Agent Skills: Lessons Learned

Codex compatibility note:

• Invoke repository skills with $skill-name in Codex; this mirrored copy rewrites legacy Claude /skill-name references.
• Prefer the plan-hard skill for planning guidance in this Codex mirror.
• 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 does not receive Claude hook-based doc injection. 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)

Situation-based docs:

• Backend/CQRS/API/domain/entity changes: backend-patterns-reference.md, domain-entities-reference.md, project-structure-reference.md
• Frontend/UI/styling/design-system: frontend-patterns-reference.md, scss-styling-guide.md, design-system/README.md
• Spec/test-case planning or TC mapping: feature-docs-reference.md
• 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 saving to the most relevant reference doc.

Workflow:

1. Capture -- Identify the lesson from user instruction or experience
2. Route -- Analyze lesson content against Reference Doc Catalog, select best target file
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:

• 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
• 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.

| File | Role & Content | Injected By | Injection Trigger | Scan Skill | | -------------------------------- | ------------------------------------------------------------------------------------------------ | ---------------------------------------------------- | ----------------------------------- | ------------------------- | | project-structure-reference.md | Architecture, directory tree, tech stack, module registry, service map | subagent-init-*.cjs (8 hooks) | Agent spawn | $scan-project-structure | | backend-patterns-reference.md | Backend/hook patterns: CJS modules, CQRS, repositories, validation, message bus, background jobs | code-patterns-injector.cjs, backend-context.cjs | Edit/Write backend files | $scan-backend-patterns | | seed-test-data-reference.md | Seed/dev-data patterns: environment gate, idempotency loop, DI scope safety, command-dispatch | Referenced in config + seed workflows | Seeder/DataSeeder file edits | $scan-seed-test-data | | frontend-patterns-reference.md | Frontend patterns: components, state mgmt, API services, styling conventions, directives | code-patterns-injector.cjs, frontend-context.cjs | Edit/Write frontend files | $scan-frontend-patterns | | integration-test-reference.md | Test architecture: base classes, fixtures, helpers, service-specific setup, test runners | Referenced in config | Test file edits | $scan-integration-tests | | feature-docs-reference.md | Feature doc templates, app-to-service mapping, doc structure conventions | On-demand (skill reads) | Skill activation | $scan-feature-docs | | code-review-rules.md | Review rules, conventions, anti-patterns, decision trees, checklists | code-review-rules-injector.cjs | Review skill activation | $scan-code-review-rules | | lessons.md | General lessons — fallback catch-all. Injected on EVERY prompt (budget-controlled) | prompt-injections.cjs | Every UserPromptSubmit + Edit/Write | Managed by $learn | | scss-styling-guide.md | SCSS/CSS: BEM methodology, mixins, variables, theming, responsive patterns | design-system-context.cjs | Styling file edits | $scan-scss-styling | | design-system/README.md | Design system: tokens overview, component inventory, app-to-doc mapping | design-system-context.cjs | Design file edits | $scan-design-system | | e2e-test-reference.md | E2E test patterns: framework, page objects, config, best practices | code-patterns-injector.cjs | E2E file edits | $scan-e2e-tests | | domain-entities-reference.md | Domain entities, data models, DTOs, aggregate boundaries, ER diagrams, cross-service sync | backend-context.cjs, frontend-context.cjs | Backend/frontend file edits | $scan-domain-entities | | docs-index-reference.md | Documentation tree, file counts, doc relationships, keyword-to-doc lookup | On-demand (manual) | Manual reference | $scan-docs-index |

Key insight: Files injected automatically by hooks have higher visibility — lessons placed enforced during edits. Files injected on-demand are only seen when skills explicitly read them. Prefer auto-injected files for high-recurrence lessons.

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, 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: CJS modules, CQRS, repositories, entities, validation, message bus, background jobs, migrations, EF Core, MongoDB | docs/project-reference/backend-patterns-reference.md | Add to relevant section or Anti-Patterns section | | Frontend Angular/TS patterns: components, stores, forms, API services, BEM, RxJS, 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-docs-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 | | 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 | | Prompt rule (development-rules.md) | Rule that ALL agents must follow on every task (injected on UserPromptSubmit) | "Grep after bulk edits" → .claude/docs/development-rules.md | | System Lesson (prompt-injections.cjs) | Universal AI mistake, high recurrence, silent failure, any project | "Re-read files after context compaction" → .claude/hooks/lib/prompt-injections.cjs | | 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 System Lesson promotion (see below)
5. Present options to user with a direct user question:
   • "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 + System Lesson" — also add to prompt-injections.cjs System 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-hard instead of just saving.

System Lesson Promotion (MANDATORY evaluation)

After generalizing a lesson, evaluate whether it qualifies as a System Lesson in .claude/hooks/lib/prompt-injections.cjs. System Lessons are injected into EVERY prompt — they are the highest-visibility prevention layer.

