Git Developer Performance

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:

• Project structure/architecture/tech-stack/deployment/setup (any layer — backend, frontend, or infra): project-structure-reference.md
• Backend/CQRS/API/domain/entity changes: backend-patterns-reference.md, domain-entities-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: Plan and generate a developer KPI-style quality-work report from local git history ONLY, so every story-point, man-day, and value claim in it rests on inspected diffs rather than commit counts.

Summary: (read-this-if-nothing-else digest — purpose + ALL main steps)

• PURPOSE — turn raw git history into an evidence-backed contribution report. The script collects evidence; AI reads the changes and synthesizes the value — a commit-list export is NOT this skill's output.
• STEP 1 — SET GOAL + PLAN. Declare the goal, trigger $plan, create one todo task per contributor. Large task: NEVER analyze before planning.
• STEP 2 — COLLECT PACKETS. Run the script (defaults: --branch develop→main, --days 60, --out reports/developer-performance). Traverse FULL merged history, not first-parent only.
• STEP 3 — ANALYZE PER CONTRIBUTOR. Read direct authored patches + merge/admin commits from work-packets/*.md. Attribute shared feature-branch implementation to each developer's OWN direct commits — never to the merge author.
• STEP 4 — ESTIMATE via the carried SYNC:estimation-framework (the AUTHORITY, and it OUTRANKS the script's legacy size-based rubric): bottom-up hours → likely_days → SP DERIVED, never assigned from cluster size. Discount generated/docs/lockfile churn FIRST.
• STEP 5 — SANITY-CHECK, then SYNTHESIZE. Velocity plausible vs active days; separate product / infra / docs / merge-admin signal; write quality-work-summary.md + evidence-proof.md outside .claude.
• GATE — run the skill's tests, run the command for the requested range, confirm the output path is outside .claude, before delivering anything.

Workflow:

1. Set Goal + Plan — declare the goal, trigger $plan, create tasks per contributor.
2. Collect Packets — run scripts/git-developer-performance.cjs to build the commit inventory and work packets.
3. Analyze Work — read patches per contributor; estimate value, story points, man-days, quality impact.
4. Synthesize Report — write quality-work-summary.md and evidence-proof.md outside .claude.

Key Rules:

• Local git history ONLY — NEVER query external services — why: the report must be reproducible from the repo alone.
• Consolidate people by identity map → normalized email → high-confidence alias (DOMAIN\first.lastpart matching a full name); --identity-map handles exceptions — why: raw display names split one person into several.
• Large task — plan FIRST, then one todo task per contributor.
• Script collects evidence; AI reads changes and synthesizes contributed value — why: this is not a commit-list export.
• KPI values are evidence-based estimates, NEVER a complete HR assessment.
• Report BOTH man_days_traditional (no AI) and man_days_ai (AI assistant with project context) — NEVER one ambiguous MD number.
• Traverse full merged branch history, not first-parent only; shared feature-branch implementation credits each developer's own direct commits. Merge authors get integration/admin signal unless conflict-resolution changes were explicitly inspected.
• Estimate implementation SP from direct authored diffs; zero-change merge/admin commits are integration signal ONLY.
• Discount before estimating: generated files, migration designers, docs/spec output, i18n sorting, lockfiles, repeated follow-up churn.
• The carried SYNC:estimation-framework is the AUTHORITY for every SP and man-day figure — SP is DERIVED from likely_days, never from cluster size — and it OUTRANKS the size-based rubric the script embeds in its generated prompt.
• Velocity mismatch or recheck request → synthesize each contributor's direct authored work as one "giant commit" first, then split into atomic 1/2/3/5/8/13 SP clusters.
• Persist large rechecks to a report file outside .claude BEFORE finalizing — why: context loss otherwise erases the evidence.
• Separate product/domain delivery, infrastructure/tooling, docs/generated churn, merge/admin integration — NEVER mix them silently into one velocity number.
• Velocity sanity check: both man-day ranges plausible for active days and the selected period.
• Keep output outside .claude; default root reports/developer-performance/.

Git Developer Performance

Use when the user asks for developer KPI/performance, productivity, contribution value, story-point estimates, man-day estimates, quality impact, or quality-work reporting from git commits.

Required AI Workflow

Before analysis, set or declare this goal:

Plan and generate a developer performance quality-work report from local git history, then execute the plan and produce the report.

Then trigger $plan or create equivalent plan artifacts. This skill is NOT a commit-list export — it requires reading direct commits AND merge/admin commits per contributor, then synthesizing value. Use ultrathink/deep analysis for final synthesis when contributor count or churn is high.

