estimate-actual

name	estimate-actual
description	[Planning] Use when calibrating estimates from actual code, diff, PR scope, and developer time.
disable-model-invocation	true

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: Produce a 3-way estimation calibration report — pre_impl_estimate (from plan) vs true_estimate (from observed scope) vs actual_time (from git/user) — yielding two INDEPENDENT signals: developer execution variance and estimation model calibration variance.

Why two signals matter: They are confounded if not separated. If actual >> pre-impl, the bug could be (a) developer was slow, OR (b) the model under-estimated scope. Without computing TRUE from observed scope, you cannot tell which. Single-sample calibration has near-zero statistical power — the skill always reports this.

Workflow:

1. Detect input mode — plan-file path / --changes / --pr <n>
2. Read pre-impl estimate from plan frontmatter (if present)
3. Observe actual scope — git diff, file list, line counts, blast radius from the diff
4. Compute TRUE estimate — apply the canonical estimation framework (inlined below) to the OBSERVED scope (post-hoc, with full visibility)
5. Get actual time — derive from git timestamps if available; ask user to confirm/override (timestamps ≠ coding time)
6. Compute variances — scope variance (TRUE vs pre-impl) and execution variance (actual vs TRUE)
7. Per-layer breakdown — UI tier, backend tier, test count, blast radius — predicted vs observed
8. Report — calibration verdict + suggested model adjustments (only if pattern across ≥3 samples)

Key Rules:

• MUST ATTENTION compute TRUE estimate using the SAME canonical framework that was used for pre-impl — fair comparison requires identical methodology
• MUST ATTENTION separate developer execution signal from model calibration signal — never collapse to a single "good/bad estimate" verdict
• NEVER claim a model adjustment from a single sample — explicitly state "needs ≥3 samples for signal"
• NEVER trust git timestamps as actual coding time — they include sleep, meetings, context switches; ALWAYS ask user to validate
• MUST ATTENTION list per-layer deltas (UI/backend/tests/blast) — aggregate variance hides where the model went wrong
• Use min-max range for both pre-impl and TRUE — comparing single points is dishonest about uncertainty

Input Modes

Mode	Trigger	What's read
Plan-file	<path/to/plan.md>	Plan frontmatter (pre-impl estimate) + git diff scoped to plan branch
Changes	--changes	git diff working tree + last commit timestamps
PR	--pr <n>	gh pr view <n> + gh pr diff <n> + PR open/merge times

If multiple modes detected (e.g., plan file AND --changes), prefer plan-file (carries the original estimate); use changes for the diff source.

Workflow Detail

Step 1: Detect mode and gather pre-impl estimate

• Plan-file mode: read frontmatter, extract man_days_traditional, story_points, risk_margin_pct, risk_factors, blast_radius, estimate_reasoning if present
• Changes/PR mode without plan: skip pre-impl; report only TRUE vs actual
• If plan exists but uses old single-point format (no range, no risk_factors), flag in report — comparison is approximate

Step 2: Observe actual scope from diff

Run (PowerShell or Bash via tool):

• git diff --stat <base>..<head> — file count, lines added/removed
• git diff --name-only <base>..<head> — file list
• git log --format='%H %ai %s' <base>..<head> — commit timeline

Classify changed files:

• UI files (component/template/style) — count, group by screen
• Backend handlers/entities/repos — count, classify per backend tier table
• Tests (unit/integration/e2e) — count test files, count test cases (grep describe|it|Fact|Test\b)
• Migrations / contracts / shared code — flag separately

Step 3: Run Blast Radius pass on observed scope

• Touched files / components — count
• Of those, complex (>500 LOC area, multi-handler, central) — count
• Downstream consumers — use code graph trace if available: python .claude/scripts/code_graph trace <file> --direction both --json for changed entry-point files
• Shared/common code touched — yes/no
• Regression scope — list affected areas

Step 4: Apply canonical estimation framework to observed scope

Apply each tier table (UI / backend / test / risk margin / risk factors) from the inline framework below to the OBSERVED scope. Output:

• true_likely_days (single midpoint)
• true_min_days = likely × 0.9
• true_max_days = likely × (1 + risk_margin)
• true_estimate = '<min>-<max>d' range

Step 5: Get actual time

Try in order:

1. Git: timestamp of first commit on feature branch → timestamp of last commit (or merge commit). Convert to working days (8h business days, exclude weekends).
2. PR: open time → merge time. Same conversion.
3. Ask user via a direct user question: "Git suggests N working days from first commit to merge. How much was actual coding time? (excludes meetings, code-review wait, context switches, vacations)"

ALWAYS surface the gap between elapsed time and reported coding time — they are different signals.

Step 6: Compute variances

scope_variance_pct  = (true_likely - preimpl_likely) / preimpl_likely × 100
exec_variance_pct   = (actual_time - true_likely) / true_likely × 100

Interpretation matrix:

scope_var	exec_var	Verdict
~0% (±15%)	~0% (±15%)	Estimate matched scope; developer matched estimate. Healthy.
~0%	>+25%	Model OK; developer slower than expected. Performance signal.
~0%	<-25%	Model OK; developer faster than expected. Either skilled or scope simpler than apparent.
>+25%	~0%	Model UNDER-estimated scope; developer matched the harder-than-predicted reality. Model signal — too optimistic.
<-25%	~0%	Model OVER-estimated scope; actual work was simpler. Model signal — too pessimistic.
>+25%	>+25%	Both — scope was harder AND developer slower. Disambiguate over multiple samples.
<-25%	<-25%	Original estimate was way over; developer also fast. Likely simple task padded heavily.

