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 connorch/skills --skill codex-implementationgit clone --depth 1 https://github.com/connorch/skillsWrote 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/connorch/skills/codex-implementation)<a href="https://agentmods.dev/skills/connorch/skills/codex-implementation"><img src="https://agentmods.dev/badge/skills/connorch/skills/codex-implementation/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/connorch/skills/codex-implementation"><img src="https://agentmods.dev/badge/skills/connorch/skills/codex-implementation.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00085 | $0.00999 |
| Opus 5.5 | $0.00034 | $0.00400 |
| Sonnet 5.5 | $0.00017 | $0.00200 |
| Haiku 4.5 | $0.00009 | $0.00100 |
Grade A, and why
codex-implementation 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 13d 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.
How it starts
The opening of the file, as written. The whole thing — 102 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Codex Implementation
Use Codex as a separate implementation agent for bounded code changes. Claude remains responsible for scoping the task, reviewing the diff, running or checking verification, and explaining the final result.
Use this when the user asks for Codex or delegation, or when a bounded task would benefit from a parallel implementation agent producing a patch. Do not let Codex commit, push, deploy, or edit global config unless the user explicitly asked for that.
Workflow
- Pin the current state with
git status --shortand note any user changes already present. - Define the implementation scope: files or behavior to change, files to avoid, constraints, and verification commands.
- Create a temporary artifact directory for Codex's report.
- Run
codex execwith repo write access. - Confirm
run.loghas content shortly after launch. - After Codex exits, inspect
git statusandgit diff. - Run the cheapest reliable verification yourself when practical.
- Report what Codex changed, what Claude verified, and any remaining risks.
Use this command shape:
ARTIFACT_DIR="$(mktemp -d "${TMPDIR:-/tmp}/codex-implementation.XXXXXX")"
REPORT="$ARTIFACT_DIR/report.md"
PROMPT="$ARTIFACT_DIR/prompt.md"
# Write a self-contained prompt to $PROMPT, then run:
codex exec \
-C "$PWD" \
--add-dir "$ARTIFACT_DIR" \
-s workspace-write \
-o "$REPORT" \
"$(cat "$PROMPT")" </dev/null >"$ARTIFACT_DIR/run.log" 2>&1
Use -s workspace-write by default. Use -s danger-full-access only when the implementation truly needs access outside the repo, app launch automation, simulator work, package-manager global state, or other machine-level operations.
If codex is not installed or the command fails, report the error and offer to implement the change directly instead.
Running in the background
Runs commonly exceed the Bash tool's 10-minute timeout. Either pass an explicit long timeout or run in the background.
- Always close stdin with
</dev/null, backgrounded or not. Codex otherwise blocks waiting on a terminal that never answers and the run hangs silently. - Shortly after launch, check once that
run.loghas content. This catches a bad launch (missing binary, rejected flag) without waiting for the full run. After that, rely on the process exit notification; no polling. - The report file at
$REPORTis the completion signal. Do not read the diff until it exists.
What ships with it
1 file 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.
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.
- 13d ago Changed · -22 tokens per session 53dee74074f9
- 23d ago Changed · +2 lines 4203b33402fb
- 1mo ago Changed · +9 lines e6a4d009c2f1
- 1mo ago First seen · 91 lines · 107 tokens per session scan A db3ca69ed9b6
codex-implementation is a skill published in the GitHub repository connorch/skills (2 stars, last pushed today), licensed MIT. It adds 85 tokens to every session and 999 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 2026-08-31.
Other skills, from other repositories
local-ai-agents
Build local-first AI agents that run entirely on a developer workstation with Microsoft Foundry Local and Qwen function-calling models. Covers Small Language Models (SLMs), the OpenAI-compatible local endpoint, sandboxed local tools, local RAG with Chroma, local MCP servers, hybrid cloud/local routing, and the…
next-cache-components-adoption
Turn on Cache Components in a Next.js app and resolve the blocking routes it surfaces. Use when the user wants to enable, adopt, or migrate to Cache Components, flip the cacheComponents flag, work through a flood of blocking-prerender / instant validation errors, run the cache-components-instant-false codemod, or…
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…
insight-error-page
Write or audit an insight-kind error page for the Next.js dev overlay. Use when creating a new errors/ .mdx page, auditing an existing one, or checking that a page matches the framework fix cards. Covers page structure, title alignment, FixCard cards with Copy prompt button, code snippets, terminology verification…
next-cache-components-optimizer
Optimize the meaningful UI available immediately from a Next.js route on an initial load (hard navigation) or named client-side navigation (soft navigation). Encode the goal as a failing @next/playwright instant() e2e and work it to green, one verified route and entry point at a time; the shipped test then guards…