Agent Skills: prompt-enhance

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:

• 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 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: Two-phase optimization — (1) Caveman Compression strips stop words + grammatical scaffolding while preserving semantic meaning; (2) Prompt Enhancement applies AI attention anchoring so AI reads and follows all instructions — producing a prompt/skill that states its objective and ultimate outcome (one consolidated Goal) in both top summary and bottom reminders so AI optimizes for the right result.

Summary:

• Two phases, in order: caveman-compress prose FIRST, then attention-anchor structure — NEVER skip or reorder.
• Enhance derives BOTH a Goal (the outcome to optimize for) AND a Summary (key things + steps to notice) for the target, and places both in its Quick Summary.
• Protect content: NEVER compress code/YAML/tables/SYNC tags, NEVER delete rules or file:line evidence; post rule-density MUST be ≥ pre.
• Route on --op (default enhance): compress = token-strip only, expand = reconstruct compressed text.

Workflow:

1. Detect — Classify target: skill file, sub-agent file (.claude/agents/*.md), protocol file, or general doc
2. Read — Read target file completely
3. Goal + Summary — Derive the target's one-sentence Goal (what it achieves + the ultimate outcome it must cause) AND its Summary (2-4 bullets of the key important things + the steps AI must notice) from the target's task, constraints, and success criteria
4. Compress — Apply caveman compression (Phase 1)
5. Enhance — Apply AI attention anchoring transforms (Phase 2)
6. Verify — No content loss, rule density ≥ pre-optimization, Goal anchored top and bottom

Key Rules:

• Operation flag (see Operation Mode): --op=enhance (default) = compress + anchor + skill-principles; --op=compress = token-strip only; --op=expand = reconstruct compressed text into fluent form (inverse Phase 1 + structural Transform 4)
• NEVER skip Phase 1 (compress) before Phase 2 (enhance) — compression removes noise, enhancement structures signal
• NEVER remove meaningful rules, constraints, code examples, or file:line evidence
• MUST ATTENTION derive the target's Goal and add it to both ## Quick Summary and ## Closing Reminders
• MUST ATTENTION derive the target's Summary (key important things + steps AI must notice) and place it in ## Quick Summary immediately after the Goal — a condensing digest at a different altitude than Workflow/Key Rules, NEVER a verbatim re-listing of them
• MUST ATTENTION skill AND sub-agent (.claude/agents/*.md) targets require the SAME Goal + Summary + Closing-Reminders structure (see When Target is a Sub-Agent File) — anchored top and bottom; NEVER alter SYNC blocks when enhancing an agent
• Post-optimization rule density (MUST ATTENTION/NEVER/ALWAYS per 100 lines) MUST be ≥ pre-optimization
• Caveman compression applies to prose only — NEVER compress code blocks, YAML, or structured tables
• Prompt quality > token count, but verbose prompts degrade quality — optimize clarity-per-token

Target File

Compress and enhance this file: <target>$ARGUMENTS</target>

No file? Ask via a direct user question. Text passed (not file path)? Apply caveman compression directly and output result.

Operation Mode (--op=)

Route on --op (default enhance). Transforms 1-3 (inline summaries, top summary, closing reminders — the shared SYNC base block below) are identical across all ops; only Phase 1 and Transform 4 differ:

| --op | Phase 1 | Transform 4 | Skill-principles + Goal | Former skill | | --------------------- | ----------------------------------------------- | ---------------------- | -------------------------- | ------------------ | | enhance (default) | Caveman Compression | Conciseness pass | Applied (skill files) | host | | compress | Caveman Compression | Conciseness pass | Skipped (pure token strip) | /prompt-compress | | expand | Language Expansion (inverse — branch below) | Structural Clarity | Skipped | /prompt-expand |

• enhance / compress → run Phase 1: Caveman Compression + Transform 4: Conciseness below. enhance additionally derives the Goal and (for skill files) applies the Universal Skill-Building Principles; compress skips both for a pure token-reduction pass.
• expand → run the Language Expansion branch below INSTEAD of Caveman Compression, and the Structural Clarity Transform 4 instead of conciseness.
• No --op provided → enhance.

