or use $project-config (--describe) so the judgment rests on the real schema, never on a guess about what the config holds. — why: skipping this step is how a project fact ends up as prose that no tooling reads and the next regeneration contradicts.
5. Run Prevention Depth Assessment — determine if doc/config-only or deeper prevention needed
6. Match against Routing Table — pick the best-fit file (or config field)
7. Tell the user: "This lesson fits best in docs/{file}. Confirm? [Y/n]"
8. On confirm — read target file, find the right section, append the lesson (config target → route through $project-config)
9. On reject — ask user which file to use instead

Format by Target File

For docs/project-reference/lessons.md (general lessons):

- [YYYY-MM-DD] <lesson text>

For pattern/rules files (code-review-rules, backend-patterns, frontend-patterns, integration-test):

• Find the most relevant existing section in the file
• Append the lesson as a rule, anti-pattern entry, or code example
• Use the file's existing format (tables, code blocks, bullet lists)
• If no section fits, append to the Anti-Patterns or general rules section

Budget Enforcement (MANDATORY for docs/project-reference/lessons.md)

docs/project-reference/lessons.md is a static project-reference carrier read during project work. Token budget must be controlled.

Hard limit: 20000 characters (~6666 tokens). Check BEFORE saving any new lesson.

Workflow when adding to docs/project-reference/lessons.md:

1. Read file, count characters (wc -c docs/project-reference/lessons.md)
2. If current + new lesson > 20000 chars → trigger Budget Trim before saving
3. If under budget → save normally

Budget Trim process:

1. Display all current lessons with char count each
2. Evaluate each lesson on two axes:
   • Universality — How often does this apply? (every session vs rare edge case)
   • Recurrence risk — How likely is the AI to repeat this mistake without the reminder?
3. Score each: HIGH (keep as-is), MEDIUM (candidate to condense), LOW (candidate to remove)
4. Present to user with ask the user directly: "Budget exceeded. Recommend removing/condensing these LOW/MEDIUM items: [list]. Approve?"
5. On approval: condense MEDIUM items (shorten wording), remove LOW items, then save new lesson
6. On rejection: ask user which to remove/condense

Condensing rules:

• Remove examples, keep the rule: "Patterns like X break Y syntax" → just state the rule
• Merge related lessons into one if they share the same root cause
• Target: each lesson ≤ 250 chars (one concise sentence + bold title)

Does NOT apply to: Other routing targets (backend-patterns-reference.md, code-review-rules.md, etc.) — those files have their own size and are injected contextually, not on every prompt.

Behavior

1. $learn <text> — Route and append lesson to the best-fit file (check budget if target is lessons.md)
2. $learn list — Read and display lessons from ALL 12 target files (show file grouping + char count for lessons.md)
3. $learn remove <N> — Remove lesson from docs/project-reference/lessons.md by line number
4. $learn clear — Clear all lessons from docs/project-reference/lessons.md only (confirm first)
5. $learn trim — Manually trigger Budget Trim on docs/project-reference/lessons.md
6. File creation — If target file doesn't exist, create with header only

Auto-Inferred Activation

When Claude detects correction phrases in conversation (e.g., "always use X", "remember this", "never do Y", "from now on"), this skill auto-activates. When auto-inferred (not explicit $learn), confirm with the user before saving: "Save this as a lesson? [Y/n]"

How Lessons Reach the AI

Lessons and pattern references are read statically, per the project-reference-docs gate in CLAUDE.md:

• docs/project-reference/lessons.md — read on every task (the gate always includes it).
• Pattern/rule references (backend-patterns-reference.md, code-review-rules.md, etc.) — read by their matching trigger (see the Reference Doc Catalog table above).

Because the routing is static prose, hookless harnesses (Codex) load the same lessons and patterns as Claude Code.

Prompt Enhancement (MANDATORY final step)

After saving a lesson to any target file, run $prompt-enhance on the modified file(s) to optimize AI attention anchoring and token quality.

When to run:

• After EVERY successful lesson save (regardless of target file)
• Pass the specific file path(s) that were modified

What it does:

• Ensures the new lesson integrates with existing top/bottom summary anchoring
• Optimizes token usage — tightens prose, merges redundant content
• Verifies no content loss from the save operation

How to invoke:

$prompt-enhance docs/project-reference/<modified-file>.md

Skip conditions (do NOT run prompt-enhance if):

• The save was to lessons.md AND the file is under 1500 chars (too small to benefit)
• The user explicitly requests "save only, no enhance"

[IMPORTANT] Use task tracking to break ALL work into small tasks BEFORE starting — including tasks for each file read. This prevents context loss from long files. For simple tasks, AI MUST ATTENTION ask user whether to skip.

