C Review logo

C Review

OrganizationPopular
trailofbits
c-review

Performs comprehensive C/C++ security review for memory corruption, integer overflows, race conditions, and platform-specific vulnerabilities. Use when auditing native C/C++ applications, reviewing daemons or services for memory safety, or hunting integer overflow / use-after-free / race conditions in userspace code.

Overview

Publishertrailofbits
Repositoryskills
Skill namec-review
Stars
7.1K
Forks
611
Bundled files
Instructions only
LicenseCC-BY-SA-4.0
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.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

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

Installation

Install the C Review 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.
  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/trailofbits/skills.git /tmp/skills
mkdir -p .claude/skills
cp -r /tmp/skills/plugins/c-review/skills/c-review .claude/skills/c-review
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable C Review 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 C Review 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 C Review 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.

C/C++ Security Review

Resolve four parameters, make one Workflow call, return the report. The workflow owns concurrency, retries and result collection.

Use for: native C/C++ userspace — memory safety, integer overflow, races, type confusion, Linux/macOS daemons, Windows services.

Not for: kernel drivers or modules; managed languages (Java, C#, Python, Go, Rust); embedded or bare-metal code with no libc.

Phase 0 — Parameters

Parse any free text on the invocation line (flamenco only, high severity only, use haiku) and pre-fill what it implies. Then make one AskUserQuestion call for whatever is still unresolved. Never silently default a required parameter.

ParameterValuesInferring it from the invocation
threat_modelREMOTE / LOCAL_UNPRIVILEGED / BOTH"remote", "network", "attacker" → REMOTE; "local", "unprivileged" → LOCAL_UNPRIVILEGED; otherwise ask
worker_modelhaiku / sonnet / opus / inheritAn explicit model name. Otherwise ask. inherit uses the session model
severity_filterall / medium / high"all", "every", "noisy" → all; "medium and above" → medium; "high only" → high; otherwise ask
scope_subpathrepo-relative directory, optional"X only", "just audit X/" → the matching subdirectory, fuzzy-matched against top-level dirs. Absent → .. Ambiguous → ask

Two scopes stay separate for the whole run:

  • finding_scope_root = scope_subpath (default .) — a finding must live inside it, and it is the tree the unit list is generated from.
  • context_roots = . — read freely to establish callers, build flags and reachability. Narrow it to finding_scope_root only if the user explicitly forbids wider reading, and say that reachability confidence drops when you do.

Phase 1 — Resolve paths

bash
root="${CLAUDE_PLUGIN_ROOT:-}"
if [ -z "$root" ] || [ ! -f "$root/workflows/c-review.js" ]; then
  # Fallback for a cache layout that does not set the variable. ~/.claude ONLY — never `.`:
  # `.` is the AUDITED repository, and a tree that vendors or mirrors this marketplace would
  # win the traversal and run its copy of the scripts, with a different question set and
  # nothing saying which copy ran. Let find's stderr through; a missing ~/.claude is a real
  # failure to report, not noise to hide.
  hit="$(find "$HOME/.claude" -path '*/c-review/workflows/c-review.js' -print -quit)"
  root="${hit%/workflows/c-review.js}"
fi
[ -n "$root" ] && [ -f "$root/workflows/c-review.js" ] && echo "PLUGIN ROOT: $root"

Stop if neither resolves, rather than running with an empty path — and say which path you resolved, so a copy other than the installed plugin is visible before eight agents run against it.

bash
# The workflow cannot call Date.now(), so the timestamp is made here.
output_dir="$(pwd)/.c-review-results/$(date -u +%Y%m%dT%H%M%SZ)"
mkdir -p "$output_dir"; echo "$output_dir"

# A Workflow script has no filesystem APIs, and `assemble_findings.py` resolves `--scope`
# against ITS OWN cwd. Resolve it once here and pass BOTH spellings, or the workflow strips
# `src/` from a finding's path while the assembler strips `/repo/src/`, and the two disagree
# about which findings are duplicates of each other.
scope_abs="$(cd "${scope_subpath:-.}" && pwd)" || echo "scope_subpath does not exist"
echo "$scope_abs"

uv must be on PATH: Detect runs the unit enumerator and Assemble runs assemble_findings.py. If uv is missing, say so and stop — the whole review is partitioned from that unit list.