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 System Lesson addresses the same root cause

System Lessons — Universal AI mistake prevention rules injected into EVERY prompt. Stored in injectAiMistakePrevention() → "Common AI Mistake Prevention" array. Each must be universal, high-recurrence, and silent-failure. READ .claude/hooks/lib/prompt-injections.cjs to check for duplicates before adding.

If qualified: Recommend "Doc + System Lesson" option. On user approval, append the lesson as a new bullet to the System Lessons array in prompt-injections.cjs following the existing format: `- **Bold title.** Explanation sentence.`

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

Lesson Quality Gate (MANDATORY before saving)

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, $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. Run Prevention Depth Assessment — determine if doc-only or deeper prevention needed
5. Match against Routing Table — pick the best-fit file
6. Tell the user: "This lesson fits best in docs/{file}. Confirm? [Y/n]"
7. On confirm — read target file, find the right section, append the lesson
8. 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 injected into EVERY prompt and EVERY file edit. Token budget must be controlled.

Hard limit: 10000 characters (~3333 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 > 10000 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 a direct user question: "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]"

Injection

Lessons are injected by lessons-injector.cjs hook on:

• UserPromptSubmit — docs/project-reference/lessons.md content (with dedup)
• PreToolUse(Edit|Write|MultiEdit) — docs/project-reference/lessons.md content (always)
• Pattern reference files are injected by their respective hooks (code-patterns-injector.cjs, code-review-rules-injector.cjs, etc.)

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:

Check downstream references before deleting. Deleting components causes documentation and code staleness cascades. Map all referencing files before removal. Verify AI-generated content against actual code. AI hallucinates APIs, class names, and method signatures. Always grep to confirm existence before documenting or referencing. Trace full dependency chain after edits. Changing a definition misses downstream variables and consumers derived from it. Always trace the full chain. Trace ALL code paths when verifying correctness. Confirming code exists is not confirming it executes. Always trace early exits, error branches, and conditional skips — not just happy path. When debugging, ask "whose responsibility?" before fixing. Trace whether bug is in caller (wrong data) or callee (wrong handling). Fix at responsible layer — never patch symptom site. Assume existing values are intentional — ask WHY before changing. Before changing any constant, limit, flag, or pattern: read comments, check git blame, examine surrounding code. Verify ALL affected outputs, not just the first. Changes touching multiple stacks require verifying EVERY output. One green check is not all green checks. Holistic-first debugging — resist nearest-attention trap. When investigating any failure, list EVERY precondition first (config, env vars, DB names, endpoints, DI registrations, data preconditions), then verify each against evidence before forming any code-layer hypothesis. Surgical changes — apply the diff test. Bug fix: every changed line must trace directly to the bug. Don't restyle or improve adjacent code. Enhancement task: implement improvements AND announce them explicitly. Surface ambiguity before coding — don't pick silently. If request has multiple interpretations, present each with effort estimate and ask. Never assume all-records, file-based, or more complex path.

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 thinking — every claim needs traced proof, confidence >80% to act. Anti-hallucination: never present guess as fact.

MUST ATTENTION apply AI mistake prevention — holistic-first debugging, fix at responsible layer, surface ambiguity before coding, re-read files after compaction.

Closing Reminders

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 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)

[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/hooks/lib/prompt-injections.cjs + .claude/.ck.json

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

1. DETECT: Match prompt against workflow catalog
2. ANALYZE: Find best-match workflow AND evaluate if a custom step combination would fit better
3. ASK (REQUIRED FORMAT): Use a direct user question with this structure:
   • Question: "Which workflow do you want to activate?"
   • Option 1: "Activate [BestMatch Workflow] (Recommended)"
   • Option 2: "Activate custom workflow: [step1 → step2 → ...]" (include one-line rationale)
4. ACTIVATE (if confirmed): Call $workflow-start <workflowId> for standard; sequence custom steps manually
5. CREATE TASKS: task tracking for ALL workflow steps
6. EXECUTE: Follow each step in sequence [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.

Learned Lessons

Lessons Learned

[CRITICAL] Hard-won project debugging/architecture rules. MUST ATTENTION apply BEFORE forming hypothesis or writing code.

Quick Summary

Goal: Prevent recurrence of known failure patterns — debugging, architecture, naming, AI orchestration, environment.

Top Rules (apply always):

• MUST ATTENTION verify ALL preconditions (config, env, DB names, DI regs) BEFORE code-layer hypothesis
• MUST ATTENTION fix responsible layer — NEVER patch symptom sites with caller-specific defensive code
• MUST ATTENTION use ExecuteInjectScopedAsync for parallel async + repo/UoW — NEVER ExecuteUowTask
• MUST ATTENTION name by PURPOSE not CONTENT — adding member forces rename = abstraction broken
• MUST ATTENTION persist sub-agent findings incrementally after each file — NEVER batch at end
• MUST ATTENTION Windows bash: verify Python alias (where python/where py) — NEVER assume python/python3 resolves