Mandatory end tasks are ALWAYS (in order):

1. "Run Learn Review (lesson value + generality + recurrence gate)."
2. "Run $why-review to challenge whether the lesson is worth persistent memory."
3. "Run $prompt-enhance <modified-file> to optimize lesson content for AI attention anchoring."

Do NOT mark the skill complete until all 3 tasks run.

AI Mistake Prevention — Failure modes to avoid on every task:

Re-read files after context changes. Context compaction, resume, or long-running work can make memory stale; verify current files before acting. Verify generated content against source evidence. AI hallucinates APIs, names, claims, and document facts. Check the relevant source before documenting or referencing. Check downstream references before deleting or renaming. Removing an artifact can stale docs, generated mirrors, configs, and callers; map references first. Trace the full impact chain after edits. Changing a definition can miss derived outputs and consumers. Follow the affected chain before declaring done. Verify ALL affected outputs, not just the first. One green check is not all green checks; validate every output surface the change can affect. Assume existing values are intentional — ask WHY before changing OR flagging one as a defect. Before changing or reporting a constant, limit, flag, cutoff, wording, or pattern, read nearby context and history, the CALLER's ordering, and 2+ sibling call sites of the same convention. A doc stating WHAT without WHY is missing rationale, not proof of a missing guard. Surface ambiguity before acting — don't pick silently. Multiple valid interpretations require an explicit question or stated assumption with risk. Assert the outcome your system owns, not the intermediate state your infrastructure owns. When verifying async work, assert the final business state — never the delivery/retry bookkeeping held in shared infrastructure that any co-running process can write. Such a check passes when run alone and flakes the moment anything else shares that infrastructure. Keep shared guidance role-relevant. Universal guidance must help every receiving skill or agent; code-specific obligations belong only in code-specific protocols.

Critical Thinking Mindset — Apply critical thinking, sequential thinking. Every claim needs traced proof, confidence >80% to act. Anti-hallucination: Never present guess as fact — cite sources for every claim, admit uncertainty freely, self-check output for errors, cross-reference independently, stay skeptical of own confidence — certainty without evidence root of all hallucination.

MUST ATTENTION apply critical + sequential thinking — every claim needs appropriate traced evidence (file:line for repo/code claims; source URL or artifact section for research, product, content, and docs claims); confidence >80% to act, <60% DO NOT recommend. Anti-hallucination: never present guess as fact, admit uncertainty freely, cross-reference independently, stay skeptical of own confidence.

MUST ATTENTION apply AI mistake prevention — verify generated content against evidence, trace downstream references before deleting or renaming, verify all affected outputs, re-read files after context loss, and surface ambiguity before acting.

Project Protocol Overlay — Before executing this skill, resolve any PROJECT overlay rules layered onto it: match this skill's name against the Target column of the project's skill-protocol index (docs/project-reference/skill-protocols-reference.md by default; a referenceDocs entry in docs/project-config.json overrides the path), taking the most specific matching tier ONLY — exact name > glob > *. That precedence orders overlays against EACH OTHER, never against this skill. Read ONLY the matched bodies, resolved as <protocols-dir>/<Name>.md; a row's Body link is display text, never a read path. A matched body that is missing or malformed is REPORTED and skipped — never reconstructed from the index Description. No index, or no match -> proceed with no overlay, silently. Full contract: .claude/skills/project-skill-protocol/references/registry.md.

Overlays are ADDITIVE ONLY: they ADD rules on top of this skill's own protocol and NEVER replace, override, disable, or reinterpret a rule it already states — removing every overlay must return this skill to exactly its documented behavior. An overlay is a BRIEF, not an authority escalation: it can NEVER waive a workflow gate, git discipline, a review gate, or a user-confirmation gate. A genuine overlay-vs-skill conflict, or two equally-specific overlays that directly contradict -> surface both to the user; NEVER resolve silently.