Phase 2 — Run the workflow

Invoking this skill is the opt-in to multi-agent orchestration — call Workflow without asking again. A review of a real codebase also runs past any default workflow size guideline; that guideline is advisory and this is the case it exempts. Do not shrink the fan-out to fit it, and do not substitute hand-spawned Agent calls.

One Workflow call. scriptPath takes the absolute path resolved in Phase 1; args must be a real JSON object, not a JSON-encoded string.

Workflow({
  scriptPath: "<plugin_root>/workflows/c-review.js",
  args: {
    outputDir:        "<output_dir>",
    pluginRoot:       "<plugin_root>",
    threatModel:      "REMOTE",
    severityFilter:   "all",
    findingScopeRoot: "expat/lib",
    findingScopeRootAbs: "/abs/path/to/repo/expat/lib",
    contextRoots:     ".",
    workerModel:      "sonnet"
  }
})

findingScopeRootAbs is the scope_abs from Phase 1 and is not optional in practice: omitted, the workflow tells the assembler no absolute root is known and a finding filed as /repo/expat/lib/xmlparse.c stops merging with the same bug filed as xmlparse.c.

Six further arguments are optional. Omitted, each takes its default; passed with the wrong TYPE, the workflow throws with the field name rather than defaulting. Pass them only when the user asks or when running an evaluation:

ArgumentDefaultWhat it is for
maxUnitLines150Cap on a review unit; a larger function is split at syntactic seams. Raising it reintroduces the saturation the cap prevents
linesPerAgent1500Source lines per review agent. A no-op on a small tree--agent-min (default 4) floors the derived count, so two very different values can produce identical assignments. Use reviewAgents to pin the fan-out
reviewAgentsderivedPins the review fan-out, subject to the same floor as the derived count: both are clamped to 4–14, and an explicit value above 14 raises the cap to itself. A value below 4 is raised to 4, and a trailing slice too small to be worth an agent is folded into its neighbour, so the final count can come out one lower than asked
invariantAuditfalseAdds the shared-state invariant audit to the sweep. A whole extra agent; turn it on for state-machine-heavy targets
exclude[]Array of globs or substrings the unit enumerator skips (each becomes a repeated --exclude). Use when enumeration aborts naming a path it cannot own — a symlink resolving outside the scope root, an unreadable directory — and the excluded paths land in the enumerator's totals as a visible coverage hole, not silence
benchmarkModefalseEval-only. Adds an external-source declaration to reviewer prompts and two schema fields. Changes no finding; leave it off for a real audit

The workflow validates its own arguments and throws with a named field if one is missing. It runs five phases:

PhaseAgentsWhat it does
Detect1Runs enumerate_units.py for the unit list; platform flags from actual API usage; shared-state structs; per bug class, whether any candidate site exists
Review4–14One agent per contiguous slice of the unit list. Each returns findings with severity and a ledger row per (unit × question)
Sweep0–2The class axis: one agent over every bug class with no entry anywhere that Detect did not rule out. None, and the phase is skipped. Plus the struct-field audit when invariantAudit: true
Dedup0–1Only for collisions the assembler cannot merge deterministically. Usually skipped
Assemble1Runs assemble_findings.py: ledger gate, deterministic merges, findings.json, REPORT.md, REPORT.sarif

Around 8–10 agents on a mid-size target.

Phase 3 — Return the report

Read <output_dir>/REPORT.md and return it.

Say once, plainly, next to the findings: no false-positive review ran. Every severity is the reviewer's own (severity_source: "reviewer"), judgeRan is always false, and nothing rejected anything. Expect some of what you are shown to be wrong or out of scope — say so rather than presenting the list as adjudicated, and do not filter it yourself.

