[Planning] Use when calibrating estimates against actual code, diff, PR scope, and developer time.
Scanned 9/9/2026
Install to Claude Code
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---
name: estimate-actual
description: '[Planning] Use when calibrating estimates against actual code, diff, PR scope, and developer time.'
disable-model-invocation: false
argument-hint: '<plan-file> | --changes | --pr <number>'
---
## 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 `AskUserQuestion`: "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)
The canonical framework lives in the **Estimation Framework** sync block at the end of this skill; Step 4 applies it verbatim to the observed (post-hoc) scope.
## Output Report Template
```markdown
# 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. |
---
<!-- SYNC:estimation-framework -->
> **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:**
>
> ```yaml
> 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
<!-- /SYNC:estimation-framework -->
<!-- SYNC:ai-mistake-prevention -->
> **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.
<!-- /SYNC:ai-mistake-prevention -->
<!-- SYNC:project-protocol-overlay -->
> **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.
<!-- /SYNC:project-protocol-overlay -->
<!-- SYNC:project-protocol-overlay:reminder -->
**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.
<!-- /SYNC:project-protocol-overlay:reminder -->
## Closing Reminders
**Protocols in force (concise digest of the SYNC/shared blocks this skill carries):**
- **Estimation Framework:** Bottom-up first, blast-radius pass, min-max range, tier + risk-margin tables.
- **AI Mistake Prevention:** verify generated content against evidence, trace downstream references, verify all affected outputs, re-read after context loss, surface ambiguity.
- **Critical Thinking:** Traced `file:line` proof per claim, confidence >80% to act, no guess-as-fact.
**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 `TaskCreate` to break ALL work into small tasks BEFORE starting.
<!-- SYNC:critical-thinking-mindset -->
> **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.
<!-- /SYNC:critical-thinking-mindset -->
<!-- SYNC:critical-thinking-mindset:reminder -->
**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.
<!-- /SYNC:critical-thinking-mindset:reminder -->
<!-- SYNC:ai-mistake-prevention:reminder -->
**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.
<!-- /SYNC:ai-mistake-prevention:reminder -->
**[TASK-PLANNING]** Before acting, analyze task scope and systematically break it into small todo tasks and sub-tasks using TaskCreate.
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