code-review
Review code for actionable defects. Correctness is the core; performance and security are optional sub-cases of the same engine. Anchors on agreements between participants across a boundary, forces a violating execution, and refutes every candidate before reporting
Install / Use
npx skills add phuryn/pm-skills --skill code-reviewInstalls into whichever agent you are using.
SKILL.md
Installable skill definition
Quality Score
Category
SecuritySupported Platforms
Our assessment of code-review
code-review scores 97/100 on our quality scale, 75th of 544 Security skills we index (top 14%).
Its SKILL.md is 14 KB long, well organised into 12 sections with 3 code examples: a thorough specification that gives an agent plenty to work with.
With 26,568 GitHub stars, it is one of the more widely adopted skills in the catalogue.
Maintenance, license and trust
- The repository was last updated 11 days ago, so code-review is actively maintained.
- It is released under the MIT license, a permissive license that allows use, modification and commercial use with attribution.
- Its trust signals score 100/100, with no cautions. These come from repository metadata, not a code audit — read the skill file before letting an agent act on it.
Safety scan
No issues foundOur scan of the whole file found no instruction hijacking, hidden characters, credential access, data exfiltration or destructive commands. An AI review of the same text found nothing harmful.
AI review by kimi-k2.7-code on 2026-09-25. Automated pattern scan on 2026-09-25. It catches known dangerous patterns, not every risk — read a skill before letting an agent act on it.
code-review compared with similar skills
All 4 of these similar skills score higher than code-review; compare them before choosing.
| Skill | Score | Stars | Updated | Format |
|---|---|---|---|---|
| code-review (this skill)by phuryn | 97 | 26.6k | 11d ago | SKILL.md |
| algorithmic-artby anthropics | 100 | 177.9k | 3d ago | SKILL.md |
| pptxby anthropics | 100 | 177.9k | 3d ago | SKILL.md |
| designby nextlevelbuilder | 100 | 130.2k | 4d ago | SKILL.md |
| ui-ux-pro-maxby nextlevelbuilder | 100 | 130.2k | 4d ago | SKILL.md |
Frequently asked questions
- How do I install code-review?
- Run
npx skills add phuryn/pm-skills --skill code-review. The install tabs above show the steps for each supported agent. - Which AI agents does code-review work with?
- It is written for Universal, as a SKILL.md file. Other agents that read the same format can often use it too.
- Is code-review safe to use?
- Our scan of the whole file found no instruction hijacking, hidden characters, credential access, data exfiltration or destructive commands. An AI review of the same text found nothing harmful. It is MIT-licensed and scores 100/100 on trust signals. Skills are instructions an agent will follow, so read the file before installing it and do not approve commands you do not understand.
- Is code-review still maintained?
- The repository was last updated 11 days ago, so code-review is actively maintained.
Skill content
View source on GitHubname: code-review description: "Review code for actionable defects. Correctness is the core; performance and security are optional sub-cases of the same engine. Anchors on agreements between participants across a boundary, forces a violating execution, and refutes every candidate before reporting. Use when asked to review changes, find bugs, audit a codebase, or check whether a fix is safe."
Code Review
Purpose
Most review output is noise: a list of things that look wrong, unranked, unrefuted, and impossible to act on. This skill produces the opposite — a small number of findings, each with a required behaviour, a feasible trigger, a concrete contradiction, an observable consequence, and the strongest counterargument already checked.
Its central bet: the defects reviewers miss are rarely visible inside one file. They are disagreements between two participants that each look reasonable alone — a caller and a callee, a producer and a consumer, a writer and a later reader, two branches that should establish the same state. A checklist applied file-by-file cannot see those, because the two halves are never in view at the same time. So the unit of review here is the agreement, not the file.