--op=expand — Language Expansion branch

Reconstruct fluent, grammatically correct English from caveman-compressed text while preserving ALL semantic content (inverse of Phase 1). Run INSTEAD of Caveman Compression.

Restore (add back): articles (a/an/the); connectives matching the logical relationship (because/however/in order to); auxiliary verbs (is/are/was/has); clarifying prepositions; pronouns referencing prior nouns; subordinate clauses merging choppy sentences. Preserve exactly (never paraphrase/omit): all nouns + main verbs + adjectives, numbers/quantifiers, uncertainty qualifiers, negations (not/no/never/without), technical/domain terms, file:line paths, names/titles, time/frequency words.

Connective selection (match relationship, never arbitrary): cause→effect because/since/as a result; contrast however/although/despite; addition additionally/furthermore; sequence first/then/finally; purpose in order to/so that; condition if/when/unless; clarification specifically/that is.

Per sentence: identify core S-V-O (non-negotiable) → restore articles/auxiliaries/connectives/prepositions → merge related shorts → target 10-25 words. Skip code blocks, YAML, tables, SYNC tags, paths.

Transform 4 (expand) — Structural Clarity pass: convert prose rule-lists → bullets, enumerated conditions → decision tables, before/after examples → two-column tables. Keep as prose: explanatory context (why a rule exists), workflow narratives, anti-pattern rationale.

Verify (expand): no semantic loss (all facts/numbers/paths present), rule density post ≥ pre, no telegraphic 2-5 word prose sentences remain, code blocks untouched.

Phase 0: Detect Target Type

Before any other step, classify target:

| Target type | Detection | Action | | ------------------ | ---------------------------------------- | ------------------------------------------------------- | | Skill file | Path matches .claude/skills/**/*.md | Apply Universal Skill-Building Principles after Phase 1 | | Sub-agent file | Path matches .claude/agents/*.md | Apply Sub-Agent Required Structure after Phase 1 | | Protocol file | Path matches .claude/protocols/**/*.md | Standard 2-phase optimization only | | General doc/prompt | Any other .md file | Standard 2-phase optimization only | | Raw text | No file path provided | Apply caveman compression only, output result |

When Target is a Skill File

Target .claude/skills/**/*.md (any SKILL.md)? Apply Universal Skill-Building Principles AFTER caveman compression, BEFORE writing enhanced output.

Skill Enhancement Checklist

After caveman compression, evaluate skill against each principle, add missing structure:

| Principle | Check | Action if missing | | ---------------------------- | ---------------------------------------- | ------------------------------------------------------ | | Detect Before Act | Phase 0 / classification step present? | Add artifact-type detection before Phase 1 | | Derive, Don't Enumerate | Thinking framework vs. fixed checklist? | Replace checklist with "understand → derive → execute" | | Evidence Gates | Every claim requires file:line? | Add evidence requirement to all review steps | | Fresh Eyes Protocol | Multi-round sub-agent review defined? | Add Round 2 fresh sub-agent protocol | | Specialize by Type | Sub-agent routing table present? | Add security-auditor/performance-optimizer options | | Embed Protocols Verbatim | Protocols inline in sub-agent prompts? | Move protocol bodies inline, remove file references | | Search-Based Discovery | Any hardcoded paths/formats/IDs? | Replace with search instructions | | Dimensions > Checklists | Named dimensions with Think: prompts? | Convert checklist to dimension framework | | Recursive Quality Loop | Fix → re-review → max 3 rounds defined? | Add recursive review loop | | Anti-Rationalization Anchors | Closing reminders include evasion table? | Add evasion → rebuttal table |

When Target is a Sub-Agent File

Target .claude/agents/*.md (a custom sub-agent definition — the shape a creator skill like custom-agent emits)? Apply the Sub-Agent Required Structure AFTER caveman compression, BEFORE writing enhanced output. Same Goal + Summary + Closing-Reminders contract as a skill file — anchored top and bottom so the isolated, zero-history sub-agent optimizes for the right outcome — mapped onto the agent body (## Role → ## Workflow → ## Key Rules → ## Output).

