learn

name	learn
description	[Utilities] Use when you need to teach Claude lessons that persist across sessions.
disable-model-invocation	false

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: Teach Claude lessons that persist across sessions by saving to the most relevant reference doc.

Workflow:

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

Key Rules:

• GENERALIZE FIRST (the #1 protocol): Extract the GENERIC lesson that applies to many cases — NEVER save the specific case as-is. The user's words describe one incident; your job is to climb from that incident to the reusable rule. Strip every project/file/tool/domain name. If the saved text only helps on this exact ticket, you failed — abstract it up a level. (Enforced by the Lesson Quality Gate below.)
• Triggers on "remember this", "always do X", "never do Y"
• Triage first: pass Recurrence gate + Auto-fix gate BEFORE routing or saving
• Smart-route to the most relevant file, NOT always docs/project-reference/lessons.md
• Check for existing entries before creating duplicates
• Confirm target file with user before writing

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

Usage

Add a lesson

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

List lessons

$learn list

Remove a lesson

$learn remove 3

Clear all lessons

$learn clear

Reference Doc Catalog (READ before routing)

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

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

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

---

Smart File Routing (CRITICAL)

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

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

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

---

Routing Table

Route to the most relevant file based on lesson content:

If lesson is about...	Route to	Section hint
Code review rules, anti-patterns, review checklists, YAGNI/KISS/DRY, naming conventions, review process	docs/project-reference/code-review-rules.md	Add to most relevant section (anti-patterns, rules, checklists)
Backend/hook patterns: modules, CQRS, repositories, entities, validation, message bus, background jobs, migrations, configured persistence	docs/project-reference/backend-patterns-reference.md	Add to relevant section or Anti-Patterns section
Frontend patterns: components, state stores, forms, API services, styling conventions, directives, pipes	docs/project-reference/frontend-patterns-reference.md	Add to relevant section or Anti-Patterns section
Integration/unit tests: test base classes, fixtures, test helpers, test patterns, assertions, test runners	docs/project-reference/integration-test-reference.md	Add to relevant section
E2E tests: Playwright, Cypress, Selenium, page objects, E2E config, browser automation, visual regression	docs/project-reference/e2e-test-reference.md	Add to relevant section
Domain entities, data models, DTOs, aggregates, entity relationships, cross-service data sync, ER diagrams	docs/project-reference/domain-entities-reference.md	Add to Entity Catalog or Relationships section
Project structure, directory organization, module boundaries, tech stack choices, service architecture	docs/project-reference/project-structure-reference.md	Add to relevant architecture section
SCSS/CSS styling, BEM methodology, mixins, variables, theming, responsive design, CSS conventions	docs/project-reference/scss-styling-guide.md	Add to relevant styling section
Design system, design tokens, component library, UI kit conventions, Figma-to-code patterns	docs/project-reference/design-system/README.md	Add to relevant design section
Feature documentation, doc templates, doc structure conventions, app-to-service doc mapping	docs/project-reference/feature-spec-reference.md	Add to relevant conventions section
Documentation indexing, doc organization, doc-to-code relationships, doc lookup patterns

---

Prevention Depth Assessment (MANDATORY before saving)

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

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

Decision flow:

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

Static Protocol Lesson Promotion (MANDATORY evaluation)

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

Qualification criteria (ALL must be true):

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

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

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

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

Lesson Quality Gate (BLOCKING — generalize before you save)

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

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

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

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

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

Step 2 — Verify generality:

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

Step 3 — Write as a universal rule:

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

Anti-pattern examples:

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

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

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

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

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

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

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

Routing Decision Process

1. Run Triage Gate — recurrence + auto-fix filters; stop here if either fails
2. Read the lesson text — identify keywords and domain
3. Apply Lesson Quality Gate — analyze root cause, generalize, verify universality
4. Run Prevention Depth Assessment — determine if doc-only or deeper prevention needed
5. Match against Routing Table — pick the best-fit file
6. Tell the user: "This lesson fits best in docs/{file}. Confirm? [Y/n]"
7. On confirm — read target file, find the right section, append the lesson
8. On reject — ask user which file to use instead

Format by Target File

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

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

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

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

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

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

Hard limit: 10000 characters (~3333 tokens). Check BEFORE saving any new lesson.

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

1. Read file, count characters (wc -c docs/project-reference/lessons.md)
2. If current + new lesson > 10000 chars → trigger Budget Trim before saving
3. If under budget → save normally

Budget Trim process:

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

Condensing rules:

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

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

Behavior

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

Auto-Inferred Activation

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

How Lessons Reach the AI

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

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

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

Prompt Enhancement (MANDATORY final step)

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

When to run:

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

What it does:

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

How to invoke:

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

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

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

---

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

Mandatory end tasks are ALWAYS (in order):

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

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

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

Re-read files after context changes. Context compaction, resume, or long-running work can make memory stale; verify current files before acting. Verify generated content against source evidence. AI hallucinates APIs, names, claims, and document facts. Check the relevant source before documenting or referencing. Check downstream references before deleting or renaming. Removing an artifact can stale docs, generated mirrors, configs, and callers; map references first. Trace the full impact chain after edits. Changing a definition can miss derived outputs and consumers. Follow the affected chain before declaring done. Verify ALL affected outputs, not just the first. One green check is not all green checks; validate every output surface the change can affect. Assume existing values are intentional — ask WHY before changing. 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 GENERALIZE FIRST — extract the generic, many-cases rule; NEVER persist the specific incident as written. Strip all ticket/file/service/tool names before saving.

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

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

IMPORTANT MUST ATTENTION run Triage Gate FIRST — if recurrence is low OR review skills can catch it, skip $learn entirely IMPORTANT MUST ATTENTION check Reference Doc Catalog to find the best target file — NOT always lessons.md IMPORTANT MUST ATTENTION mandatory end tasks are ALWAYS: Learn Review → $why-review → $prompt-enhance <modified-file> (in order) IMPORTANT MUST ATTENTION break work into small todo tasks using task tracking BEFORE starting IMPORTANT MUST ATTENTION prefer auto-injected files for high-recurrence lessons (higher visibility)

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

Hookless Prompt Protocol Mirror (Auto-Synced)

Source: .claude/.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.