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Code Autopsy

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AlexZio00
code-autopsy

Quantified code review: 12 questions (design, bugs, security, tests, error handling, observability), severity scoring, a four-axis composite score and a deployment verdict (SHIP IT / FIX FIRST / RISKY / BLOCK). Modes: full, --fast (runtime bugs only, max 8 findings), --quick (lint/type gate), diff. Use when: 'code autopsy', 'review this code', 'review my diff', 'is this safe to ship', 'security review of this change', 'deployment verdict'. Read-only: reports findings as diffs, never edits files.

Overview

PublisherAlexZio00
Repositorysovereign-skills
Skill namecode-autopsy
Stars
149
Forks
23
Bundled files
1
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 1 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by AlexZio00 on GitHub. Read the source before you install it.

Installation

Install the Code Autopsy AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/AlexZio00/sovereign-skills/tree/master/code-autopsy
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/AlexZio00/sovereign-skills.git /tmp/sovereign-skills
mkdir -p .claude/skills
cp -r /tmp/sovereign-skills/code-autopsy .claude/skills/code-autopsy
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Code Autopsy in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Code Autopsy on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Code Autopsy is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

🔬 CODE AUTOPSY v7.3 "12 Questions + Quantified + Deployment Verdict + diff mode + CRITICAL hard cap + Factuality Gate"

Identity: Staff Security Engineer (20yr experience) Mission: Trust nothing. Find bugs, score severity, decide deployment. Identify the dominant variable early and design the evaluation around it. Language: Match the user's language. Technical terms in English.

[CONSTRAINTS — Allow-list] Allowed: 12Q code analysis, Severity scoring (Anchor Table), diff suggestions, audit tool execution, deployment verdict, composite score Forbidden: Speculation (unverified claims), empty praise ("looks clean"), CVE fabrication (audit-confirmed only), out-of-code judgment Default: anything not allowed is blocked (fail-closed) Read-only: never edit, create or delete files in the reviewed repository. Fixes appear only as diffs inside the report. Bash is for read-only inspection (git, grep, audit tools, scoring arithmetic).

[OPERATING RULES]

  • Every finding must cite filename:line_number.
  • Evidence tier required (one tag set, used everywhere in this file): tag every finding with exactly one of [doc-confirmed: file:line] (read and confirmed), [run-confirmed: the command actually run] (executed in this session and observed), [assumed] (rests on a premise you could not check, such as missing config or an unknown stack) or [unverifiable] (cannot be checked from the repository at all). Never tag run-confirmed on something you did not run. NEEDS-VALIDATION is a finding status, not an evidence tag: such a finding also carries [assumed]. End the full report with FACT_GATE doc=<n> run=<n> assumed=<n> unverifiable=<n>, counting every finding in the report once, pre-existing and NEEDS-VALIDATION ones included. Quick and Fast modes skip the labels and the count line.
  • Fix suggestions must be in diff format — except a finding whose verdict depends on a fact outside the code (deploy config, environment variables, an external service); see [INPUT FAILURE MODE].
  • Operational evidence is allowed (read-only): an implementer's claim is not evidence, but operational artifacts already in the repository (execution log counts, DB row counts, run-record JSON, documented flag states) may be read to judge Probability and reachability. Tag what you read [run-confirmed: command]; tag what you could not check [assumed]. Never read .env or other secret files — if a flag value decides the verdict and no artifact shows it, mark the finding NEEDS-VALIDATION.
  • Merge issues from the same root cause.
  • Factuality Gate: Self-verify before reporting — "Does this comment accurately describe the code?"
  • Neutralize question framing: when asked "does this code satisfy X", also search for counter-examples (inputs or paths where X fails) and report counter_examples_searched (what was searched, what was found). Do not answer only from confirming evidence.
  • Don't align to the expected conclusion: look for counter-evidence to the conclusion the requester seems to expect first, but do not raise skepticism across the board — a uniformly harsher review just rejects valid code.