Command

node .claude/skills/git-developer-performance/scripts/git-developer-performance.cjs [options]

Options: --branch <ref> defaults to develop then main; --days <n> defaults to 60; --since <date> overrides days; --until <date> defaults now; --out <dir> defaults to reports/developer-performance; --identity-map <csv> accepts identity,email,displayName,id; --json prints machine-readable result.

Examples:

node .claude/skills/git-developer-performance/scripts/git-developer-performance.cjs
node .claude/skills/git-developer-performance/scripts/git-developer-performance.cjs --branch release/1.4 --days 30
node .claude/skills/git-developer-performance/scripts/git-developer-performance.cjs --since 2026-01-01 --until 2026-03-31 --out reports/dev-performance-q1

Output

Creates a timestamped run folder containing:

• summary.md - team evidence report, authored signal sort, warnings, and integration/admin activity.
• analysis-plan.md - AI execution plan with one task per contributor.
• work-packets/*.md - per-contributor commit/change packets for qualitative analysis.
• quality-work-summary.md and evidence-proof.md - AI-written value synthesis and proof appendix.
• analysis/ - target folder for AI-written per-contributor synthesis.
• contributors.csv, commits.csv, developers/*.md, data/*.json - source evidence and deterministic aggregates.

Analysis Rules

• Read references/analysis-workflow.md before final synthesis.
• Contributors are PEOPLE — consolidated by identity map / email / high-confidence alias, NEVER raw display names.
• Count distinct contributors, then create one todo task per contributor from analysis-plan.md.
• Per contributor, inspect direct authored commits AND merge/admin commits from work-packets/*.md.
• Use git show --stat --find-renames <hash> plus targeted patches for high-impact commits.
• Several developers on one feature branch → analyze each contributor's direct commits separately; NEVER give the whole feature's implementation SP to the merge author or PR owner — why: branch ownership is not authorship.
• Estimate every work cluster per the SYNC:estimation-framework block this skill carries (below) — it is the AUTHORITY for every SP and man-day figure in the report. Bottom-up hours first, then likely_days = ceil(bottom_up_hours / 6) × productivity_factor, then story_points DERIVED from likely_days via the SP→Days ladder — never assigned from cluster size — plus no-AI and AI-assisted man-days and a stated confidence.
• Precedence: where the size-based SP rubric embedded in the generated prompt (scripts/git-developer-performance.cjs) disagrees with the carried block, the carried block WINS. Treat the script's table as a legacy heuristic pending rewire, and say so in the report if the two would have produced different numbers.
• Displayed theme above 13 SP → state it is a SUM of smaller atomic clusters, never one unsplit story.
• NEVER add implementation SP for zero-file merge/admin commits — report them separately as integration/admin signal.
• Discount non-implementation churn BEFORE estimating: generated code, EF designer snapshots, docs/specs, i18n sorting, lockfiles, repeated follow-ups.
• Reconcile final SP/man-day totals against authored active days and team velocity; implausible → re-audit BEFORE delivery.
• Analyze contributed value across: features/changes, bug fixes, refactors, tests/docs, integration/admin, code quality.
• Many contributors → split contributor tasks across subagents with disjoint developer lists — why: overlapping lists double-count one person's work.
• Review identity and bulk-change warnings before comparing contributors.
• History incomplete, stale, squashed, or carrying bot/shared authors → state explicitly that report quality is bounded by local git data quality.

Verification

Before delivering a generated report:

1. Run node --test .claude/skills/git-developer-performance/tests/*.test.cjs.
2. Run the command for the requested repo/range.
3. Confirm the output path is outside .claude.

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 OR flagging one as a defect. Before changing or reporting a constant, limit, flag, cutoff, wording, or pattern, read nearby context and history, the CALLER's ordering, and 2+ sibling call sites of the same convention. A doc stating WHAT without WHY is missing rationale, not proof of a missing guard. Surface ambiguity before acting — don't pick silently. Multiple valid interpretations require an explicit question or stated assumption with risk. Assert the outcome your system owns, not the intermediate state your infrastructure owns. When verifying async work, assert the final business state — never the delivery/retry bookkeeping held in shared infrastructure that any co-running process can write. Such a check passes when run alone and flakes the moment anything else shares that infrastructure. Keep shared guidance role-relevant. Universal guidance must help every receiving skill or agent; code-specific obligations belong only in code-specific protocols.

Estimation Framework — Bottom-up first; SP DERIVED; output min-max range when likely ≥3d. Stack-agnostic. Baseline: 3-5yr dev, 6 productive hrs/day. AI estimate assumes Claude Code + project context.