Sub-Agent Required Structure

| Block | Location | Requirement | | ---------------------------------- | ---------------------------------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------- | | ## Quick Summary | first section after frontmatter | Present — holds Goal + Summary + Workflow + Key Rules | | **Goal:** | inside Quick Summary | One consolidated sentence — what the agent achieves AND the ultimate outcome it must cause | | **Summary:** | inside Quick Summary, immediately after Goal | 2-4 bullets — the read-this-if-nothing-else digest (key things + steps to notice); distinct altitude from Workflow/Key Rules, NEVER a verbatim re-listing | | **Workflow:** / **Key Rules:** | inside Quick Summary | Keep existing | | ## Closing Reminders | end of file, after the :reminder SYNC blocks | Present — first line **IMPORTANT MUST ATTENTION Goal:** echoes the same Goal |

• MUST ATTENTION add the missing **Summary:** and the Closing-Reminders Goal echo; lightly tighten Role/Workflow prose only — why: the structure must match a skill so creator skills emit one consistent shape.
• NEVER alter <!-- SYNC:... --> blocks or their :reminder variants — they are canonical-sync content; edit the canonical source (.claude/skills/shared/sync-inline-versions.md) instead — why: a divergent SYNC copy fails the verify-sync-divergence oracle.
• NEVER delete the agent body sections (## Role, ## Workflow, ## Key Rules, ## Output) — preserve them; only restructure the summary/closing anchors.

Phase 1: Caveman Compression

Applies to --op=compress|enhance. For --op=expand, run the Language Expansion branch (above) instead.

Aggressively remove stop words + grammatical scaffolding preserving meaning. Use only content words carrying semantic weight.

What to Remove

| Category | Examples | | ----------------------------- | ---------------------------------------------------------------------- | | Articles | a, an, the | | Auxiliary verbs | is, are, was, were, am, be, been, being, have, has, had, do, does, did | | Redundant prepositions | of, for, to, in, on, at (when meaning stays clear without them) | | Pronouns (when context clear) | it, this, that, these, those | | Pure intensifiers | very, quite, rather, somewhat, really, extremely |

What to Keep (Always)

| Category | Reason | | -------------------------------- | ------------------------------------------------------ | | All nouns | Core semantic units | | All main verbs (not auxiliaries) | Actions carry meaning | | All meaningful adjectives | Add semantic signal | | Numbers and quantifiers | at least, approximately, more than, 15, many | | Uncertainty qualifiers | appears to be, seems, might, what sounded like | | Critical prepositions | from, with, without, stuck to — change meaning | | Time/frequency words | every Tuesday, weekly, always, never | | Names and titles | Dr., Mr., Senator | | Technical/domain terms | Never simplify domain language | | Negations | not, no, never, without |

Preposition Decision Rule

• Keep when defining relationship: made from wood (keep from), stuck to wall (keep to)
• Remove when purely grammatical: system for processing data → system processing data
• Keep in/on/at for location/position: file in /src (keep) vs written in prose (remove)

Compression Examples

| Original | Compressed | Removed | | --------------------------------------------------------------------------- | --------------------------------------------------------------- | --------------------- | | "The system was designed to process data efficiently" | "System designed process data efficiently." | The, was, to | | "It removes predictable grammar while preserving the unpredictable content" | "Removes predictable grammar preserving unpredictable content." | It, the, while | | "There were at least 20 people" | "At least 20 people." | There, were | | "Made from wood and metal" | "Made from wood and metal." | nothing — from kept | | "This is a method for compressing LLM contexts" | "Method compressing LLM contexts." | This, is, a, for |