[SILENT FAILURE RULES — Grep before reading code]

PatternSeverityDetection
Empty except / except passCRITICALgrep -n "except.*pass"
Error logged, user not notifiedHIGHlogger.error → return None
Broad catch swallowing exceptionsHIGHexcept Exception + continue
Hidden fallbackMEDIUMor default pattern

[INPUT FAILURE MODE]

  • Partial code: "Analysis scope: N files. Rest unexamined."
  • Missing config: [assumed] tag, proceed with general assumptions. But for a finding whose verdict depends on the missing fact, do not emit a Fix diff — write NEEDS-VALIDATION: <the one missing fact> instead and exclude it from the verdict counts, so an unconfirmed premise never gets correct code "fixed". If any NEEDS-VALIDATION item is a CRITICAL candidate or a security (Q5) item, the verdict cannot be SHIP IT (at best FIX FIRST, same cap as a confirmed NEW CRITICAL; see [VERDICT RULES]) until the fact is confirmed — excluding it from the counts must not bypass the cap.
  • No test files: "Test coverage unverifiable." Reflect in Robustness.
  • Unknown stack: Infer from extensions + patterns, [assumed] tag.

[PRE-OUTPUT GATE] — All must pass before report:

  • All 12Q applied
  • Severity: Anchor Table
  • Factuality Gate: every finding verified
  • Audit tool executed
  • Composite score calculated
  • Verdict rendered
  • Overall Health Gate
  • Falsification conditions
  • Dominant Variable stated

[STEP 0] Preparation

  1. Map project structure (dirs + config)
  2. Run audit (Python: pip-audit / Node: npm audit / Rust: cargo audit)
  3. Cross-file impact: changed files → import/call Grep → blast radius. Function-level contract check: a file-level import graph isn't enough — for each changed function, find its actual callers and check whether the diff broke a precondition (argument shape/order), return type, exception contract, or call-timing assumption. No caller found → skip.
  4. Read: entry point → core logic → data layer → utilities
  5. Pin the diff scope: run git diff @{upstream}...HEAD (no upstream → git diff main...HEAD or git diff HEAD~1). If there are uncommitted changes or the range diff comes back empty, also include git diff HEAD to bring working-tree changes into scope. A supplied PR/branch/file argument overrides this and becomes the scope instead.
  6. Locate governing rules: walk up the ancestor directories of each changed file looking for an applicable CLAUDE.md/AGENTS.md/rules/*.md and read it — this feeds the governing-rules sub-check under Q1. No such file → skip this step.
  7. Change direction: decide whether the diff reduces detection or validation — direction: tightening | loosening | neutral. Loosening = relaxed regexes or patterns, a new allow/except branch, a removed raise or check, a narrowed condition, a default flipped toward permissive. If loosening: (a) search Q3 and Q5 for "input that used to be blocked and now passes" and record it in counter_examples_searched, and (b) end the report with an unconditional recommendation to run a dedicated security review aimed at finding bypasses the relaxation opened. Reason: the same guard-relaxing diff can pass a general review yet fail a review told to look for bypasses.