Method:

1. Blast Radius pass (below) — drives code AND test cost
2. Decompose phases → hours/phase → bottom_up_hours = Σ phase_hours
3. likely_days = ceil(bottom_up_hours / 6) × productivity_factor
4. Sum Risk Margin (base + add-ons) → max_days = likely_days × (1 + margin)
5. min_days = likely_days × 0.9
6. Output as range when likely_days ≥3; single point allowed <3 (still record margin)
7. man_days_ai = same range × AI speedup
8. story_points DERIVED from likely_days via SP-Days — NEVER driver. Disagreement >50% → trust bottom-up

Productivity factor: 0.8 strong scaffolding+codegen+AI hooks · 1.0 mature default · 1.2 weak patterns · 1.5 greenfield

Cost Driver Heuristic (apply BEFORE work-type row):

• UI dominates in CRUD/business apps — 1.5-3x backend (states, validation, responsive, a11y, polish)
• Backend dominates ONLY: multi-aggregate invariants, cross-service contracts, schema migrations, heavy query/perf, new event flows

Reuse-vs-Create axis (PRIMARY lever, per layer):

UI tier	Cost
Reuse component on existing screen	0.1-0.3d
Add control/column to existing screen	0.3-0.8d
Compose components into NEW screen	1-2d
NEW screen, custom layout/states/validation	2-4d
NEW shared/common component (themed, tested)	3-6d+

Backend tier	Cost
Reuse query/handler from new place	0.1-0.3d
Small update existing handler/entity	0.3-0.8d
NEW query on existing repo/model	0.5-1d
NEW command/handler on existing aggregate (additive)	1-2d
NEW aggregate/entity (repo, validation, events)	2-4d
NEW cross-service contract OR schema migration	2-4d each
Multi-aggregate invariant / heavy domain rule	3-5d

Rule: Sum tiers across UI+backend+tests, apply productivity factor. Reuse short-circuits tiers — call out.

Test-Scope drivers (compute test_count EXPLICITLY — "+tests" hand-wave is #1 failure):

Driver	Count
Happy-path journeys	1 per story / AC main flow
State-machine transitions	reachable transitions × allowed actors
Multi-entity state combos	state(A) × state(B) — REACHABLE only, not Cartesian
Authorization matrix	(owner, non-owner, elevated, unauth) × each mutation
Validation rules	1 per required field / boundary / format / cross-field
UI states (per new screen/dialog)	happy, loading, empty, error, partial — present only
Negative paths / invariants	1 per violatable business rule

Test tier (Trad, incl. setup+assert+flake)	Cost
1-5 cases, fixtures reused	0.3-0.5d
6-12 cases, 1 new fixture	0.5-1d
13-25 cases, multi-entity setup	1-2d
26-50 cases OR new state-machine coverage	2-3d
>50 cases OR full E2E journey	3-5d

Test multipliers: new fixture/seed harness +0.5d · cross-service/bus assertion +0.3d each · UI E2E ×1.5 · each new role +1-2 cases

Blast Radius (mandatory pre-pass — affects code AND test):

1. Files/components directly modified — count
2. Of those, "complex" (>500 LOC, multi-handler, central, frequently-modified) — count
3. Downstream consumers (callers, event subscribers, cross-service) — list
4. Shared/common code touched (multi-app blast) — yes/no
5. Regression scope — areas needing re-test

Rule: Complex touch → add risk_factors. Each downstream consumer → +1-3 regression cases. Blast >5 areas OR >2 complex → re-evaluate SPLIT before estimating.

Risk Margin (drives max bound):

likely_days	Base margin
<1d trivial	+10%
1-2d small additive	+20%
3-4d real feature	+35%
5-7d large	+50%
8-10d very large	+75%
>10d	+100% AND flag SHOULD SPLIT

Risk-factor add-ons (additive — enumerate in risk_factors):

Factor	+margin
touches-complex-existing-feature (>500 LOC, multi-handler, central)	+20%
cross-service-contract change	+25%
schema-migration-on-populated-data	+25%
new-tech-or-unfamiliar-pattern	+30%
regression-fan-out (≥3 downstream areas re-test)	+20%
performance-or-latency-critical	+20%
concurrency-race-event-ordering	+25%
shared-common-code (multi-consumer/multi-app)	+25%
unclear-requirements-or-design	+30%

Collapse rule: total margin >100% → STOP, split (padding past 2x is dishonesty). Margin <15% on likely_days ≥5 → under-estimated, widen.