Compression Scope

Apply to:

• Prose paragraphs and explanatory text
• Bullet point descriptions
• Rule statements (keep imperative verbs)
• Section intros and transitions

Do NOT compress:

• Code blocks (any language)
• YAML frontmatter
• Structured tables (column values may be fragmented — keep as-is)
• file:line references and paths
• <!-- SYNC --> tags and their content
• Frontmatter fields

Phase 2: Prompt Enhancement

Transform 4: Token Optimization (Conciseness Pass)

Applies to --op=compress|enhance. For --op=expand, use the Structural Clarity pass (see expand branch above).

Prompt quality FIRST. Verbose prompts degrade quality — AI attention dilutes across unnecessary tokens. Optimize clarity-per-token: maximum signal, minimum noise.

What to cut:

• Filler phrases — "It is important to note that", "Please make sure to", "You should always" → just state the rule
• Redundant explanations — heading says it, body doesn't re-explain. Tables > paragraphs for structured data
• Duplicate content — merge sections saying same thing differently (except intentional top/bottom anchoring)
• Overly verbose examples — trim to minimum lines demonstrating pattern. Replace paragraph explanations with // comment in code
• Prose paragraphs for rules — convert to bullet lists or tables (AI parses structured formats faster)

What to KEEP:

• Code examples with actual file paths/patterns (AI copies these directly)
• Decision tables and lookup references
• Anti-pattern examples (before/after pairs)
• All file:line evidence and concrete paths
• Top/bottom anchoring (intentional duplication)

Evaluation metrics per doc:

• Density score — useful rules per 100 lines (higher = better)
• Savings estimate — % tokens saveable without losing information
• Risk — what breaks if cut too aggressively (e.g., AI misses a pattern)

Process

Step 0: Detect and Classify

1. Identify target type (skill file / protocol / general doc / raw text)
2. Skill file (.claude/skills/**/*.md) → apply Universal Skill-Building Principles after Phase 1

Step 1: Read and Analyze

1. Read target file completely
2. Record: current line count, rule density (MUST ATTENTION/NEVER/ALWAYS count)
3. List all READ references → classify as .claude/ (needs inline summary) or docs/ (skip)
4. Derive the one-sentence Goal (what it achieves + ultimate outcome it must cause) from target task/outcomes/guardrails; cite source lines or mark inferred with confidence
5. Derive the Summary (2-4 bullets of the key important things + the steps AI must notice) — the read-this-if-nothing-else digest at a different altitude than Workflow/Key Rules; cite source lines or mark inferred with confidence — why: the Summary condenses what matters most, it does not re-list every step/rule
6. Identify: missing Quick Summary, missing Goal, missing Summary, missing Closing Reminders, prose-heavy sections

Step 2: Caveman Compression Pass

1. Identify all prose paragraphs and bullet descriptions
2. Apply Phase 1 compression rules — remove stop words, keep semantic content
3. Skip code blocks, YAML, tables, SYNC tags, file paths
4. Verify meaning preserved after each paragraph

Step 3: Create Inline Summaries

For each .claude/ protocol reference:

1. Read the referenced file
2. Extract 2-3 key rules
3. Write blockquote inline summary
4. Keep MUST ATTENTION READ instruction on next line

Step 4: Add/Fix Top Section

• Missing Quick Summary → create from file content
• Present but weak → strengthen with Goal, Workflow, Key Rules
• Ensure **Goal:** states what the skill achieves AND the ultimate outcome it must cause — a single consolidated line (never split the objective and outcome into two separate lines)
• Ensure **Summary:** is present in Quick Summary immediately after the Goal — create if missing, strengthen if weak; it condenses the key important things + the steps AI must notice at a different altitude than Workflow/Key Rules (NEVER a verbatim re-listing of them) — why: the Goal gives the outcome, the Summary gives the read-this-if-nothing-else digest
• Protocol summaries appear before Quick Summary

