prompt-enhance

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

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: 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 a direct user question. Text passed (not file path)? Apply caveman compression directly and output result.

---

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

---

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

---

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)

---

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

---

[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-{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]** task tracking 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 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. Cheapest check first.
• [2026-04-01] Ask "whose responsibility?" before fixing. Trace: bug in 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 is 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

• [2026-03-31] ParallelAsync + repo/UoW MUST 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 include service name prefix — core services NEVER consume feature events. Prefix declares schema ownership (AccountUserEntityEventBusMessage = Accounts owns). Core services (Accounts, Communication) are leaders. Feature services (Growth, Talents) sending to core MUST 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 it 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 in 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 in bash PATH under those names. Check: where python / where py. Prefer py (Windows Python Launcher) for 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 in .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

[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.