Work-Type Caps (hard ceilings on likely_days):

Work type	Max SP	Max likely
Single field / config flag / style fix	1	0.5d
Add property to existing model + bind to existing UI	2	1d
Additive endpoint + minor UI control (button/menu/column), reuses fixtures	3	2-3d
Additive endpoint + NEW UI surface OR additive multi-layer + new domain rule + 2+ test files	5	3-5d
NEW model/aggregate OR migration OR cross-module contract OR heavy test (>1.5d) OR NEW UI + non-trivial backend	8	5-7d
NEW UI surface + (NEW aggregate OR migration OR cross-service contract)	13	SHOULD split
Cross-service contract + migration combined	13	SHOULD split
Beyond	21	MUST split

SP→Days (validation only): 1=0.5d/0.25d · 2=1d/0.35d · 3=2d/0.65d · 5=4d/1.0d · 8=6d/1.5d · 13=10d/2.0d (Trad/AI likely) AI speedup: SP 1≈2x · 2-3≈3x · 5-8≈4x · 13+≈5x. AI cost = (code_gen × 1.3) + (test_gen × 1.3) (30% review overhead).

MANDATORY frontmatter:

story_points: <n>
complexity: low | medium | high | critical
man_days_traditional: '<min>-<max>d' # range when likely ≥3d; '<N>d' when <3d
man_days_ai: '<min>-<max>d'
risk_margin_pct: <n> # base + add-ons
risk_factors: [touches-complex-existing-feature, regression-fan-out] # closed-list from add-ons; [] if none
blast_radius:
    touched_areas: <n>
    complex_touched: <n>
    downstream_consumers: [list or count]
    shared_common_code: yes | no
estimate_scope_included: [code, integration-tests, frontend, i18n, docs]
estimate_scope_excluded: [unit-tests, e2e, perf, deployment, code-review-rounds]
estimate_reasoning: |
    5-7 lines covering:
    (a) UI tier — row applied
    (b) Backend tier — row applied
    (c) Test scope — case breakdown by driver, file count, fixtures, tier row
    (d) Cost driver — dominant tier + why
    (e) Blast radius — touched, complex, regression scope
    (f) Risk factors — list driving margin; why not larger/smaller
    Example: "UI: compose Form/Table/Dialog → NEW screen (~1.5d). Backend: NEW command on existing aggregate,
    reuses validation+repo (~1d). Tests: 4 transitions × 2 actors + 3 validation + 2 UI states = 13 cases,
    1 new fixture → tier 13-25 ~1.5d. Driver: UI composition + new states. Blast: 4 areas, 1 complex.
    Risk: base 35% + touches-complex +20% = 55% → max 3.9d → range 2.5-4d."

Sanity self-check:

• likely_days ≥3d and single-point? → reject, must be range
• Margin <15% on likely_days ≥5d? → under-estimated, widen
• Margin >100%? → STOP, split instead of buffer
• Complex existing feature touched, no regression budget in (c)? → reject
• Blast >5 areas OR >2 complex, no split discussion? → reject
• Purely additive on existing model AND existing UI? → cap SP 3 unless tests >1.5d
• NEW UI surface (page/complex form/dashboard)? → SP 5+ even if backend one endpoint
• Backend cross-service / migration / multi-aggregate? → SP 8+ regardless of UI
• bottom_up_hours / 6 vs SP-Days disagreement >50%? → trust bottom-up, downgrade SP
• Without tests, SP drops ≥1 bucket? → tests dominate; state explicitly
• Reasoning called out UI vs backend vs blast vs risk factors? → if missing, add

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.

Project Protocol Overlay — Before executing this skill, resolve any PROJECT overlay rules layered onto it: match this skill's name against the Target column of the project's skill-protocol index (docs/project-reference/skill-protocols-reference.md by default; a referenceDocs entry in docs/project-config.json overrides the path), taking the most specific matching tier ONLY — exact name > glob > *. That precedence orders overlays against EACH OTHER, never against this skill. Read ONLY the matched bodies, resolved as <protocols-dir>/<Name>.md; a row's Body link is display text, never a read path. A matched body that is missing or malformed is REPORTED and skipped — never reconstructed from the index Description. No index, or no match -> proceed with no overlay, silently. Full contract: .claude/skills/project-skill-protocol/references/registry.md.