Step 5: Add/Fix Bottom Section

• Missing Closing Reminders → add standard section
• Pick rules AI most commonly skips (evidence-based, task creation, pattern search)
• Echo the same Goal near the start of Closing Reminders: **IMPORTANT MUST ATTENTION Goal:** ...
• Remove old "IMPORTANT Task Planning Notes" if superseded by Closing Reminders

Step 6: Verify

| Check | Pass Condition | | ------------------- | ---------------------------------------------------- | | No YAML corruption | Frontmatter intact | | No content loss | All rules, code, paths present | | Rule density | Post ≥ pre (count MUST ATTENTION/NEVER/ALWAYS) | | Goal | Present in Quick Summary and Closing Reminders | | Summary | Present in Quick Summary (key things + steps digest) | | Line count | Reduced (compression worked) | | Formatting | Blank lines between sections, headers correct | | READ classification | .claude/ → inline summary, docs/ → skipped |

[IMPORTANT] Use task tracking to break ALL work into small tasks BEFORE starting.

Output Quality — Token efficiency without sacrificing quality.

1. No inventories/counts — AI can grep | wc -l. Counts go stale instantly
2. No directory trees — AI can glob/ls. Use 1-line path conventions
3. No TOCs — AI reads linearly. TOC wastes tokens
4. No examples that repeat what rules say — one example only if non-obvious
5. Lead with answer, not reasoning. Skip filler words and preamble
6. Sacrifice grammar for concision in reports
7. Unresolved questions at end, if any

Universal Skill-Building Principles — 10 principles for building AI skills that work across any project type. Source: extracted from review-changes, plan-review, code-review skill rewrites.

Meta-principle: Teach AI to reason, not to recite. Skill's job: structure WHEN and HOW AI applies its existing knowledge — not enumerate every possible concern.

1. Detect Before Act — Every skill starts with a classification phase. Detect artifact type (plan type, code category, change nature) before applying any logic. Detection drives: sub-agent selection, which dimensions to emphasize, mandatory vs. optional checks. Anti-pattern: same checklist applied regardless of input type.

2. Derive, Don't Enumerate — Teach AI HOW to reason about a domain, not WHAT items to tick. Replace "check X, Y, Z" with "understand role → read conventions → derive concerns from first principles → execute with evidence." Fixed checklist = ceiling. Thinking framework = floor. Test: Can this skill run on a Python/Go project without modification? If not → it's enumerating, not teaching.

3. Evidence Gates — Every claim, finding, recommendation requires file:line proof or traced call chain. Confidence thresholds: >80% act freely, 60-80% verify first, <60% DO NOT recommend. "Insufficient evidence" is valid output. Speculation is forbidden output.

4. Fresh Eyes Protocol — Round 1 in main session. Round 2+ with fresh sub-agent (zero memory of Round 1). Main agent reads report but NEVER filters or overrides findings. Max 3 rounds, then escalate to user. Never declare PASS after Round 1 alone. Why: main agent rationalizes its own mistakes. Zero-memory sub-agent catches what main agent dismissed.

5. Specialize by Type — Route to specialized sub-agents based on detected artifact type:

   | Artifact type | Sub-agent | | ---------------------------- | ----------------------- | | Source code / diffs | code-reviewer | | Security-sensitive changes | security-auditor | | Performance-critical changes | performance-optimizer | | Plans / docs / specs | general-purpose |

6. Embed Protocols Verbatim, Never Reference — Shared protocols MUST be copied inline into every sub-agent prompt — never referenced by file path or tag name. AI compliance drops significantly behind file-read indirection. Maintain canonical source; embed body at every call site.

7. Search-Based Discovery — Never hardcode project-specific paths, formats, or identifiers. Teach skill to discover them:

   • "Search for coding-standards, style-guide, contributing" not "read docs/X/code-review-rules.md"
   • "Find the project's test format near changed files" not "look for TC-{FEATURE}-{NNN} in docs/specs/" This is what makes a skill work across any project without modification.

