prompt-enhance

name	prompt-enhance
version	3.2.0
description	[Skill Management] Use when enhancing prompts, docs, or skills with compression and attention anchoring.

Quick Summary

Goal: Two-phase optimization: (1) Caveman Compression — strip stop words + grammatical scaffolding, preserve semantic meaning; (2) Prompt Enhancement — apply AI attention anchoring so AI reads and follows all instructions.

Workflow:

1. Detect — Classify target: skill file, protocol file, or general doc
2. Read — Read target file completely
3. Compress — Apply caveman compression (Phase 1)
4. Enhance — Apply AI attention anchoring transforms (Phase 2)
5. Verify — No content loss, rule density ≥ pre-optimization

Key Rules:

• 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
• 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: $ARGUMENTS

No file? Ask via AskUserQuestion. Text passed (not file path)? Apply caveman compression directly and output result.

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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
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

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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

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Phase 1: Caveman Compression

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)

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)

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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. Identify: missing Quick 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
• 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)
• 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)
Line count	Reduced (compression worked)
Formatting	Blank lines between sections, headers correct
READ classification	.claude/ → inline summary, docs/ → skipped

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[IMPORTANT] Use TaskCreate 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-{FEAT}-{NNN} in docs/business-features/" 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.

Prompt Enhancement Transforms (Base) — Transforms 1-3 are identical across prompt-enhance / prompt-expand. Transform 4 is per-skill (conciseness pass for enhance; structural clarity pass for expand) and stays local to each skill.

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: (read directly when relevant; do not rely on hook-injected conversation text)

Transform 2: Top Summary Section

Required structure (first 20 lines after frontmatter):

> **[IMPORTANT]** TaskCreate instruction...

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

## Quick Summary

**Goal:** [One sentence — what this skill achieves]

**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** [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: 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 apply caveman compression FIRST (Phase 1) before any structural enhancement — never skip 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 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 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 TaskCreate.