Debugging & Root Cause Reasoning

• [2026-04-11] Holistic-first: verify environment before code. Failure → list ALL preconditions (config, env vars, DB names, endpoints, DI regs, credentials, permissions, data prerequisites) → verify each via evidence (grep/cat/query) BEFORE code-layer hypothesis. Worst rabbit holes: diving nearest layer while bug sits elsewhere — e.g., hours debugging "sync timeout", real cause: test appsettings pointing wrong DB. ALWAYS cheapest check first.
• [2026-04-01] Ask "whose responsibility?" before fixing. Trace: bug caller (wrong data) or callee (wrong handling)? Fix responsible layer — NEVER patch symptom site masking real issue.
• [2026-04-01] Trace data lifecycle, not error site. Follow data: creation → transformation → consumption. Bug usually where data created wrong, not consumed.
• [2026-04-01] Code caller-agnostic. Functions/handlers/consumers don't know who invokes them. Comments/guards/messages describe business intent — NEVER reference specific callers (tests, seeders, scripts).

Architecture Invariants

• [2026-05-09] User name materialization MUST ATTENTION go through User.UpdateName(firstName, middleName, lastName). Domain method (src/Services/bravoTALENTS/Employee.Domain/AggregatesModel/User.cs:202-209) recomputes FullName as single source of truth. Three sites still manually patch user.FullName = user.GetFullName() after assigning name fields — src/Services/bravoTALENTS/Employee.Application/Factories/UserFactory.cs:50, src/Services/bravoSURVEYS/LearningPlatform.Application/ApplyPlatform/MessageBus/Consumers/AccountUserDeletedEventBusConsumer.cs:102, src/Services/bravoINSIGHTS/Analyze/Analyze.Application/MessageBus/Consumers/AccountUserDeletedEventBusConsumer.cs:66. Next time touching any: replace manual patch with user.UpdateName(...) to maintain invariant.
• [2026-03-31] ParallelAsync + repo/UoW MUST ATTENTION use ExecuteInjectScopedAsync, NEVER ExecuteUowTask. ExecuteUowTask creates new UoW but reuses outer DI scope (same DbContext) — parallel iterations sharing non-thread-safe DbContext silently corrupt data. ExecuteInjectScopedAsync creates new UoW + new DI scope (fresh repo per iteration).
• [2026-03-31] Bus message naming MUST ATTENTION include service name prefix — core services NEVER consume feature events. Prefix declares schema ownership (AccountUserEntityEventBusMessage = Accounts owns). Core services (Accounts, Communication) leaders. Feature services (Growth, Talents) sending to core MUST ATTENTION use {CoreServiceName}...RequestBusMessage — NEVER define own event for core to consume.

Naming & Abstraction

• [2026-04-12] Name PURPOSE not CONTENT — "OrXxx" anti-pattern. HrManagerOrHrOrPayrollHrOperationsPolicy names set members, not what guards. Add role → rename = broken abstraction. Rule: names express DOES/GUARDS, not CONTAINS. Test: adding/removing member forces rename? YES = content-driven = bad → rename to purpose (e.g., HrOperationsAccessPolicy). Nuance: "Or" fine behavioral idioms (FirstOrDefault, SuccessOrThrow) — expresses HAPPENS, not membership.

Environment & Tooling

• [2026-04-20] Windows bash: NEVER assume python/python3 resolves — verify alias first. Python may not be bash PATH under those names. Check: where python / where py. ALWAYS prefer py (Windows Python Launcher) one-liners, node if JS alternative exists.

Test-specific lessons → docs/project-reference/integration-test-reference.md Lessons Learned section. Production-code anti-patterns → docs/project-reference/backend-patterns-reference.md Anti-Patterns section. Generic debugging/refactoring reminders → System Lessons .claude/hooks/lib/prompt-injections.cjs.

Closing Reminders

• IMPORTANT MUST ATTENTION holistic-first: verify ALL preconditions (config, env, DB names, endpoints, DI regs) BEFORE code-layer hypothesis — cheapest check first
• IMPORTANT MUST ATTENTION fix responsible layer — NEVER patch symptom site; trace caller (wrong data) vs callee (wrong handling), fix root owner
• IMPORTANT MUST ATTENTION parallel async + repo/UoW → ALWAYS ExecuteInjectScopedAsync, NEVER ExecuteUowTask (shared DbContext = silent data corruption)
• IMPORTANT MUST ATTENTION bus message prefix = schema ownership; feature services NEVER define events for core services — use {CoreServiceName}...RequestBusMessage
• IMPORTANT MUST ATTENTION name by PURPOSE — adding/removing member forces rename = broken abstraction
• IMPORTANT MUST ATTENTION sub-agents MUST write findings after each file/section — NEVER batch all findings into one final write
• IMPORTANT MUST ATTENTION Windows bash: NEVER assume python/python3 resolves — run where python/where py first, use py launcher or node
• IMPORTANT MUST ATTENTION every claim needs file:line evidence — confidence >80% to act, NEVER speculate

[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/$security/$lint catch this?" — Yes → improve review skill instead.
7. BOTH gates pass → ask user to run $learn. [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.