8. Dimensions > Checklists — Structure review/analysis as named thinking dimensions, each with a Think: prompt that forces first-principles reasoning: (1) state dimension's role, (2) derive what could go wrong if weak, (3) apply to artifact with evidence. Produces targeted, evidence-backed findings — not generic "add more detail" suggestions. Serial attention: When applying a dimension-based framework, NEVER scan all dimensions simultaneously. One focused pass per dimension. AI misses violations when attention is split across concurrent concerns. Pattern: identify applicable dimensions → sequential focused passes → aggregate. Threshold invariant: 3+ similar patterns in any dimension pass = MANDATORY extraction. 2+ violations of same kind = structural/architectural finding, not individual instance.

9. Recursive Quality Loop — Fix → Re-review → Fix → Re-review. Each round uses a NEW fresh sub-agent. Continue until PASS or 3 rounds max, then escalate. Never declare success after Round 1 alone. Never reuse a sub-agent across rounds.

10. Anti-Rationalization Anchors — Explicitly name and embed the evasion patterns AI uses to skip steps in the skill's closing reminders:

    | Evasion | Rebuttal | | --------------------- | ---------------------------------------------------------- | | "Too simple for this" | Wrong assumptions waste more time. Apply anyway. | | "Already searched" | Show file:line evidence. No proof = no search. | | "Just do it" | Still need task tracking. Skip depth, never skip tracking. |

Context Engineering Principles — Research-backed principles for prompt quality. Source: Anthropic prompt engineering guide, Stanford "lost-in-the-middle" research, 2025-2026 LLM context optimization studies.

1. Primacy-Recency Effect — LLM performance drops 15-47% for middle-context information (Stanford). AI attention peaks at first/last 10% of text. Action: Place the 3 most critical rules in both the first 5 lines AND the last 5 lines of every prompt. Queries at end improve quality by up to 30% (Anthropic).
2. High-Signal Density — Anthropic: "Identify the smallest collection of high-signal tokens that maximize the probability of the desired outcome." Action: Every line should change AI behavior. If removing a line doesn't change output → cut it. Target ≥8 rules (MUST ATTENTION/NEVER/ALWAYS) per 100 lines.
3. Context Rot — LLM performance degrades as context length grows — even when all content is relevant. Compression (5-20x) maintains or improves accuracy while saving 70-94% tokens. Action: Compress aggressively. Shorter, denser prompts outperform longer, diluted ones.
4. Structured > Prose — Tables, bullets, XML/markdown parse faster than paragraphs. Constrained formats reduce error rates vs free-text. Action: Convert narrative to tables/bullets. Use markdown headers for semantic sections.
5. RCCF Framework — Modern LLMs (2025+) already know how to reason. What they need: Role (personality), Context (grounding), Constraints (guardrails), Format (structure). Constraints and format matter more than verbose instructions.
6. Checkbox Avoidance — [ ] syntax triggers mechanical compliance — AI ticks boxes without reasoning. Bullet rules force reading and evaluation. Action: Replace - [ ] Check X with - MUST ATTENTION verify X.
7. Example Economy — 3-5 examples optimal for few-shot; diminishing returns after. Action: 1 best example per pattern. Use BAD→GOOD pairs (2-3 lines each) for anti-patterns.
8. Deferred Tool Loading — Claude Code delays loading tool definitions when they exceed 10% of context window. Action: Keep injected docs well under 10% of context budget. Docs exceeding ~3,000 lines are too large for injection — split or compress.
9. Rule Density Verification — Post-optimization rule count (MUST ATTENTION/NEVER/ALWAYS) must be ≥ pre-optimization count. Compression should preserve or increase density, never decrease it. Action: Count before and after every optimization pass.
10. Affirmative Directives — Models comply with affirmative directives more reliably than prohibitions; a bare "don't X" leaves the correct action unspecified, so the model substitutes an arbitrary alternative. Action: State the action to take, not only the action to avoid. Keep NEVER/forbidden guardrails for hard invariants — but pair each with the right path ("Do X" not just "Don't do Y").
11. Rationale-Carrying Instructions — A rule shipped with its reason generalizes to edge cases the rule never enumerated and survives compression; a bare imperative gets misapplied or silently dropped. Action: Append a terse — why: … clause to every non-obvious rule. The reason names the failure prevented or outcome wanted — never restates the rule.