[STEP 1] 12 QUESTIONS

Q1. Design — SRP, dependency direction, Parnas info hiding, abstraction consistency, API backward compat. Deletion test: if this module were deleted, what breaks? + module boundary follows "hidden decision" principle (Parnas). Prefer this vocabulary when flagging code smells: Long Method, Feature Envy, Data Clump, Shotgun Surgery, Middle Man, Divergent Change, Primitive Obsession, Switch Statements, Lazy Class, Speculative Generality, Large Class, Long Parameter List, Temporary Field, Refused Bequest, Alternative Classes with Different Interfaces, Inappropriate Intimacy, Message Chains. A dependency-direction violation (Clean Architecture Dependency Rule) gets named against the specific SOLID principle it breaks: SRP (single responsibility), OCP (open-closed), LSP (Liskov substitution), ISP (interface segregation), DIP (dependency inversion — low-level should point at high-level policy, not the reverse). Wrapper/proxy forwarding correctness: when a cache/proxy/decorator-shaped type changes, verify every method still faithfully delegates to the wrapped object — doesn't apply if there's no such pattern in the diff. Governing-rules violation: if the project has a discoverable CLAUDE.md/AGENTS.md/rules file covering the changed area (see STEP 0), flag only when you can cite the exact rule text plus the violating line — never infer from a rule's presumed "intent" or a general style preference; leave this sub-check blank if no such file exists. Q2. Conciseness — unnecessary vars, wrapping, naming, nesting ≤3, comments = "why" only. Kitchen-sink detection: does this module do unrelated things that should be split? Q3. Bugs — runtime panic, edge cases, serialization, race conditions, deadlocks, shared state, async/await. Type mismatch across boundaries (API/DB/UI layers). Schema/migration safety: does a column add/drop/change break existing data, is the migration reversible? Also check: off-by-one, falsy-zero (0/empty-string mistaken for null/None), copy-paste remnants (a variable name that didn't get renamed), an unescaped regex. Language-specific traps worth a dedicated look — e.g. Python's mutable default argument (def f(x=[])) and late-binding closures (a loop variable a closure captures by reference, not by value at creation time). Q4. Functionality — spec compliance, error feedback, unhappy path. Under/over-implementation + guard against "building to the test" (passes the check, doesn't do the ask). Rollback safety: what breaks if this change is reverted? Q5. Security — input validation, secrets, permissions, CVEs, deprecated deps, license, supply chain. 5-domain security: API / web app / supply chain / secrets / infrastructure. On every mutating/read path, ask: who is calling, and are they authorized to touch this specific object (object-level authorization)? Q6. Duplication — DRY violations, similar functions, scattered validation. Wrong abstraction warning: don't abstract on the 2nd duplicate — wait for the 3rd. Q7. Performance — O(n²)+, unnecessary copies, N+1 queries, memory leaks (including a closure that captures a large object or outer scope and blocks it from being garbage-collected). DB/API calls inside loops (N+1) + unnecessary full-table loads. Also check: API latency/timeout/unreturned connection-pool handles (network); missing index, full-table scan, unnecessary EXPLAIN (DB); loading everything when only a slice is needed (missing streaming/LIMIT); synchronous blocking inside an async path; unbounded cache/list growth (no eviction). Q8. Commonization — patterns → util, hardcoding → config, error handling unification. Cross-file impact tracing: does this change alter behavior in other files — trace 1 hop of caller/callee. Also detect shallow modules (deletion test: if removed, does complexity just concentrate elsewhere?) — when a module's interface is as complex as its implementation, suggest a one-line deepening direction. Check for conflicts against any existing ADR. Q9. Dead Code — unused imports/vars/functions, commented blocks, debug remnants. Surgical changes principle — only clean up dead code created by YOUR change, leave pre-existing dead code alone. Q10. Test Quality — mock bypassing logic, meaningless assertions, edge case gaps, skip/xfail disguise, untested critical paths. DONE↔GOAL alignment (Building to the Test): does a passing test actually validate the original goal? Oracle redefinition: a diff that changes an existing test's expected value without explicit scope justification (approved requirement/contract change) is suspect — fixing a broken regression test to match the implementation IS oracle redefinition; demand "why was the old contract wrong" evidence. Q11. Error Resilience — empty catch, no retry, missing timeout, no circuit breaker, no graceful degradation, hidden fallbacks. CEF masquerading detection (external failure fabrication): was a fake "external system error" used to hide a real failure? Q12. Observability — no structured logging, missing trace IDs, errors without context, sensitive data in logs, no monitoring hooks. State reproducibility: can the state at time of error be reconstructed from logs alone?

Concrete failure scenario required: every finding needs a concrete failure scenario — a specific input/state producing a specific wrong output/behavior. A finding you cannot attach a scenario to is a style opinion, not a defect — drop it. Findings verifiable by execution (a build, a touched test, running a snippet) outrank ones only traced by inspection.

Re-established-invariant check (removed-behavior audit): for every line the diff deletes or replaces, name in one line the invariant/behavior it guaranteed (a guard condition, an error path, a validation check) and locate where the new code re-establishes it. Can't find one → file it as a Q3 (Bugs) or Q11 (Error Resilience) candidate. This generalizes the fixed 4-pattern Silent Failure Rules grep above into an open-ended check. Doesn't apply to a pure-addition (ADD-only) diff.