Overlays are ADDITIVE ONLY: they ADD rules on top of this skill's own protocol and NEVER replace, override, disable, or reinterpret a rule it already states — removing every overlay must return this skill to exactly its documented behavior. An overlay is a BRIEF, not an authority escalation: it can NEVER waive a workflow gate, git discipline, a review gate, or a user-confirmation gate. A genuine overlay-vs-skill conflict, or two equally-specific overlays that directly contradict -> surface both to the user; NEVER resolve silently.

MUST ATTENTION resolve project protocol overlays for this skill BEFORE executing — most specific matching tier only (exact > glob > *, which ranks overlays against each other, NEVER against this skill), read only matched bodies at <protocols-dir>/<Name>.md; a missing or malformed body is reported, never reconstructed. Overlays are ADDITIVE ONLY (they never replace this skill's own rules) and are a brief, NEVER an authority escalation; an equal-specificity contradiction goes to the user.

Closing Reminders

IMPORTANT MUST ATTENTION Goal: Plan and generate a developer KPI-style quality-work report from local git history ONLY, so every story-point, man-day, and value claim in it rests on inspected diffs rather than commit counts.

IMPORTANT MUST ATTENTION main steps — execute in order, the skill AI keeps forgetting: (1) SET GOAL + trigger $plan + one todo task per contributor — NEVER analyze before planning; (2) COLLECT PACKETS via the script over the FULL merged history; (3) ANALYZE each contributor's direct authored patches + merge/admin commits, crediting shared branches to the direct author; (4) ESTIMATE every cluster via the carried SYNC:estimation-framework — bottom-up hours → likely_days → SP DERIVED — discounting generated/docs/lockfile churn first, and the carried block OUTRANKS the script's legacy size rubric; (5) SANITY-CHECK velocity, separate product / infra / docs / merge-admin signal, SYNTHESIZE quality-work-summary.md + evidence-proof.md outside .claude; (6) VERIFY — run tests, re-run the command, confirm the output path. — why: steps buried in the middle get skipped, and a report that skips step 3 or 4 reports churn as effort.

Protocols in force (concise digest of the SYNC/shared blocks this skill carries):

• Critical Thinking: trace every KPI/value claim; confidence >80% to act.
• AI Mistake Prevention: verify generated content against evidence, trace downstream references, verify all affected outputs, re-read after context loss, surface ambiguity.

• MANDATORY MUST ATTENTION estimation: bottom-up phase hours drive man_days_traditional (Σh/6 × productivity_factor); SP DERIVED. UI cost usually dominates — bump SP one bucket if NEW UI surface (page/complex form/dashboard). Frontmatter MUST include story_points, complexity, man_days_traditional, man_days_ai, estimate_scope_included, estimate_scope_excluded, estimate_reasoning (UI vs backend cost driver). Cap SP 3 for additive-on-existing-model+existing-UI unless test scope >1.5d. SP 13 SHOULD split, SP 21 MUST split.

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.

IMPORTANT MUST ATTENTION use local git history ONLY — NEVER query an external service. IMPORTANT MUST ATTENTION trigger planning BEFORE qualitative analysis — this is a large task. IMPORTANT MUST ATTENTION default develop, fallback main, last 60 days when the user does not specify. IMPORTANT MUST ATTENTION NEVER present authored or integration signal as a complete measure of human performance — state the estimate's limits in the report. IMPORTANT MUST ATTENTION shared feature-branch implementation credit follows DIRECT commit authors, never merge authors; NEVER let raw churn or zero-change merge/admin commits inflate implementation SP or man-days. IMPORTANT MUST ATTENTION NEVER publish a single ambiguous MD number — show no-AI and AI-assisted MD separately. IMPORTANT MUST ATTENTION derive every SP from likely_days via the carried SYNC:estimation-framework, which OUTRANKS the size-based rubric the script embeds; if the two would disagree, say so in the report — why: cluster size measures diff bulk, not effort. IMPORTANT MUST ATTENTION write output outside .claude and persist large rechecks to that file BEFORE finalizing — why: context loss erases un-persisted evidence. IMPORTANT MUST ATTENTION add a final review task to verify report quality against the evidence packets.

Anti-Rationalization:

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.

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. PARALLELIZE: Before executing the task list, tag each task PAR (independent inputs + write set disjoint from every other PAR task) or SEQ (name the blocking dependency), group PAR tasks into waves, declare the wave plan, and spawn each wave's sub-agents in ONE message — all-return barrier per wave, fan-out one level deep unless a sub-agent's own definition authorizes further fan-out. Sequential-by-default is a defect when tasks are independent; do not parallelize shared write targets, output-consuming tasks, trivial single-file work, ordering a skill or workflow explicitly fixes, or user-approval gates.
7. 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

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