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 agentmods add skills/lagz0ne/c3-skill/codexnpx skills add lagz0ne/c3-skill --skill codexgit clone --depth 1 https://github.com/lagz0ne/c3-skillWhat 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 | $0.00100 | $0.00626 |
| Opus 5 | $0.00050 | $0.00313 |
| Sonnet 5 | $0.00020 | $0.00125 |
| Haiku 4.5 | $0.00010 | $0.00063 |
Grade A, and why
codex 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 3d 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.
What it actually says
Codex — cross-model delegation
codex (OpenAI Codex CLI, model gpt-5.5, approval_policy = never so it never blocks
on prompts) is a second coding agent that reads this repo and reasons over it precisely
(verified). Consult it for a different model's perspective — it catches what a single
model misses.
When to consult Codex
- A well-scoped implementation chunk you want a second model to write, or to cross-check your own implementation against.
- An independent design/diff review — different model, different failure modes caught.
- Adversarial verification of a claim ("is X actually true in the shipped code?").
How — always non-interactive codex exec
| Goal | Command |
|---|---|
| Analyze / review (read-only, safe — the default) | codex exec --sandbox read-only "<tight prompt>" 2>/dev/null |
| Let Codex EDIT files | codex exec --sandbox workspace-write "<prompt>" 2>/dev/null |
| Independent review of the working tree | codex review |
| Apply Codex's last proposed diff | codex apply |
| Cheaper/deeper | add -c model_reasoning_effort=low|medium|high|xhigh |
| Capture output | append -o out.txt, or --json for structured events |
| Continue a session | codex exec resume --last "<follow-up>" |
Discipline (non-negotiable)
- Scope tightly + hand it concrete material — point Codex at exact
file:line, the locked contract (tasks/v11-locked-contract.md), or the spec. It works best on concrete input, not vague asks. 2>/dev/nullto suppress thinking tokens unless you're debugging the run.- Verify every workspace-write run with the repo's own gates —
go -C cli build ./...,go -C cli test ./...,c3local check. Codex's edits are a proposal, never ground truth; treat its diff like an untrusted PR. - For high-stakes work, run it twice across models — have Codex and a Claude subagent do the same task independently, then reconcile the diff. Cross-model disagreement is signal.
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.
- 3d ago First seen · 46 lines · 100 tokens per session scan A f3aa58a8ae9c
codex is a skill published in the GitHub repository lagz0ne/c3-skill (12 stars, last pushed 1mo ago), licensed MIT. It adds 100 tokens to every session and 626 once invoked, about $0.0005 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 2026-08-30.
Other skills, from other repositories
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
brainstorming
You MUST use this before any creative work - creating features, building components, adding functionality, or modifying behavior. Explores user intent, requirements and design before implementation.
auto-perf-optimize
Run agent-driven VS Code performance or memory investigations. Use when asked to launch Code OSS, automate a VS Code scenario, run the Chat memory smoke runner, capture renderer heap snapshots, take workflow screenshots, compare run summaries, or drive a repeatable scenario before heap-snapshot analysis.
chat-perf
Run chat perf benchmarks and memory leak checks against the local dev build or any published VS Code version. Use when investigating chat rendering regressions, validating perf-sensitive changes to chat UI, or checking for memory leaks in the chat response pipeline.
chat-pet-sprite-creation
Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.
cpu-profile-analysis
Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…