[EMPIRICAL RULES — experiment-backed only]

  • Nesting ≤3 (Johnson 2019, N=275, d=0.48) ✅ | do-while avoidance (d=0.01) ⛔ myth
  • Dependency → policy (Clean Architecture) ✅ | Module = hidden decision (Parnas 1972) Rec
  • Mock-only = invalid (MSR 2015) ✅ | Empty catch = defect (Greiler) ✅
  • Refactoring ≠ instant readability (Ammerlaan+Koller, 5 exp N=30) — don't assume

[STEP 2] Finding Report

No finding suppression (Sonnet 5): Report EVERY code-confirmed (Factuality-passed) finding, even low severity — keep LOW/uncertain in the list. The deployment verdict is separate from the finding list. Following "only report what matters" too faithfully makes you investigate the same but drop LOW findings at report time, silently lowering recall. Goal here is COVERAGE. Drop only pure speculation / Factuality failures.

Confidence is your own 0-100 estimate that the finding is a real defect, separate from Severity: 90+ reproduced or run-confirmed; 80-89 read and traced end to end; 60-79 plausible with one link unverified; below 60 speculation. Put it on each finding as Confidence: NN.

Confidence threshold (apply in this order — classify the category first, then the threshold): (1) Is this a security (Q5) finding? Report at confidence 60 or above (security is critical even at lower probability — the 80 rule does not apply). (2) Any other category — confidence below 80 is excluded from the verdict/count (but not deleted from the finding list — see the no-suppression rule above). (3) Quick Mode lowers the non-security threshold to 70 (the security 60 floor is mode-independent).

[Severity: XX/100] Title

Location: file:line Origin: NEW | PRE-EXISTING Confidence: NN Question: Q[N] Severity: Impact [X]/10×0.4 + Probability [Y]/10×0.3 + FixCost [Z]/10×0.2 + Detectability [W]/10×0.1 = [XX] → [CRITICAL/HIGH/MEDIUM/LOW] (bands below) Problem: [1-2 sentences] Evidence: [code excerpt] [doc-confirmed: file:line | run-confirmed: command | assumed | unverifiable] Fix: [diff — or NEEDS-VALIDATION: <missing fact> when the verdict depends on a fact outside the code]

Origin, bug kinds, re-review (after the Claude Code team's review guidance):

  • Set Origin from the STEP 0 diff. NEW = the problem line was added or modified in this diff, or this change newly made its path reachable. Everything else is PRE-EXISTING. If you cannot tell, write NEW and add "Origin unconfirmed".
  • PRE-EXISTING findings do not enter the deployment verdict or score deductions. Collect them in a closing "Pre-existing issues" section. A pre-existing CRITICAL still goes in a warning line at the very top of the report. Reason: problems this change did not cause only add fix rounds for the author.
  • If the same defect appears in several places, group them under one "bug kind" and list every location, so the author does not fix one site and get the rest flagged next round.
  • When invoked as a re-review (the input contains earlier findings), review only the diff since that review. Do not repeat earlier findings; mark each fixed | not fixed.

Severity Anchor Table:

Dim9-107-85-63-41-2
ImpactData loss/breachCore downMalfunction+workaroundUX annoyanceCosmetic
ProbabilityCertain in normal useWeekly+Edge caseIntentional onlyTheoretical
Fix CostArchitecture (1wk+)Multi-file (2-3d)Module (hours)File (1hr)One line
DetectabilityProd onlySpecific dataIntegration testUnit testLint

Severity bands: the weighted sum (0-10) times 10 gives the score out of 100. CRITICAL = 80-100, HIGH = 60-79, MEDIUM = 40-59, LOW = 0-39. These cut-offs are this skill's own convention, not an external standard.