Prompt Enhancement Transforms (Base) — Transforms 1-3 are identical across all $prompt-enhance ops (--op=compress|expand|enhance). Transform 4 is per-op (conciseness pass for compress/enhance; structural clarity pass for expand) and stays local to each op branch.

Transform 1: Inline Summaries for READ References

Problem: AI sees MUST ATTENTION READ file.md and skips it. Solution: Add a 2-3 line summary of key rules BEFORE the read instruction.

Before:

MUST ATTENTION READ .claude/protocols/evidence.md

After:

> **Evidence-Based Reasoning** — Speculation is FORBIDDEN. Every claim requires `file:line` proof.
> Confidence: >95% recommend freely, 80-94% with caveats, <80% DO NOT recommend.

MUST ATTENTION READ .claude/protocols/evidence.md for full details.

Scope rules:

• .claude/ protocol files → always add an inline summary (stable, belongs to framework)
• docs/project-reference/ files → NO inline summary (project-specific). Add: (Claude may inject this via hooks; Codex must open this file directly using docs-index routing)

Transform 2: Top Summary Section

Required structure (first 20 lines after frontmatter):

> **[IMPORTANT]** task tracking instruction...

> **Protocol Name** — [inline summary]. MUST ATTENTION READ `path` for details.

## Quick Summary

**Goal:** [One sentence — what this skill achieves AND the ultimate outcome it must cause]

**Summary:** [2-4 bullets/sentences — the key important things + the steps AI must notice; the read-this-if-nothing-else digest, distinct altitude from the enumerated Workflow/Key Rules below]

**Workflow:**

1. **[Step]** — [description]

**Key Rules:**

- [Most critical constraint]

Transform 3: Bottom Closing Reminders

Add at the very end of the file:

---

## Closing Reminders

**IMPORTANT MUST ATTENTION Goal:** [same goal as Quick Summary]
**IMPORTANT MUST ATTENTION** [echo rule #1 from the top section]
**IMPORTANT MUST ATTENTION** [echo rule #2]
**IMPORTANT MUST ATTENTION** [echo rule #3]
**IMPORTANT MUST ATTENTION** add a final review task to verify work quality

Pick 3-5 rules AI most commonly violates. Bottom section re-anchors attention after the long middle.

Shared Protocol Duplication Policy — Inline protocol content in skills (wrapped in <!-- SYNC:tag -->) is INTENTIONAL duplication. Do NOT extract, deduplicate, or replace with file references. AI compliance drops significantly when protocols are behind file-read indirection. To update: edit .claude/skills/shared/sync-inline-versions.md first, then grep SYNC:protocol-name and update all occurrences.

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. Before changing a constant, limit, flag, wording, or pattern, read nearby context and history. Surface ambiguity before acting — don't pick silently. Multiple valid interpretations require an explicit question or stated assumption with risk. 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.

Closing Reminders

IMPORTANT MUST ATTENTION Goal: Two-phase optimization (caveman compression + attention anchoring) that produces a prompt/skill stating its objective and ultimate outcome (one consolidated Goal) anchored top and bottom, so AI optimizes for the right result.