Structure: one engine, three anchors
Code review is the skill. Correctness is its core — the dimension generic tooling covers worst, and the one described in full below. Performance and security are sub-cases: the same engine, the same refutation discipline, the same report contract, with a different anchor and one or two extra rules each.
| Sub-case | Anchor | Where its rules live |
|---|---|---|
| Correctness (core, default) | Agreements between participants across a boundary | This file + references/correctness-taxonomy.md |
| Performance | Workload → resource demand → growth or contention → consequence | references/performance-review.md |
| Security | Source → trust boundary → sink, with an attacker controlling the source | references/security-review.md |
Read a sub-case's file only when that sub-case is selected. Each is short on purpose: it states what differs, and the rest of this file still applies.
Sub-cases are independently activated, not mutually exclusive. One root cause can carry correctness and security impact — report it once, with both impacts.
Invocation
/pm-ai-shipping:code-review
/pm-ai-shipping:code-review dimensions=correctness scope=changes
/pm-ai-shipping:code-review dimensions=performance,security
/pm-ai-shipping:code-review dimensions=all
Claude Code ships its own bundled /code-review. Use the plugin-qualified form above when you mean
this one.
These are instruction arguments, not shell flags.
- Default:
correctness. Bare "review this" or "find bugs" means correctness only. - An explicit list selects exactly those sub-cases;
allselects three. Never silently reinterpret an unknown or empty selection — ask. - Scope: use what was asked. Otherwise review working changes if present, else the repository.
- State the selected dimensions, the scope and the comparison baseline before investigating.
- Reviewing changes means following dependencies beyond the changed lines, and distinguishing defects the change introduced from defects it merely revealed.
- Review and report. Apply fixes only when asked.
Shared engine
Every sub-case uses one skeleton. Only the anchor and the refutation rules differ.
Map a flow → identify an obligation → inspect every participant → construct a violating execution → trace the consequence → attempt refutation → report.
Build one minimal map first: inputs, major execution flows, who owns which state, external dependencies, observable effects. Each selected sub-case enriches it — do not build three maps, and do not make a security-only run wait on correctness mapping.
Correctness: the agreement engine
A boundary is semantic, not a file split. It separates a caller and a callee, two callbacks, two executions of the same function, a producer and a consumer, or a value written now and read later.
For each consequential agreement, hold these in working notes — not in the report:
Participants:
Value, entity or effect exchanged:
Authority (who decides the real answer):
Identity and lifetime/version:
Required relationship:
Evidence for that relationship:
Relevant transitions or orderings:
Observable consumer or consequence:
Establish the obligation without inventing intent. Evidence comes from specifications, documented contracts, language or protocol semantics, tests that encode an expectation, or a necessary producer/consumer relationship. A consumer's implementation alone does not prove the consumer is right. Where participants disagree, say why the disagreement produces a wrong outcome — sometimes the contradiction is certain while which side should change is genuinely open. Missing documentation is a limitation, not automatically a finding.
Start where agreements are most likely to break: values transformed or negotiated, identities reassigned, work becoming asynchronous, state persisted and reloaded, several effects that must agree. Then do a local pass over ordinary decisions, arithmetic, boundaries and error branches — the anchor must not become a filter that discards plain bugs.
Force a violating execution
A suspicion is not a finding until you construct the execution that breaks it. Where the implementation permits:
- make a requested value differ from the accepted or effective one;
- keep two operations live at once and vary their completion order;
- change the relevant identity or generation between observation and use;
- compare distinct transitions that should end in equivalent state;
- inject failure between effects, and interruption before completion;
- exercise empty, exact-boundary and adjacent-boundary inputs.
Establish that each case is actually reachable. Do not assume it.
Two lenses that need a forced probe, not a mention
Across a large evaluation of planted runtime defects in real codebases, two classes were almost never even reported by strong agents — not missed at the fix, missed at the look. Naming them in a checklist will not help; each needs an explicit probe:
- Authority reconciliation. Follow a proposed value through validation, normalisation, negotiation or commit, and find downstream state still derived from the proposal where the authority can return something different. A requested value is not an applied value. Probe: force them apart and ask what still reads the request.
- Identity and correlation. Trace how an operation's result finds its originating entity, then establish that the key is unique, stable and live for long enough — under overlap, reordering, removal and reuse. A label, a position or arrival order is suspicious exactly when those properties can fail. Probe: run two operations concurrently and complete them out of order.