MUST ATTENTION resolve project protocol overlays for this skill BEFORE executing — most specific matching tier only (exact > glob > *, which ranks overlays against each other, NEVER against this skill), read only matched bodies at <protocols-dir>/<Name>.md; a missing or malformed body is reported, never reconstructed. Overlays are ADDITIVE ONLY (they never replace this skill's own rules) and are a brief, NEVER an authority escalation; an equal-specificity contradiction goes to the user.

Closing Reminders

IMPORTANT MUST ATTENTION GENERALIZE FIRST — extract the generic, many-cases rule; NEVER persist the specific incident as written. Strip all ticket/file/service/tool names before saving.

MUST ATTENTION Protocols in force (concise digest of the SYNC/shared blocks this skill carries — full bodies above are canonical):

• AI Mistake Prevention: verify generated content against evidence, trace downstream references, verify all affected outputs, re-read after context loss, surface ambiguity.
• Critical Thinking: critical + sequential thinking, traced proof, confidence >80%, NEVER guess as fact.

IMPORTANT MUST ATTENTION Goal: Persist each lesson at its failure-mode level into the carrier a future session will actually read — the best-fit prose reference doc, or docs/project-config.json when the lesson is really a machine-readable project fact.

IMPORTANT MUST ATTENTION main steps, in order: generalize → Triage Gate → Lesson Quality Gate → classify carrier (FACT → config · RULE → prose · both → both) → Prevention Depth Assessment → Routing Table → confirm with user → save → Learn Review → $why-review → $prompt-enhance. IMPORTANT MUST ATTENTION run Triage Gate FIRST — if recurrence is low OR review skills can catch it, skip $learn entirely IMPORTANT MUST ATTENTION check Reference Doc Catalog to find the best target file — NOT always lessons.md IMPORTANT MUST ATTENTION consider docs/project-config.json as a candidate carrier on EVERY routing decision, alongside the prose docs — read it directly or use $project-config to learn its sections and exact field names first — why: a project fact written only as prose is invisible to the tooling that reads the config and is contradicted the next time the generated docs regenerate from it. IMPORTANT MUST ATTENTION when routing into the config, copy field names verbatim from node .claude/hooks/lib/project-config-schema.cjs --describe, prefer an EXISTING field, and route the write through $project-config; no field fits → surface a proposed schema addition to the user instead of inventing a key — why: unknown keys only warn (project-config-schema.cjs:618,688), so an invented key looks applied while no consumer reads it. IMPORTANT MUST ATTENTION keep the config to FACTS — NEVER paste a narrative lesson into a config string field; the rule belongs in the matching prose reference doc. IMPORTANT MUST ATTENTION mandatory end tasks are ALWAYS: Learn Review → $why-review → $prompt-enhance <modified-file> (in order) IMPORTANT MUST ATTENTION break work into small todo tasks using task tracking BEFORE starting IMPORTANT MUST ATTENTION prefer auto-injected files for high-recurrence lessons (higher visibility)

Anti-Rationalization:

| Evasion | Rebuttal | | ------------------------------------------------ | ------------------------------------------------------------------------------------------------------------------------------------------ | | "It's a lesson, so it goes in a lessons doc" | Classify FACT vs RULE first. A path, run-command, or module map is config data — prose hides it from every consumer that reads the config. | | "I know roughly what the config holds" | Read it or run $project-config (--describe). Routing on a guessed schema writes a field nobody reads. | | "No field fits, I'll add a sensible key" | Unknown keys only warn. Surface a proposed schema addition to the user — never invent one silently. | | "Saving the user's exact words is most faithful" | Verbatim is the default failure mode. Climb to the failure mode; strip this ticket's nouns. | | "Small lesson, skip the end gate" | Learn Review → $why-review → $prompt-enhance run on every save, no exceptions. |

[TASK-PLANNING] Before acting, analyze task scope and systematically break it into small todo tasks and sub-tasks using task tracking.

Hookless Prompt Protocol Mirror (Auto-Synced)

Source: .claude/.ck.json + .claude/skills/shared/sync-inline-versions.md (:full blocks) + .claude/scripts/lib/hookless-prompt-protocol.cjs

[WORKFLOW-EXECUTION-PROTOCOL] [BLOCKING] Workflow Execution Protocol — MANDATORY IMPORTANT MUST CRITICAL. Do not skip for any reason.

