Borrowing it
Nothing to install: this file belongs to deonmenezes/mantishack. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/deonmenezes/mantishack/main/.codex/skills/codex-bug/SKILL.mdgit clone --depth 1 https://github.com/deonmenezes/mantishackWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/deonmenezes/mantishack/codex-bug)<a href="https://agentmods.dev/skills/deonmenezes/mantishack/codex-bug"><img src="https://agentmods.dev/badge/skills/deonmenezes/mantishack/codex-bug/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/deonmenezes/mantishack/codex-bug"><img src="https://agentmods.dev/badge/skills/deonmenezes/mantishack/codex-bug.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 2 findings, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Data Exfiltration · line 23 Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.Fix: Verify the destination URL is trusted and necessary. Remove or replace with documented APIs. Ensure no secrets, tokens, or PII are transmitted.
- medium Data Exfiltration · line 24 Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.Fix: Verify the destination URL is trusted and necessary. Remove or replace with documented APIs. Ensure no secrets, tokens, or PII are transmitted.
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00066 | $0.00562 |
| Opus 5.5 | $0.00026 | $0.00225 |
| Sonnet 5.5 | $0.00013 | $0.00112 |
| Haiku 4.5 | $0.00007 | $0.00056 |
Grade A, and why
codex-bug scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
Copies of this mod
3 near-identical copies found in the catalogue:
- codex-bug — 100% identical, 0 lines differ
- codex-bug — 100% identical, 0 lines differ
- onequery-bug — 89% identical, 14 lines differ
How it starts
The opening of the file, as written. The whole thing — 49 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Codex Bug
Overview
Diagnose a Codex GitHub bug report and decide the next action: verify against sources, request more info, or explain why it is not a bug.
Workflow
- Confirm the input
- Require a GitHub issue URL that points to
github.com/openai/codex/issues/…. - If the URL is missing or not in the right repo, ask the user for the correct link.
- Network access
- Always access the issue over the network immediately, even if you think access is blocked or unavailable.
- Prefer the GitHub API over HTML pages because the HTML is noisy:
- Issue:
https://api.github.com/repos/openai/codex/issues/<number> - Comments:
https://api.github.com/repos/openai/codex/issues/<number>/comments
- Issue:
- If the environment requires explicit approval, request it on demand via the tool and continue without additional user prompting.
- Only if the network attempt fails after requesting approval, explain what you can do offline (e.g., draft a response template) and ask how to proceed.
- Read the issue
- Use the GitHub API responses (issue + comments) as the source of truth rather than scraping the HTML issue page.
- Extract: title, body, repro steps, expected vs actual, environment, logs, and any attachments.
- Note whether the report already includes logs or session details.
- If the report includes a thread ID, mention it in the summary and use it to look up the logs and session details if you have access to them.
- Summarize the bug before investigating
- Before inspecting code, docs, or logs in depth, write a short summary of the report in your own words.
- Include the reported behavior, expected behavior, repro steps, environment, and what evidence is already attached or missing.
- Decide the course of action
- Verify with sources when the report is specific and likely reproducible. Inspect relevant Codex files (or mention the files to inspect if access is unavailable).
- Request more information when the report is vague, missing repro steps, or lacks logs/environment.
- Explain not a bug when the report contradicts current behavior or documented constraints (cite the evidence from the issue and any local sources you checked).
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago First seen · 49 lines · 66 tokens per session scan A cfdaae2defa5
codex-bug is a skill published in the GitHub repository deonmenezes/mantishack (504 stars, last pushed 18d ago), licensed Apache-2.0. It adds 66 tokens to every session and 562 once invoked, about $0.0003 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
diag-harness
Kernel-version skew check (ADR-027). Reports manifest surface + manifest kernel + installed kernel + verdict (match/patch-diff/minor-diff/major-diff). Exits 1 on minor/major skew with a copy-pasteable npm install @metaharness/[email protected] next step. Exits 2 if no .harness/manifest.json at path.
publish-harness
Publish a generated harness to npm — runs the smoke test, signs the witness manifest, and dispatches npm publish --provenance from your tagged release.
resolving-merge-conflicts
Use when a git merge or rebase reports conflicts and the operation is in progress.
git-investigate
Git history investigation. Use when: tracking code changes, finding where bugs were introduced, root cause analysis. Not for: code exploration (use code-explore), issue analysis (use issue-analyze). Output: history trace + root cause report.
C31-debug
Investigate bugs through reproduction → trace → hypothesis → test-first fix → verify → archive. All active sessions are tracked in memory/.planning/debug/ for resumption and knowledge compounding.
C31-research
Unified research skill. Covers best practices, framework documentation, git history, community issues, institutional memory, and bug reproduction. All modes are read-only — does not write code.