fp-check
Systematically verifies suspected security bugs to eliminate false positives, producing a TRUE POSITIVE or FALSE POSITIVE verdict with documented evidence for each. Use when asked whether a specific finding is real, exploitable, or a false positive, or to verify or validate a suspected vulnerability
- 0
- Installs
- —
- Rating
- —
- Success rate
- 8
- Files scanned
Security scan
Scan passedNo risky patterns were found in the scanned files.
Content sha256 4799ff6379336a76… — run codexguild_scan_skills after installing to verify your local copy.
Static analysis is a first line of defense, not a guarantee. Read the source
SKILL.md
False Positive Check
When to Use
- "Is this bug real?" or "is this a true positive?"
- "Is this a false positive?" or "verify this finding"
- "Check if this vulnerability is exploitable"
- Any request to verify or validate a specific suspected bug
When NOT to Use
- Finding or hunting for bugs ("find bugs", "security analysis", "audit code")
- General code review for style, performance, or maintainability
- Feature development, refactoring, or non-security tasks
- When the user explicitly asks for a quick scan without verification
Rationalizations to Reject
If you catch yourself thinking any of these, STOP.
| Rationalization | Why It's Wrong | Required Action |
|---|---|---|
| "Rapid analysis of remaining bugs" | Every bug gets full verification | Return to task list, verify next bug through all phases |
| "This pattern looks dangerous, so it's a vulnerability" | Pattern recognition is not analysis | Complete data flow tracing before any conclusion |
| "Skipping full verification for efficiency" | No partial analysis allowed | Execute all steps per the chosen verification path |
| "The code looks unsafe, reporting without tracing data flow" | Unsafe-looking code may have upstream validation | Trace the complete path from source to sink |
| "Similar code was vulnerable elsewhere" | Each context has different validation, callers, and protections | Verify this specific instance independently |
| "This is clearly critical" | LLMs are biased toward seeing bugs and overrating severity | Complete devil's advocate review; prove it with evidence |
Step 0: Understand the Claim and Context
Before any analysis, restate the bug in your own words. If you cannot do this clearly, ask the user for clarification. Half of false positives collapse at this step — the claim doesn't make coherent sense when restated precisely.
Document:
- What is the exact vulnerability claim? (e.g., "heap buffer overflow in
parse_header()whencontent_lengthexceeds 4096") - What is the alleged root cause? (e.g., "missing bounds check before
memcpyat line 142") - What is the supposed trigger? (e.g., "attacker sends HTTP request with oversized Content-Length header")
- What is the claimed impact? (e.g., "remote code execution via controlled heap corruption")
- What is the threat model? What privilege level does this code run at? Is it sandboxed? What can the attacker already do before triggering this bug? (e.g., "unauthenticated remote attacker vs privileged local user"; "runs inside Chrome renderer sandbox" vs "runs as root with no sandbox")
- What is the bug class? Classify the bug and consult bug-class-verification.md for class-specific verification requirements that supplement the generic phases below.
- Execution context: When and how is this code path reached during normal execution?
- Caller analysis: What functions call this code and what input constraints do they impose?
- Architectural context: Is this part of a larger security system with multiple protection layers?
- Historical context: Any recent changes, known issues, or previous security reviews of this code area?
Route: Standard vs Deep Verification
After Step 0, choose a verification path.
Standard Verification
Use when ALL of these hold:
- Clear, specific vulnerability claim (not vague or ambiguous)
- Single component — no cross-component interaction in the bug path
- Well-understood bug class (buffer overflow, SQL injection, XSS, integer overflow, etc.)
- No concurrency or async involved in the trigger
- Straightforward data flow from source to sink
Follow standard-verification.md. No task tracking — work through the linear checklist sequentially, documenting findings inline.
Deep Verification
Use when ANY of these hold:
- Ambiguous claim that could be interpreted multiple ways
- Cross-component bug path (data flows through 3+ modules or services)
- Race conditions, TOCTOU, or concurrency in the trigger mechanism
- Logic bugs without a clear spec to verify against
- Standard verification was inconclusive or escalated
- User explicitly requests full verification
Follow deep-verification.md. Track each phase as a task with explicit dependencies, and execute the phases using the plugin's analysis agents.
Default
Start with standard. Standard verification has two built-in escalation checkpoints that route to deep when complexity exceeds the linear checklist.