Step 7: Per-layer breakdown (where the model went wrong)

Layer	Pre-impl tier (from plan)	Observed tier (from diff)	Delta
UI	e.g. "Compose components into NEW screen"	e.g. "Add control to existing screen"	-1 tier (~0.7d over)
Backend	e.g. "NEW command on existing aggregate"	e.g. "Small update existing handler"	-1 tier (~0.5d over)
Tests	e.g. "13 cases"	e.g. "5 cases"	-8 cases (~0.5d over)
Blast	e.g. "4 areas, 1 complex"	e.g. "2 areas, 0 complex"	lower regression risk
Risk factors	predicted list	applicable in retrospect	call out missing/unused

Step 8: Report

Produce a markdown report with sections:

1. Summary table — three numbers (pre-impl range, TRUE range, actual single)
2. Variance verdict — interpretation matrix row + plain-English explanation
3. Per-layer breakdown — table above
4. Risk factors — predicted vs applicable; note any new factors that surfaced (e.g., regression-fan-out not flagged but should have been)
5. Calibration suggestion — ONLY if user has run this skill ≥3 times with consistent direction. Single-sample → state "no statistical power, log this sample for future calibration"
6. Confidence — state confidence level for each verdict; uncertainty about actual time goes here

Step 9: Persist sample (optional)

If user wants longitudinal tracking, append the calibration row to plans/_estimation-samples.csv:

date,plan,preimpl_min,preimpl_max,true_min,true_max,actual,scope_var_pct,exec_var_pct,risk_factors_predicted,risk_factors_applicable

After ≥5 rows, run pattern detection on the CSV: if scope_var_pct is consistently negative (model over-estimates), suggest tier adjustment; if consistently positive (under-estimates), suggest adding risk factors or widening tier.

Estimation Framework (canonical — applied in Step 4)

Output Report Template

# Estimation Calibration Report — <plan or branch name>

## Summary

| Metric            | Range / Value              | Source                                   |
| ----------------- | -------------------------- | ---------------------------------------- |
| Pre-impl estimate | <min>-<max>d (likely <m>d) | <plan path frontmatter>                  |
| TRUE estimate     | <min>-<max>d (likely <m>d) | observed scope (post-hoc)                |
| Actual time       | <n>d                       | git <first commit→merge>, user-confirmed |

**Scope variance** (TRUE vs pre-impl): <±n>% — <under/over/matched>
**Execution variance** (actual vs TRUE likely): <±n>% — <fast/slow/matched>

## Verdict

| Signal              | Direction                                       | Magnitude | Confidence        |
| ------------------- | ----------------------------------------------- | --------- | ----------------- |
| Estimation model    | <too optimistic / too pessimistic / calibrated> | <±n>%     | <low/medium/high> |
| Developer execution | <fast / slow / on-pace>                         | <±n>%     | <low/medium/high> |

## Per-Layer Breakdown

| Layer        | Predicted tier     | Observed tier      | Delta           |
| ------------ | ------------------ | ------------------ | --------------- |
| UI           | …                  | …                  | …               |
| Backend      | …                  | …                  | …               |
| Tests        | … cases            | … cases            | …               |
| Blast radius | … areas, … complex | … areas, … complex | …               |
| Risk factors | <predicted list>   | <applicable list>  | <added/removed> |

## Calibration Suggestions

- <If single sample> No model adjustment from one data point. Logged to `plans/_estimation-samples.csv` (row N). Re-run $estimate-actual on future plans to build calibration corpus. Suggested adjustment after ≥3-5 samples with consistent direction.
- <If pattern across samples> e.g. "UI tier 'Compose components into NEW screen' overshoots in 4/5 samples by ~0.5d → suggest splitting into two tiers OR widening band to 1-2.5d"

## Caveats

- Actual time derived from <git/user>; <list any uncertainty: weekends, code-review days, vacations excluded?>
- Pre-impl estimate format <range/single-point/missing> — comparison <exact/approximate>
- Confidence in TRUE estimate: <high/medium/low> — observed scope <fully visible / partially obscured>

Anti-Rationalization Anchors

Evasion	Rebuttal
"Single sample is enough — clearly the dev was slow"	NO. Without separating scope from execution variance, you confound model error and performance. State signal + caveat.
"Use git timestamps as actual time"	Wrong. Includes weekends, meetings, code-review wait, sleep. Always confirm with user.
"Skip TRUE estimate — just compare pre-impl vs actual"	That's the data point that's MISSING and exactly why estimates don't improve over time. Never skip Step 4.
"Apply hindsight to pump up TRUE estimate"	Use the SAME framework that was used for pre-impl. Hindsight bias inflates TRUE and falsely vindicates the original estimate.
"One signal is fine, no need to split"	Two signals is the entire point. Performance review needs execution variance; model tuning needs scope variance. Confounded data is unactionable.

---

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.

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.

Closing Reminders

IMPORTANT MUST ATTENTION compute TRUE estimate using the SAME canonical framework — fair comparison requires identical methodology IMPORTANT MUST ATTENTION separate developer execution signal from model calibration signal — never collapse to single verdict IMPORTANT MUST ATTENTION never claim model adjustment from a single sample — explicitly state "needs ≥3 samples for signal" IMPORTANT MUST ATTENTION never trust git timestamps as coding time — always ask user to confirm/override IMPORTANT MUST ATTENTION list per-layer deltas (UI/backend/tests/blast) — aggregate variance hides where model went wrong IMPORTANT MUST ATTENTION use min-max ranges for both pre-impl and TRUE — comparing single points is dishonest about uncertainty IMPORTANT MUST ATTENTION apply Blast Radius pass on observed diff before applying tier tables IMPORTANT MUST ATTENTION persist samples to plans/_estimation-samples.csv for longitudinal calibration IMPORTANT MUST ATTENTION state confidence per verdict — uncertainty about actual time goes in caveats

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

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.

[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.