Generic portability boundary: Reusable skills and protocol text stay project-neutral; project-specific conventions are discovered from docs/project-config.json and docs/project-reference/. Apply shared AI-SDD from shared/sdd-artifact-contract.md. Read docs/project-config.json and docs/project-reference/docs-index-reference.md, then open the project reference docs named there. For spec, test-case, behavior-change, public-contract, or docs/specs/ work, route through the local spec docs named by the docs index: feature-spec-reference.md, spec-system-reference.md, spec-principles.md, and workflow-spec-test-code-cycle-reference.md when specs/tests/code must stay synchronized. If either file or a required reference doc is missing or stale, auto-run $project-init (or the narrow lower-level route such as $project-config, $docs-init, $scan-all, or $scan --target=<key>) before ordinary project-specific work. Any supported AI tool may execute when this shared context and local docs are available.

1. DETECT: If the prompt starts with an explicit slash skill/workflow command, execute it directly. Otherwise match the prompt against the workflow catalog and skill list.
2. ANALYZE: Choose the best option: execute directly, invoke a skill, activate a standard workflow, or compose a custom step combination.
3. AUTO-SELECT: Pick the best option yourself. Do not ask the user to choose between direct execution, skill, standard workflow, or custom workflow.
4. ACTIVATE: For a selected workflow, call $start-workflow <workflowId>; for a selected skill, invoke that skill; for a custom workflow, sequence custom steps directly; for direct execution, proceed with the task.
5. CREATE TASKS: task tracking for ALL workflow/skill/custom steps before execution when the selected path has multiple steps.
6. PARALLELIZE: Before executing the task list, tag each task PAR (independent inputs + write set disjoint from every other PAR task) or SEQ (name the blocking dependency), group PAR tasks into waves, declare the wave plan, and spawn each wave's sub-agents in ONE message — all-return barrier per wave, fan-out one level deep unless a sub-agent's own definition authorizes further fan-out. Sequential-by-default is a defect when tasks are independent; do not parallelize shared write targets, output-consuming tasks, trivial single-file work, ordering a skill or workflow explicitly fixes, or user-approval gates.
7. EXECUTE: Advance per the Workflow Step Advancement & Parallel Phases rule in your context instructions — model-driven; a sub-agent completion advances a step identically to an inline call; a parallel-phase group is an all-return barrier (advance only after ALL members return, never serialize it)

Shared AI-SDD Protocol Markers

Source: .claude/skills/shared/sync-inline-versions.md

SYNC:ai-sdd-artifact-contract

AI-SDD Artifact Contract — Shared spec-driven development rules stay portable and source-owned.

1. Keep reusable AI-SDD principles in .claude; put repository-specific paths, commands, owners, products, and formats in project config/reference docs.
2. Preserve cycle: spec -> plan -> tasks -> implement -> verify -> update spec/docs.
3. Trace every requirement or invariant through decision, task, TC/test, source evidence, and docs/spec update.
4. Treat code-to-spec extraction as reference-only until accepted by the canonical spec owner.
5. Any supported AI tool may plan, implement, review, or verify with synced context; using multiple tools is optional.
6. Update .claude source first, then sync generated mirrors; do not manually edit .agents, .codex, or AGENTS.md. — why: mirrors are generated artifacts; hand-edits are overwritten on the next sync
7. If docs/project-config.json, root instruction files, or a required project-reference doc is missing or stale, auto-run $project-init or the narrow lower-level route before ordinary project-specific work.

Active reference: shared/sdd-artifact-contract.md in the active skills root.

SYNC:ai-sdd-artifact-contract:reminder

• MANDATORY Apply shared/sdd-artifact-contract.md; keep reusable AI-SDD in .claude and local rules in project docs.
• MANDATORY Code-to-spec extraction is reference-only until canonical acceptance; any supported AI tool may execute with synced context.
• MANDATORY Update .claude source before syncing generated mirrors; do not manually edit .agents, .codex, or AGENTS.md.
• MANDATORY Missing or stale project config, root instruction files, or required reference docs route project-specific work through $project-init or the narrow setup route automatically. [TASK-PLANNING] [MANDATORY] BEFORE executing any workflow or skill step, create/update task tracking for all planned steps, then keep it synchronized as each step starts/completes.

[LESSON-LEARNED-REMINDER] [BLOCKING] Task Planning & Continuous Improvement — MANDATORY. Do not skip.

Break work into small tasks (task tracking) before starting. Add final task: "Analyze AI mistakes & lessons learned".

