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 vsladkov/claudex-stereo --skill codex-promptinggit clone --depth 1 https://github.com/vsladkov/claudex-stereoWrote 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/vsladkov/claudex-stereo/codex-prompting)<a href="https://agentmods.dev/skills/vsladkov/claudex-stereo/codex-prompting"><img src="https://agentmods.dev/badge/skills/vsladkov/claudex-stereo/codex-prompting.svg" alt="Measured on agentmods" 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 | $0.00033 | $0.00764 |
| Opus 5 | $0.00016 | $0.00382 |
| Sonnet 5 | $0.00007 | $0.00153 |
| Haiku 4.5 | $0.00003 | $0.00076 |
Grade A, and why
codex-prompting 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 8d 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.
This is a copy
88% identical to gpt-5-4-prompting — 14 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
What it actually says
Codex CLI Model Prompting
Use this skill when stereo:codex-rescue needs to ask a Codex CLI model for help.
Prompt Codex like an operator, not a collaborator. Keep prompts compact and block-structured with XML tags. State the task, the output contract, the follow-through defaults, and the small set of extra constraints that matter.
Core rules:
- Prefer one clear task per Codex run. Split unrelated asks into separate runs.
- Tell Codex what done looks like. Do not assume it will infer the desired end state.
- Add explicit grounding and verification rules for any task where unsupported guesses would hurt quality.
- Prefer better prompt contracts over raising reasoning or adding long natural-language explanations.
- Use XML tags consistently so the prompt has stable internal structure.
Default prompt recipe:
<task>: the concrete job and the relevant repository or failure context.<structured_output_contract>or<compact_output_contract>: exact shape, ordering, and brevity requirements.<default_follow_through_policy>: what Codex should do by default instead of asking routine questions.<verification_loop>or<completeness_contract>: required for debugging, implementation, or risky fixes.<grounding_rules>or<citation_rules>: required for review, research, or anything that could drift into unsupported claims.
When to add blocks:
- Coding or debugging: add
completeness_contract,verification_loop, andmissing_context_gating. - Review or adversarial review: add
grounding_rules,structured_output_contract, anddig_deeper_nudge. - Research or recommendation tasks: add
research_modeandcitation_rules. - Write-capable tasks: add
action_safetyso Codex stays narrow and avoids unrelated refactors.
How to choose prompt shape:
- Use built-in
revieworadversarial-reviewcommands when the job is reviewing local git changes. Those prompts already carry the review contract. - Use
taskwhen the task is diagnosis, planning, research, or implementation and you need to control the prompt more directly. - Use
task --resume-lastfor follow-up instructions on the same Codex thread. Send only the delta instruction instead of restating the whole prompt unless the direction changed materially.
Working rules:
- Prefer explicit prompt contracts over vague nudges.
- Use stable XML tag names that match the block names from the reference file.
- Do not raise reasoning or complexity first. Tighten the prompt and verification rules before escalating.
- Ask Codex for brief, outcome-based progress updates only when the task is long-running or tool-heavy.
- Keep claims anchored to observed evidence. If something is a hypothesis, say so.
Prompt assembly checklist:
- Define the exact task and scope in
<task>. - Choose the smallest output contract that still makes the answer easy to use.
- Decide whether Codex should keep going by default or stop for missing high-risk details.
- Add verification, grounding, and safety tags only where the task needs them.
- Remove redundant instructions before sending the prompt.
Reusable blocks live in references/prompt-blocks.md. Concrete end-to-end templates live in references/codex-prompt-recipes.md. Common failure modes to avoid live in references/codex-prompt-antipatterns.md.
What ships with it
3 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.
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.
- 8d ago First seen · 61 lines · 33 tokens per session scan A cf20599d4c26
codex-prompting is a skill published in the GitHub repository vsladkov/claudex-stereo (3 stars, last pushed 3d ago), licensed Apache-2.0. It adds 33 tokens to every session and 764 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 88% identical to gpt-5-4-prompting, differing in 14 lines, and is treated as a copy.
Other skills, from other repositories
meta-prompting
Enhanced reasoning patterns via slash commands (/think, /verify, /adversarial, /edge, /compare, /confidence, /budget, /constrain, /json, /flip, /assumptions, /tensions, /analyze, /trade) or natural language ("argue against", "what could break", "show reasoning", "deep review", "meta-prompts", "thinking modes"…
review-prompt
Review LLM prompts against the prompt-engineering skill's principles — leading with where each line came from — and report the findings without modifying files. Use when reviewing prompt quality, auditing a prompt, evaluating a system prompt, or checking whether prompt issues are high-confidence and fixable.
goal-test
A local experiment for testing a goal command that keeps an AI coding session working until a stated condition is judged complete. It uses a separate language model to evaluate the conversation after each assistant turn.
refine-prompt
Transforms vague or rough prompts into precise, structured AI instructions. Use when asked to "refine prompt", "improve prompt", "make this prompt better", "promptify", "optimize prompt", "rewrite prompt", "enhance prompt", or "sharpen instructions".
gemini-prompting
Internal guidance for composing Gemini and AGY prompts for coding, review, diagnosis, and research tasks inside the Gemini Claude Code plugin.
refine
Crossover layer between the user's intent and LLM execution: takes a raw prompt (terse, frustrated, ambiguous, or shorthand), resolves every vague reference to a concrete artifact, recalls past decisions and lessons, and compiles a verifiable execution contract BEFORE any code is touched. Invoke explicitly as $refine…