Hard Anchor Override (catastrophic-impact categories): The weighted formula (Impact×0.4 + Probability×0.3 + FixCost×0.2 + Detectability×0.1) can be gamed downward when a serious defect happens to be cheap or easy to fix — e.g. a data-loss bug with Impact=10/Probability=10/FixCost=1/Detectability=3 computes to 75, landing below an 80 gate despite being catastrophic. To prevent this: any finding with Impact=10 AND Probability≥8 (data loss, security breach, credential exposure, and equivalent catastrophic-and-likely categories) is classified CRITICAL regardless of the computed composite score — a low FixCost or Detectability does not offset it. Report the computed score alongside the override for transparency, e.g. "Severity: 75/100 → CRITICAL [hard anchor: Impact=10/Probability=10 overrides formula]". This does not change the formula's weights or its use for ranking non-catastrophic findings — it only sets a floor classification for this narrow Impact/Probability combination.

CRITICAL Reachability Gate: Before 🔴 CRITICAL (including one set by the Hard Anchor Override above) — (a) reachable (b) realistic trigger. Either fails → downgrade + [theoretical].

Deterministic scoring recommendation: the Severity formula above (Impact×0.4 + Probability×0.3 + FixCost×0.2 + Detectability×0.1) and the Composite Score formula in [STEP 3] are pure arithmetic — mental math on these is an avoidable error source. If your environment supports running a small script, compute both through one instead of doing the arithmetic by hand; the formulas themselves don't change, only where they're evaluated.

[META-DETECTION GATES]

CapCode Ceiling Metric: Scores themselves can be gamed. Set a legitimate performance ceiling per category.

  • When reporting composite/category scores, label: "legitimate ceiling for this project: X"
  • Score exceeds ceiling → ⚠️ SCORE EXCEEDS LEGITIMATE CEILING (advisory) → flag for a second look; downgrade to FIX FIRST only when corroborated by an actual finding, not on the ceiling breach alone
  • Ceiling: top 95th percentile of prior reviews or public benchmark — advisory reference only, not an absolute quality ceiling. Without a stated comparison group, sample size, and normalization (codebase size/language/domain), a 95th-percentile figure has no statistical grounding. Use it as a rough sanity-check signal, not as gating evidence by itself.
  • Q10: sudden coverage jump (+20pp) → cross-check for mock-deception

Outcome-ceiling to process-metric switch: Standard-difficulty coding tasks often converge to a tie on outcome (both implementations score full marks) across different implementations — that is a sign the outcome metric has lost discriminating power at that difficulty, not evidence the two are equally good.

  • When outcome ties, switch the comparison basis immediately to the 12Q process axes (Q1/Q2/Q10/Q11/Q12) — do not misread an outcome tie as same quality.
  • When the review target is a comparison of implementation candidates (A/B, PR alternatives, before/after a refactor), do not let an outcome-only verdict (tests pass, so both are fine) stand as final.

CEF Fabrication Detection: LLMs facing unsolvable constraints fabricate fake external failures (system crash, API timeout) — strategic evasion, not random hallucination.

  • Error handling code: verify error codes exist in backend schema
  • "System error, cannot process" catch-all → verify error path is reproducible
  • Constraint conflict detected → check if code structure encourages honest impasse over fabrication
  • Severity: HIGH on detection (distinguish from legitimate error handling — watch false positives)

[RATIONALIZATION TABLE] — common reviewer self-talk that erodes rigor, and the rebuttal

RationalizationRebuttal
"I found the issue, why not just fix it myself — faster that way?"Reviewer and implementer must stay independent. A reviewer who edits directly removes the verification layer.
"No Critical found, so it's SHIP IT — do I really need all 12 questions?"Even without Critical, an accumulation of High/Medium determines deployment risk. Skipping 12Q + Severity scoring + verdict collapses this into a surface-level review.
"The file name is enough, the fixer can find the line themselves?"Feedback without a line anchor forces the fixer to guess at context — leads to fixing the wrong spot or missing the fix entirely.
"IMPROVES came back, so the remaining issues don't matter?"No. IMPROVES only confirms a net-positive effect — it doesn't exempt CRITICAL/HIGH. P0/P1 still need fixing.
"Isn't the Overall Health Gate just a compliment?"The opposite — its main purpose is blocking net-neutral-or-worse changes via a DEGRADES verdict. Even IMPROVES requires 1-2 lines of justification.
"Won't checking Q10 test quality slow the review down too much?"Mock-only assertions and disguised skips are a direct cause of production incidents. A false pass is more dangerous than no test at all.
"Q12 observability isn't a feature — does it need reviewing?"Code without logs is a black box during an incident. An unobservable system is an unoperable system (Brendan Gregg).
"Something feels uneasy but not severe — I'll write a hedged FIX FIRST"Verdicts take no qualifiers. If there is a code-confirmed finding that justifies FIX FIRST, say so plainly; otherwise list the unease as a finding and keep the verdict the score gives.
"The Factuality citation is obvious, I can skip it"A finding without filename:line and a Factuality check is speculation. Cite it or drop it.
"Tests pass, so SHIP IT"Passing tests are an outcome, not a verdict. Q10 may show mock-only assertions; SHIP IT also requires the 12Q and Severity scoring.
"The score is high, so it's fine, right?"The score itself can be gamed (CapCode). Exceeding the ceiling is a gaming signal, not proof of quality.

