Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add AnastasiyaW/codex-claude-code-config --skill bug-reproducergit clone --depth 1 https://github.com/AnastasiyaW/codex-claude-code-configWrote 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/anastasiyaw/codex-claude-code-config/bug-reproducer)<a href="https://agentmods.dev/skills/anastasiyaw/codex-claude-code-config/bug-reproducer"><img src="https://agentmods.dev/badge/skills/anastasiyaw/codex-claude-code-config/bug-reproducer/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/anastasiyaw/codex-claude-code-config/bug-reproducer"><img src="https://agentmods.dev/badge/skills/anastasiyaw/codex-claude-code-config/bug-reproducer.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, 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 Excessive Agency · line 12 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
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 | $0.00147 | $0.03280 |
| Opus 5 | $0.00073 | $0.01640 |
| Sonnet 5 | $0.00029 | $0.00656 |
| Haiku 4.5 | $0.00015 | $0.00328 |
Grade A, and why
bug-reproducer 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 yesterday.
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.
How it starts
The opening of the file, as written. The whole thing — 241 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Bug Reproducer
Turn a codebase or bug report into evidence. When no bug is supplied, discover concrete candidates from the code and its contracts, then try to prove the strongest candidates with focused tests. Never present a suspicion as a confirmed bug.
Honor existing authority, then apply missing gates
Invocation alone to hunt for unknown bugs is inspection-only. A direct request to fix or implement a reported bug, or to find and fix bugs in a named repository, authorizes the normal reversible reproduction tests and causal production edits inside that stated task. A direct request to reproduce a reported bug or create its regression test likewise authorizes the normal reversible reproduction files and commands, but not a production repair. Record the matching task scope and proceed without stopping for duplicate confirmation, even when the exact responsible file is learned during diagnosis. Do not ask the user to approve the same scope twice.
If the user asked only to inspect, audit, find, or isolate, do not infer permission to create reproduction files or change production code. A direct reproduction or regression-test request authorizes only the normal reversible reproduction work described above; it does not authorize a production repair. Before a gate that has not been satisfied, do not create or edit project files, install dependencies, run formatters or migrations, modify configuration, generate reports, create worktrees, or execute commands likely to mutate project state. Read source, configuration, documentation, existing tests, user-supplied logs, and Git history. Run an existing targeted test only when it is clearly safe and does not require project changes.
Gate 1 — test the candidate
When the current request has not explicitly authorized creating and running a reproducer, present and stop at:
## Bug candidates
| # | Candidate | Contract evidence | Trigger | Location | Confidence |
|---|---|---|---|---|---|
| 1 | ... | ... | ... | file:line | high/medium |
## Proposed bug test
- Candidate(s) to test:
- Why each could be a real bug:
- Exact files to create or edit:
- Minimal fixture/input:
- Test or harness command:
- Signal that will confirm each bug:
- Main risk or uncertainty:
No project files have been modified. Do you want me to create and run these tests?
What ships with it
10 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- agents/openai.yaml 388 B
- ATTRIBUTION.md 958 B
- LICENSE-upstream 1.0 KB
- references/bug-discovery-playbook.md 3.6 KB
- references/report-schema.md 2.3 KB
- references/reproduction-playbook.md 2.6 KB
- scripts/capture_command.py 3.9 KB runs code
- scripts/compare_evidence.py 5.7 KB runs code
- scripts/generate_report.py 9.0 KB runs code
- scripts/test_compare_evidence.py 8.2 KB runs code
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.
- yesterday Changed · +10 lines 11792325ceef
- 5d ago First seen · 231 lines · 147 tokens per session scan A 4741d5f6951e
bug-reproducer is a skill published in the GitHub repository AnastasiyaW/codex-claude-code-config (149 stars, last pushed 2d ago), licensed MIT. It adds 147 tokens to every session and 3,280 once invoked, about $0.0007 per session on Opus 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 2026-09-05.
Other skills, from other repositories
code-review
Reviews code for bugs, security issues, and best practices.
eval-harness
Professional Eval Harness Expert skill. Integrate LLM API workflows, safe system prompt guidelines, and agentic workflows.
systematic-debugging
4-phase root cause debugging: understand bugs before fixing.
fix-failing-tests
Diagnose a failing test in the googleapis/mcp-toolbox repo and land a fix by reasoning from the actual error: read the failure, reproduce it, shrink it until the cause is forced into the open, then fix the cause. Use this whenever a test or CI job is red, a build breaks after a change, many packages fail at once, or a…
promptfoo-redteam-setup
Create or refine promptfoo redteam setup configs: purpose, targets, plugins, strategies, frameworks, multi-input target inputs, policy text, grader guidance, contexts, and static-code-derived target/threat mapping. Use when preparing a red team scan plan from live probes, code evidence, or provider configs, or when…
promptfoo-provider-setup
Configure promptfoo providers or redteam targets for hosted models, live HTTP APIs, Python/JavaScript local scripts, agent SDKs, or multi-input systems. Use when connecting promptfoo to the system under test, mapping vars, auth env vars, request bodies, response transforms, or static-code-derived provider wrappers. Do…