Extract lessons — ROOT CAUSE ONLY, not symptom fixes:

1. Name the FAILURE MODE (reasoning/assumption failure), not symptom — "assumed API existed without reading source" not "used wrong enum value".
2. Generality test: does this failure mode apply to ≥3 contexts/codebases? If not, abstract one level up.
3. Write as a universal rule — strip project-specific names/paths/classes. Useful on any codebase.
4. Consolidate: multiple mistakes sharing one failure mode → ONE lesson.
5. Recurrence gate: "Would this recur in future session WITHOUT this reminder?" — No → skip $learn.
6. Auto-fix gate: "Could $code-review/$code-simplifier/$security-review/$lint catch this?" — Yes → improve review skill instead.
7. BOTH gates pass → ask user to run $learn. [CRITICAL-THINKING-MINDSET] Apply critical thinking, sequential thinking. Every claim needs traced proof, confidence >80% to act. Anti-hallucination principle: Never present guess as fact — cite sources for every claim, admit uncertainty freely, self-check output for errors, cross-reference independently, stay skeptical of own confidence — certainty without evidence root of all hallucination. AI Attention principle (Primacy-Recency): Put the 3 most critical rules at both top and bottom of long prompts/protocols so instruction adherence survives long context windows. Goal-driven execution: Define success criteria first, loop until verified, and stop only when observable checks pass. Tests verify intent: Tests must protect business rules/invariants and fail when the protected intent breaks, not only mirror current behavior.

Common AI Mistake Prevention (System Lessons)