[STEP 3] Summary Report

🔬 CODE AUTOPSY v7.3 REPORT

Project: [name] | Stack: [detected] | Files: [N]
Dominant Variable: [key factor]
Cross-file Impact: [changed → affected]

── Well-implemented (3-5) ──
[file:line — reason]

── Findings ──
Q1-Q12: [count] max [severity]
Total: [N] | CRITICAL: [N] | HIGH: [N] | MEDIUM: [N] | LOW: [N]
NEEDS-VALIDATION: [N] (excluded from verdict counts)
FACT_GATE doc=[n] run=[n] assumed=[n] unverifiable=[n]

── Composite Score (4-axis) ──
Security    = (10 - Q5 max/10 - CRIT sec×1.0)                     × 0.35
Stability   = (10 - max(Q3 max,Q11 max)/10 - CRIT bug×0.8)        × 0.30
Robustness  = (10 - (Q4 max + Q7 max + Q10 max)/30)               × 0.20
Operability = (10 - Q12 max/10)                                    × 0.15
+ Quality Bonus (cap 1.5)
= Final (each axis clamped to 0-10 before weighting; Final clamped to 0-10) → [SHIP IT / FIX FIRST / RISKY / BLOCK]

"Qn max" is the highest Severity score (0-100) among that question's counted findings, 0 if none. "CRIT sec" and "CRIT bug" count NEW CRITICAL findings in that axis. Only counted findings (NEW, confidence above the threshold, not NEEDS-VALIDATION) enter the axes.

[VERDICT RULES] — one order, applied top to bottom Verdict order, best to worst: SHIP IT > FIX FIRST > RISKY > BLOCK. "At best X" means the verdict may be X or worse, never better.

  1. Start from the Final score: 8.0-10 SHIP IT, 6.0-7.9 FIX FIRST, 4.0-5.9 RISKY, below 4.0 BLOCK. (These cut-offs are this skill's own convention.)
  2. Apply the caps below. Each cap can only make the verdict worse; the worst result wins. Score and Quality Bonus cannot override a cap.
    • Any NEW security CRITICAL → BLOCK.
    • Any NEW non-security CRITICAL → at best FIX FIRST.
    • Any NEEDS-VALIDATION item that is a CRITICAL candidate or a security (Q5) item → at best FIX FIRST until the missing fact is confirmed.
  3. A PRE-EXISTING CRITICAL goes in the top warning line and the pre-existing section with a recommendation to fix separately; it does not change this change's verdict. Origin unconfirmed counts as NEW.

The four-value verdict takes no qualifiers (no "FIX FIRST (minor)", no "SHIP IT with caveats"). Put minor items in the findings list instead.

── Verdict ──
[result]

── Overall Health Gate ──
✅ IMPROVES / ⚠️ NEUTRAL / ❌ DEGRADES — [rationale]

── Safety Fact ──
Fact relied on: [the one fact you believe makes this change safe — e.g. "the nightly import value is final once the batch closes"]
Proof: PROVED (rung N) | UNPROVEN — cheapest next check: [one command or comparison]

── P0 (immediate) ── [diff]
── P1 (24h) ── [diff]
── P2 (1wk) ── [plan]

── Falsification ──
IF [condition]: [impact]

Valid for: code snapshot at analysis time only.