Then surface, prominently and separately from the findings, anything in the workflow result that means the run was partial:

FieldMeaning and what to do
artifactsWritten: falseREPORT.md and REPORT.sarif are missing; the part files under parts/ are intact. Re-assemble by hand (below) — never reconstruct from the tool result
artifactsWritten: nullThe assemble agent returned nothing, so whether the artifacts exist is unknown — the command may have completed and only its structured answer failed. List the output directory before doing anything else
gateAccepted: false with artifacts writtenThe failure that looks like success. Artifacts are complete; the coverage gate could not run or rejected the ledger, so the review is assembled and unverified. Do not re-run the assembler — read ledger-gate.json and report the gap. artifactError always carries the reason
coverage: nullCoverage is unmeasured, not complete. Never report such a run as fully covered; point at ledger-gate.json
coverageReport checksSatisfied / checksRequired, and checksCompleted only as "answered" — never "functions reviewed". checksSatisfied below checksCompleted means the gate threw rows out: a coverage-integrity failure. Name the violations
groupsFailed, agentFailures, notesThat ground was not covered. Do not let a clean report imply it was
unrecognisedPartsWhole agents' output in no artifact. null means unchecked, not none
silentClasses / ruledOutClasses / platformDroppedClasses"Swept and found nothing" / "nothing looked" / "out of scope by configuration". Report separately — only the middle one means a human should look

Hand re-assembly, when artifactsWritten is false:

uv run --no-project <plugin_root>/scripts/assemble_findings.py --run-dir <output_dir> \
  --threat-model <MODEL> --severity-filter <FILTER> --no-judge \
  --scope <finding_scope_root> --context-roots <context_roots> \
  --worker-model <worker_model> \
  --expect <part-stem>=<finding-count>   # one per part the workflow log names
  • --expect is not optional. Without it the allowlist admits every file under parts/, including one nobody dispatched, and the run exits 1 for that reason alone. If you cannot recover the counts from the workflow log, exit 1 is correct and the run is assembled-but-unverified.
  • Never drop --no-judge. Dropping it overwrites every reviewer severity with MEDIUM and records judge_ran: true for a run no judge ever saw.
  • Six flags cannot be reconstructed at all (--expect-complete, --benchmark-mode, --groups-attempted, --groups-failed, --agent-failure, --external-source), so the document will say agent_failures: [] on a run that may have lost a slice. Report that. Without a units.json the assembler also exits 1: no gate ran.

Finally list the artifacts: findings.json, REPORT.md, REPORT.sarif, units.json, ledger-gate.json, detect.json, and the parts/ and assignments/ directories.

Rationalizations to Reject

  • "The run mostly worked, so I'll just present the report." A failed agent is uncovered ground, not a rounding error. Report it next to the findings.
  • "Coverage is 80%, that's basically complete." The missing 20% is a list of exact (unit, question) pairs in ledger-gate.json. Name them.
  • "I'll write the findings myself instead of running the workflow." Hand-orchestrating costs far more for worse recall. Always call Workflow.
  • "The artifacts failed, so I'll reconstruct the report from the tool result." The part files are on disk and the assembler is deterministic. Re-run it.
  • "Zero findings, so there is nothing to report." A zero-finding run still produces both artifacts, and zero findings on real C code is itself worth saying out loud.
  • "The workflow returned findings, so I can skip reading REPORT.md." The tool result is capped and carries counts, not findings. The report is the artifact.
  • "No judge ran, so I should filter the findings myself." No — silently dropping findings reproduces a judge's cost with none of its rigour and leaves the artifact disagreeing with what you said. Report what the pipeline produced, labelled unadjudicated.
  • "No judge ran, so I'll present severities as authoritative." Also no. They are one reviewer's opinion.
  • "A class-per-agent fan-out would find more." Location is the partition on purpose; the class catalogue is a bounded completeness sweep on top. Do not add one.

Design rationale, the coverage gate's threat model and its known limitations are in AGENTS.md. Read it before changing a prompt or a gate rule.

Frequently asked questions

What does the C Review AI skill do?

Performs comprehensive C/C++ security review for memory corruption, integer overflows, race conditions, and platform-specific vulnerabilities. Use when auditing native C/C++ applications, reviewing daemons or services for memory safety, or hunting integer overflow / use-after-free / race conditions in userspace code.

Why use C Review on TypingMind?

Because you install it once and use it with any model. C Review 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 C Review in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/trailofbits/skills/tree/main/plugins/c-review/skills/c-review. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use C Review?

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 C Review?

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

Is the C Review AI skill free?

Yes. It is published on GitHub by trailofbits under the CC-BY-SA-4.0 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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