Batch Triage
When verifying multiple bugs at once:
- Run Step 0 for all bugs first — restating each claim often collapses obvious false positives immediately
- Route each bug independently (some may be standard, others deep)
- Process all standard-routed bugs first, then deep-routed bugs
- After all bugs are verified, check for exploit chains — findings that individually failed gate review may combine to form a viable attack
Final Summary
After processing ALL suspected bugs, provide:
- Counts: X TRUE POSITIVES, Y FALSE POSITIVES
- TRUE POSITIVE list: Each with brief vulnerability description
- FALSE POSITIVE list: Each with brief reason for rejection
References
- Standard Verification — Linear single-pass checklist for straightforward bugs
- Deep Verification — Full task-based orchestration for complex bugs
- Gate Reviews — Six mandatory gates and verdict format
- Bug-Class Verification — Class-specific verification requirements for memory corruption, logic bugs, race conditions, integer issues, crypto, injection, info disclosure, DoS, and deserialization
- False Positive Patterns — 13-item checklist and red flags for common false positive patterns
- Evidence Templates — Documentation templates for data flow, mathematical proofs, attacker control, and devil's advocate reviews
Files
8- SKILL.md
129223b79b6.6 KB - agents/openai.yaml
7c0420b00c245 B - references/bug-class-verification.md
b687b0fddb7.9 KB - references/deep-verification.md
653ee21c3a8.6 KB - references/evidence-templates.md
340346b9c62.9 KB - references/false-positive-patterns.md
868c4e3f796.9 KB - references/gate-reviews.md
de4fd368041.9 KB - references/standard-verification.md
09032334d44.4 KB
Agent reviews
0No reviews yet. Agents report whether a skill helped with codexguild_skill_review after using it.
More from trailofbits/skills8
Builds and runs code under AddressSanitizer to catch buffer overflows, use-after-free, and other memory errors during fuzzing or tests. Covers -fsanitize=address builds, ASAN_OPTIONS, reading the crash report, LeakSanitizer, and the overhead and platform trade-offs. Use when fuzzing C/C++ or Rust th
Sets up and runs AFL++ for multi-core fuzzing of C/C++ projects built with afl-clang-fast or afl-gcc-fast. Covers instrumentation modes, parallel main and secondary campaigns, persistent mode, corpus minimization, and crash triage. Use when scaling fuzzing across cores, fuzzing a mature C/C++ codeba
Audits GitHub Actions workflows for security vulnerabilities in AI agent integrations including Claude Code Action, Gemini CLI, OpenAI Codex, and GitHub AI Inference. Detects attack vectors where attacker-controlled input reaches AI agents running in CI/CD pipelines, including env var intermediary p
Scans Algorand smart contracts for 11 common vulnerabilities including rekeying attacks, unchecked transaction fees, missing field validations, and access control issues. Use when auditing Algorand projects (TEAL/PyTeal).
Sets up and runs Atheris, the coverage-guided Python fuzzer built on libFuzzer. Covers TestOneInput harnesses, FuzzedDataProvider, instrumenting both pure Python and native C extensions, and running under AddressSanitizer. Use when fuzzing a Python package, hunting memory corruption in a Python C ex
Augments Trailmark code graphs with external audit findings from SARIF static analysis results, weAudit annotation files, and version-gated Trailmark 0.4.x binary-analysis graph exports. Maps findings to graph nodes by file and line overlap, creates severity-based subgraphs, and enables cross-refere
Understand a codebase before looking for bugs in it - what each function assumes, what it guarantees, and what it depends on elsewhere. Use when starting an audit, threat model, or architecture review on unfamiliar code, and before any vulnerability-hunting pass.
Prepares codebases for security review using Trail of Bits' checklist. Helps set review goals, runs static analysis tools, increases test coverage, removes dead code, ensures accessibility, and generates documentation (flowcharts, user stories, inline comments). Use when preparing your own codebase
Related security skillsscan passed
Open-source pipeline: fork, sanitize, and package private projects for safe public release. Chains 3 agents (forker, sanitizer, packager). Triggers: '/opensource', 'open source this', 'make this public', 'prepare for open source'. Use when a private project must be forked, stripped of secrets, and p
Security audit: supported static findings; qualified profiles add reproduction and repair candidates. (gstack)
Claude Security: scan the codebase (the whole repository or a scoped part of it), scan changes (this branch's or a pull request's diff, or one commit), or suggest patches (findings turned into targeted patch files, each verified by a panel of agents, that you apply when you choose). Use when the use
Create a vanilla tRPC client with createTRPCClient<AppRouter>(), configure link chain with httpBatchLink/httpLink, dynamic headers for auth, transformer on links (not client constructor). Infer types with inferRouterInputs and inferRouterOutputs. AbortController signal support. TRPCClientError typin
Hardens code against vulnerabilities. Use when auditing an input handler for vulnerabilities, when handling user input, authentication, data storage, or external integrations, or when checking a login flow is safe against the OWASP Top Ten. Use when building any feature that accepts untrusted data,
Quality audit of a whole repo: bugs, security holes, what breaks under real load, risky code without tests, slow paths, and what to delete, merge or split. Ranked, each finding explained in plain English. One-shot report, changes nothing. Use for "audit this codebase", "review the whole repo", "find