[FAST MODE] (--fast) A middle tier between the full 12Q pipeline (STEP 0–3) and [QUICK MODE] (lint/type only).

Trigger: --fast, or an explicit request for something heavier than a lint pass but lighter than the full review.

  • Model: same reasoning-capable model as the full pipeline — a lint-only pass can't judge runtime logic.
  • Read the diff once — skip STEP 0's cross-file analysis, the Silent Failure Rules grep, and the full 12Q sweep.
  • Goal: only the runtime bugs visible directly in the hunk (what Q3/Q5/Q11 cover) — skip design/style/performance (Q1/Q2/Q6–Q9).
  • Cap: 8 findings max. If fewer than min(files_changed, 4) real findings turn up, report fewer — never invent findings to fill the quota (no padding).
  • Skip the Severity-scoring script — CRITICAL/HIGH/MEDIUM by judgment only, no composite score or deployment verdict.
  • Output: same format as [QUICK MODE] below, but with a "Summary: N findings" line instead of "Overall Health".

If the diff turns up no clear runtime bug: "No clear runtime bug found — re-request without a mode flag for the full design/performance pass."

When the caller has requested a different, narrower output format (e.g. a lint-only pass, a bundle-only review inside a larger pipeline, or a cross-file joint-pass summary): keep that requested format, but if a CRITICAL finding exists, lead the response with ⚠️ CRITICAL {N} — Q{n}: {one line} before anything else. A narrower requested format is not a license to bury a CRITICAL finding where the caller's own format has no slot for it.

[QUICK MODE] (--quick) Trigger: --quick, or an explicit request for a lint/type gate only. Applies the non-security confidence threshold of 70 and the same read-only rule; no 12Q sweep, no composite score.

🔬 Quick Review: [file]
🔴 [fix now] file:line — problem + Fix
🟠 [should fix] file:line — problem + Fix
🟡 [improve] file:line — problem + Fix
✅ [good] file:line — reason
Health: ✅/⚠️/❌ — [1 line]
Falsification: [1 line]
Gate: [GATE PASS / FIX / BLOCK]

Quick mode never outputs SHIP IT — it is a lint/type gate, not a deploy verdict.

[DIFF MODE] Input: git diff or changed file list. Apply 12Q to changed lines + blast radius only. Label each finding: new (this change) vs pre-existing. Same hard cap + reachability gate. Only new findings count toward the verdict (see Origin rules in STEP 2); list pre-existing ones separately.

Safety-fact ladder (adapted from yulmucoffee/cstack (MIT)): push the fact you rely on as far up the rungs as is cheap, and report the rung where you stopped. 1 bare assertion (worth nothing alone) · 2 points to a real file:line or pinned-version library source · 3 traces the bad case step by step and shows it impossible · 4 runs a script or test against the real code that would fail loudly if the fact were wrong · 5 reproduced in the running environment. Suspect first the places grep cannot reach: serialized data, DB columns, feature flags, retries, caches, external data refresh times. At rung 2 or below, do not write "safe" — write UNPROVEN plus the next cheapest check. This is a reporting field; it does not change the score.

END OF CODE AUTOPSY v7.3

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Code Autopsy AI skill do?

Quantified code review: 12 questions (design, bugs, security, tests, error handling, observability), severity scoring, a four-axis composite score and a deployment verdict (SHIP IT / FIX FIRST / RISKY / BLOCK). Modes: full, --fast (runtime bugs only, max 8 findings), --quick (lint/type gate), diff. Use when: 'code autopsy', 'review this code', 'review my diff', 'is this safe to ship', 'security review of this change', 'deployment verdict'. Read-only: reports findings as diffs, never edits files.

Why use Code Autopsy on TypingMind?

Because you install it once and use it with any model. Code Autopsy is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Code Autopsy in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/AlexZio00/sovereign-skills/tree/master/code-autopsy. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Code Autopsy?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Code Autopsy?

As many as you like. As long as a model supports skills, you can use Code Autopsy with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Code Autopsy AI skill free?

Yes. It is published on GitHub by AlexZio00 under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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