MCPInstall

finding-discovery

Use when Codex is already in the finding-discovery phase of a security scan or the user explicitly asks to discover candidate security findings in a repository or code change. Do not use as the primary trigger for full PR, commit, branch, patch, or repository scans.

Installs
69
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Security
Not audited

Install

Works with every agent the skills CLI supports.

npx skills add https://github.com/openai/codex-security --skill finding-discovery
  1. 01

    Install

    Run the command in your project. The CLI asks which agents to install for.

  2. 02

    Describe the task

    Ask your agent for something this skill covers. It loads finding-discovery automatically when relevant.

  3. 03

    Review the result

    The agent follows the SKILL.md steps, scripts and references bundled with the skill.

SKILL.md

The instructions your agent reads when the skill is triggered.

Security Finding Discovery

Identify technically plausible security vulnerabilities in the authorized source, using the threat model and applicable security policy. Discovery preserves evidence for later validation; it does not decide final severity.

Read ../../references/artifact-storage.md before saving output and ../../references/security-guidance.md before source review. Resolve applicable policy for each reviewed path, including delegated assignments. Honor explicit input and output paths; ask for an explicitly required input only when its absence prevents the requested analysis.

Select the Workflow

Standard and Deep workers use their coordinator's self-contained prompts rather than this phase skill.

  • Running compact diff: read the supplied inventory through list_codex_security_review_items, review every assigned changed file, and record all candidates once with record_codex_security_discovery_candidates. Do not create ranked worklists, per-finding ledgers, receipts, or discovery reports.
  • Standalone diff without a compact inventory: use ../security-scan/references/scan-artifacts-and-ledger.md for the changed-file worklist and retained phase artifacts.
  • Standalone repository discovery: apply the evidence criteria below to the authorized source and return the candidates and coverage limitations. Do not start a scan or run the diff worklist pipeline.

For a diff, inspect the actual changed behavior, including deleted files at the baseline revision. Follow supporting code far enough to understand the affected boundary; expand to sibling callers when a changed shared control affects them. Do not broaden into an unrelated repository audit.

Evidence Criteria

  • Trace attacker-controlled input or a privileged operation through the closest relevant control to the sink and security impact. Explain why the control is absent, bypassable, or applied to the wrong value or boundary.
  • Check runtime reachability and deployment assumptions. Record effective guards and containment as counterevidence; a safe sibling implementation does not establish that the candidate path is safe.
  • Preserve the root-control location as well as entrypoints, wrappers, concrete implementations, and sinks needed to understand the claim. When the claim covers several implementations, inspect those implementations rather than asserting that all are affected.
  • Keep independently reachable vulnerable instances addressable. Merge duplicate observations of the same source/control/sink/impact instead of splitting cosmetic variants; retain all affected locations and original candidate IDs when merging.
  • Follow explicit user or advisory seeds to their exact source and control. Record the evidence that supports, disproves, or leaves the seed unresolved; a nearby issue in the same family does not close it. Use only authorized source material and do not fetch advisory links without permission.
  • Distinguish concrete security impact from maintainability or robustness concerns. A crash, dangerous API, optional feature, or missing check is a lead, not a finding by itself. Carry plausible candidates with explicit proof gaps to validation rather than inventing evidence or discarding them for lack of a runnable reproduction.

Divide independent review assignments among available workers when useful, with the target, scope, relevant policy/model, and required result fields. The parent reviews unassigned work and reconciles duplicates. Do not repeat validation or attack-path phases inside discovery.

Candidate Output

For each candidate, retain:

  • Stable candidate ID and title; preserve supplied seed or ledger IDs and advisory references.
  • Affected file/line locations, labeling entrypoint/wrapper, root_control, sink, and concrete_implementation where useful.
  • Attacker-controlled source, closest control, sink or broken control, plausible impact, and the source evidence connecting them.
  • Counterevidence, deployment preconditions, unresolved questions, and what validation needs to establish.
  • CWE IDs when known, otherwise an empty list.
  • For repository or scoped-path instances, an instance key <family>:<file>:<line> when required by the caller.
  • For diff findings, relevant_lines only where relevant buggy behavior overlaps the diff.

Use the caller's structured candidate contract. For a legacy diff, save the discovery report and discovery receipts at the paths in ../../references/scan-artifacts.md; subsequent phases own their own evidence and receipts. Finish when assigned source review and concrete follow-ups are accounted for, recording unfinished review honestly. If no plausible candidates remain, return that result with the actual reviewed scope and limitations.

FAQ

What does the finding-discovery skill do?

Use when Codex is already in the finding-discovery phase of a security scan or the user explicitly asks to discover candidate security findings in a repository or code change. Do not use as the primary trigger for full PR, commit, branch, patch, or repository scans.

How do I install finding-discovery?

Run npx skills add https://github.com/openai/codex-security --skill finding-discovery in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/finding-discovery/ (personal) or .claude/skills/finding-discovery/ (project).

Which AI agents can use finding-discovery?

Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.

Is finding-discovery safe to install?

No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.

Who maintains finding-discovery?

It is published by openai in openai/codex-security. It has 69 recorded installs.

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