The full set of thirteen diagnostic lenses, each with a detection tell, is in
references/correctness-taxonomy.md. They are overlapping lenses, not a quota to fill.
Refutation: the discipline that makes this worth running
A candidate becomes a finding only with all five:
- A supported obligation — what must hold, and on what evidence.
- A feasible execution — inputs, state and ordering the real system permits.
- A concrete contradiction — where the obligation fails.
- An observable consequence — wrong output, state, effect, completion or progress.
- An examined counterargument — the strongest mechanism that would prevent or repair it.
Actively hunt for the refutation: an enclosing guarantee that makes the execution impossible; synchronisation excluding the interleaving; reconciliation before any consequential read; an intentional contract; a precondition excluding the input; a different owner responsible for it.
| Outcome | Rule | |---|---| | Keep | Evidence establishes the defect; the counterargument checked does not prevent it. | | Drop | Cited evidence defeats the execution, the obligation or the consequence. | | Unresolved | An essential contract or runtime fact is unknown. List it separately from findings. |
Do not import the security sub-case's attacker/victim test into correctness. A correctness defect can harm only the person who triggered it and still be serious. Equally, "keep unless disproved" is too permissive here — an ungrounded suspicion with no constructed execution is not a finding. When both sub-cases are active, apply each test only to its own dimension.
Absorption is not prevention. The most expensive refutation mistake is finding something downstream that happens to hide the defect - a cache that usually holds the value, a retry that usually succeeds, a default that is usually right - and dropping the finding. That is not a guarantee, it is a coincidence with good odds, and it fails the day the absorber is cold, evicted or reconfigured. Drop only on a mechanism that makes the execution impossible, and say which mechanism it was. For the same reason, "it works nearly always" describes a race, not a refutation - a timing window that usually resolves correctly is a finding, and the fact that you had to reason about which side usually wins is the evidence.
Passing tests, unfamiliar code, a suspicious name, a missing test and a sibling difference are evidence to investigate — none of them is proof, and none is refutation. Deduplicate by violated agreement and root cause, never by file. There is no findings quota; zero supported findings is a valid result.
Parallelism
Fan out over complete flows or connected groups of agreements — never over files, and never one agent per taxonomy class. Partitioning by file is precisely the split that hides cross-boundary defects, which are the ones worth finding.
- The coordinator builds the initial map and identifies shared state.
- Each worker gets a bounded flow, its participants, the selected sub-cases and open questions.
- Workers inspect both sides of their agreements and may follow dependencies outside their list.
- Workers return candidates, cited evidence, completed refutations and unresolved relationships.
- The coordinator reconciles assumptions and any relationship that crosses assignments.
- Strong candidates get a separate verification pass before they are reported.
Allow overlapping reads. Two workers reading the same authority is far cheaper than either one holding half its contract. Keep integration capacity in reserve: an unresolved relationship spanning two assignments stays unexamined until someone closes it. One level of fan-out is the target; if delegation is unavailable or the scope is small, run the same procedure sequentially.
Run workers on the strongest model available, and match the current session's effort level. This is recall-first work: a missed cross-boundary flow is the costly failure, and a worker that silently drops to a cheaper model or a lower effort is the cheapest way to lose one. If any worker is rerouted or downgraded, say which in the report — a reader who assumes one model saw everything will misjudge the coverage.
One model. Name it on every worker. Fan-out here buys coverage, not a second opinion. Pass the
coordinator's own model explicitly on each spawn — "inherit" is not a routing decision, and a worker
that quietly lands on a cheaper model is the easiest way to lose a finding. Do not bring in a
different model, to review or to cross-check, unless you are explicitly asked: mixing models makes
the result unattributable, and when this skill is being measured or compared across models, one
foreign worker invalidates the number. The independent second-model pass is a separate,
explicitly-invoked step (/ship-check Step 6), never something this skill reaches for on its own.
**Te
Truncated for display — read the full file on GitHub.
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Trust signals
From repository metadata: license, adoption, age and documentation. Not a code audit — see the Safety scan above for what the skill file itself contains.