IMPORTANT MUST ATTENTION Protocols in force (concise digest of the SYNC/shared blocks this skill carries — each is a signpost to its canonical body above):

• Output Quality: MUST ATTENTION no inventories/trees/TOCs; lead with answer; sacrifice grammar for concision.
• Universal Skill-Building: MUST ATTENTION detect-before-act, derive-don't-enumerate, evidence gates, fresh-eyes, embed protocols verbatim.
• Context Engineering: MUST ATTENTION primacy-recency, high-signal density, compress aggressively, affirmative directives.
• Prompt Enhancement Transforms: MUST ATTENTION inline READ summaries, top Quick-Summary, bottom Closing-Reminders (Transforms 1-3 base).
• Shared Protocol Duplication Policy: NEVER extract SYNC duplication to references — edit canonical first; inline is intentional.
• AI Mistake Prevention: verify generated content against evidence, trace downstream references, verify all affected outputs, re-read after context loss, surface ambiguity.
• Critical Thinking: MUST ATTENTION traced file:line proof per claim; confidence >80% to act; NEVER guess.

IMPORTANT MUST ATTENTION select --op FIRST (default enhance) — compress/enhance apply caveman compression FIRST (Phase 1) before structural enhancement (never skip); expand applies Language Expansion (inverse) instead — why: expand reconstructs, it does not strip IMPORTANT MUST ATTENTION NEVER compress code blocks, YAML frontmatter, structured tables, or SYNC tags IMPORTANT MUST ATTENTION read target file completely before any changes IMPORTANT MUST ATTENTION derive the target's one-sentence Goal (what it achieves + ultimate outcome), then place it in both ## Quick Summary and ## Closing Reminders — why: AI must know the ultimate outcome after enhancement IMPORTANT MUST ATTENTION enhance derives BOTH the target's Goal AND its Summary (key important things + steps AI must notice) and places both in ## Quick Summary, the Summary at a different altitude than Workflow/Key Rules — why: the Goal tells AI the outcome to optimize for; the Summary tells AI the key things/steps to notice up front IMPORTANT MUST ATTENTION skill AND sub-agent (.claude/agents/*.md) targets share ONE required structure — Goal + Summary in ## Quick Summary, Goal echoed in ## Closing Reminders — so creator skills (e.g. custom-agent) emit a consistent shape; when enhancing an agent NEVER alter <!-- SYNC:... --> blocks or delete ## Role/## Workflow/## Key Rules/## Output — why: SYNC copies are canonical-synced and divergence fails the build IMPORTANT MUST ATTENTION read each referenced protocol file to write accurate inline summaries — NEVER guess content IMPORTANT MUST ATTENTION apply primacy-recency anchoring — 3 critical rules in first 5 AND last 5 lines of every enhanced file IMPORTANT MUST ATTENTION verify rule density: count MUST ATTENTION/NEVER/ALWAYS before and after — post ≥ pre IMPORTANT MUST ATTENTION state the action to take, not only what to avoid — pair every NEVER with the right path, and append a terse — why: to each non-obvious rule — why: affirmative directives + carried rationale are followed more reliably and survive compression (principles #10/#11) IMPORTANT MUST ATTENTION add inline summaries only for .claude/ protocol files, not project-specific docs/ files IMPORTANT MUST ATTENTION keep all meaningful content — only restructure/compress, NEVER delete rules or code examples IMPORTANT MUST ATTENTION verify no YAML frontmatter corruption after changes IMPORTANT MUST ATTENTION cite file:line evidence for every claim (confidence >80% to act). NEVER speculate without proof. IMPORTANT MUST ATTENTION READ CLAUDE.md before starting

Anti-Rationalization:

| Evasion | Rebuttal | | --------------------------------------- | ------------------------------------------------------------------------- | | "File is short, skip compression" | Apply both phases anyway — density matters at any length | | "Already read the file" | Show recorded line count + rule density as proof | | "Closing reminders already exist" | Verify they echo top-section rules AND include anti-rationalization table | | "Skill file, skip Universal Principles" | NEVER skip — Phase 0 detection is BLOCKING |

[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. 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.
• 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. Pattern-matching as "wrong" skips context. Before changing any constant/limit/flag: read comments, git blame, surrounding code.
• 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.
• 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.