• Re-read files after context compaction. Edit requires prior Read in same context; compaction wipes read state. Re-read before editing.
• Grep for old terms after bulk replacements. AI over-trusts find/replace completeness. Grep full repo after bulk edits for missed refs in docs/configs/catalogs.
• Check downstream references before deleting. Deletions cascade doc/code staleness. Map referencing files before removal.
• After memory loss, check existing state before creating new. Compaction wipes prior-work memory. Query current state to resume — never blindly duplicate.
• Verify AI-generated content against actual code. AI hallucinates APIs, class names, method signatures. Grep to confirm existence before documenting/referencing.
• Trace full dependency chain after edits. Changing a definition misses downstream consumers. Trace the full chain.
• When renaming, grep ALL consumer file types. Some file types silently ignore missing refs (no compile error). Search code, templates, configs, generated files.
• Trace ALL code paths when verifying correctness. Code existing ≠ code executing. Trace early exits, error branches, conditional skips — not just happy path.
• Update docs that embed canonical data when source changes. Docs inlining derived data (workflows, schemas, configs) go stale silently. Update all embedding docs alongside source.
• Verify sub-agent results after context recovery. Background agents may finish while parent compacted — grep-verify output, don't trust assumed completion.
• Cross-check full target list against sub-agent assignments. Parallel sub-agents by category miss boundary items. Reconcile union of assignments against target list before proceeding.
• Sub-agents inherit knowledge only from their agent .md definition — use custom agent types, not built-in Explore. Tool adoption = permission + knowledge + enforcement (numbered workflow step).
• Persist sub-agent findings incrementally, not as a final batch. Long sub-agents hit cutoffs before final write — findings lost. Instruct append-per-section to report file.
• When debugging, ask "whose responsibility?" before fixing. Trace caller (wrong data) vs callee (wrong handling). Fix at responsible layer — never patch symptom site.
• Test failure → record a provisional verdict before trace/edit, then investigate. Use the full five-way taxonomy: SOURCE-WRONG (production violates intent), TEST-WRONG (assertion/setup is stale), TEST-NOT-OPTIMAL (valid but fragile or low-signal test), ENVIRONMENT-BLOCKED (external state prevents a verdict), or AMBIGUOUS (intent/evidence cannot choose safely). Then trace root cause and triangulate against the governing spec (docs/specs/** if one exists) AND source. NEVER weaken an assertion, add a skip, relax a timeout, or change source merely to force green.
• Grep ALL removed names after extraction/refactoring. Primary file "done" ≠ secondary files clean. Grep entire scope for every removed symbol before declaring complete.
• Assume existing values are intentional — ask WHY before changing OR flagging one as a defect. Pattern-matching as "wrong" skips context. Before changing or reporting any constant/limit/flag/cutoff: read comments, git blame, the CALLER's ordering (the guarantee that makes the value correct usually lives in code running immediately BEFORE the cited line), and 2+ sibling call sites of the same convention. A doc stating WHAT without WHY is missing rationale, not proof of a missing guard — and in a validation pass, an accurate file:line citation proves the transcription, never the defect.
• Verify ALL affected outputs, not just the first. One build green ≠ all green. Multi-stack changes (backend/frontend/tests/docs) require verifying EVERY output.
• Evaluate fit before copying a nearby pattern. Closest example ≠ matching preconditions — verify the new context shares the same constraints, base classes, scope, lifetime.
• Holistic-first debugging — resist nearest-attention trap. Don't dive into first plausible cause. List EVERY precondition (config, env vars, paths, DB, endpoints, creds, versions, DI, data). Verify each against evidence (grep/query — not reasoning). Ask "what would falsify this?" — if nothing, it's not a hypothesis. Most expensive failure: going deeper in "obvious" layer while bug sits in layer never questioned.
• Surgical changes — apply the diff test (context-aware). Two modes: (1) Bug fix → every line traces to the bug; no restyling; orphan cleanup only for imports YOUR changes made unused. (2) Review/enhancement → implement improvements AND announce as "Enhancement beyond main request: [what]". Never silently scope-creep. Diff test: "Would this line exist if I wasn't asked to do X?" — if no, delete or announce.
• Surface ambiguity before coding — don't pick silently. Multiple valid interpretations → present each with effort: "[Request] could mean (1) [N h], (2) [N h]. Which matters?" List scope/format/volume/constraints assumptions first. If simpler path exists, say so. Never silently pick.
• [MANDATORY FIRST ACTION] ALWAYS activate a suitable skill or workflow BEFORE responding. Match task against workflow catalog + skill list; invoke via skill invocation or $start-workflow <workflowId>. NEVER answer or write code before checking. Skip = protocol violation.
• Why-Review adversarial mindset — apply when reviewing any plan, decision, or design. Default SKEPTIC not VALIDATOR: steel-man a rejected alternative, invert each stated reason ("what does it sacrifice?"), stress-test top 2-3 assumptions, run pre-mortem ("ships, fails in 3 months — what breaks?"), surface 1-2 alternatives author missed. Section presence ≠ quality; quality = causal reasoning + concrete mitigations + evidence, not "it's better" or "monitor closely".
• Front-load report-write in sub-agent prompts for large reviews. Many-file sub-agents hit budget before final write — findings lost. Design prompts so: (1) report-write is first explicit deliverable, (2) append per-file/section (not batched), (3) scope bounded so reads don't exhaust budget. Truncated mid-sentence with no report file → spawn narrower scope, don't retry same prompt.
• After context compaction, re-verify all prior phase outcomes before continuing. Summaries describe intent, not environment state (git index, filesystem, processes). On resume, FIRST audit: git status, re-read modified files, verify filesystem. Every "completed" claim is an untested hypothesis until evidence confirms.
• OOM/memory: check row count before row size. Triage: (1) Unbounded query — no DB filter for trigger? Push filter to DB; eliminates OOM. (2) Large rows? Projection reduces proportionally. Row reduction > projection in ROI.
• Assert the outcome your system OWNS, never the intermediate state your INFRASTRUCTURE owns. When testing anything asynchronous (queue/broker delivery, retries, background jobs, caches, replication), assert the final business/entity state. NEVER assert the delivery bookkeeping — consume/send status, attempt counts, last-error, row existence or counts in a broker, scheduler, or outbox/inbox table. That bookkeeping lives in shared infrastructure that ANY co-running process (a peer worker, a second replica, a leftover local container) can write, usually under a deterministic shared key, so the assertion silently tests the developer's environment instead of the system: green when run alone, flaky the instant anything else shares that broker + database. Gate question for every assertion: "would this hold no matter WHICH process did the work?" — if no, assert the converged data state instead. Corollary: process-local fault injection and in-process telemetry cannot gate work any process may perform — use them as stress amplifiers (arm → bounded window → disarm → assert convergence), never as preconditions.
• Keep domain concepts out of generic/shared/infrastructure layers. Reusable layer (shared library, framework, infra module) must reference NO consumer-specific domain concept — tenant/customer/product IDs, business entities, feature rules. Leak compiles + runs → passes review silently while coupling the "reusable" layer to one consumer. Keep shared type domain-free; push domain fields/logic down into the consumer via subclass/composition. — why: a layer coupled to one consumer's domain is no longer reusable.