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 shinpr/codex-workflows --skill recipe-reverse-engineergit clone --depth 1 https://github.com/shinpr/codex-workflowsWrote 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/shinpr/codex-workflows/recipe-reverse-engineer)<a href="https://agentmods.dev/skills/shinpr/codex-workflows/recipe-reverse-engineer"><img src="https://agentmods.dev/badge/skills/shinpr/codex-workflows/recipe-reverse-engineer/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/shinpr/codex-workflows/recipe-reverse-engineer"><img src="https://agentmods.dev/badge/skills/shinpr/codex-workflows/recipe-reverse-engineer.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00026 | $0.02975 |
| Opus 5.5 | $0.00010 | $0.01190 |
| Sonnet 5.5 | $0.00005 | $0.00595 |
| Haiku 4.5 | $0.00003 | $0.00298 |
Grade A, and why
recipe-reverse-engineer 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 16d 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 — 232 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Required Skills [LOAD BEFORE EXECUTION]
- [LOAD IF NOT ACTIVE]
documentation-criteria— document creation rules and templates - [LOAD IF NOT ACTIVE]
ai-development-guide— evidence and completeness discipline - [LOAD IF NOT ACTIVE]
subagents-orchestration-guide— agent coordination and review resolution - [LOAD IF NOT ACTIVE]
llm-friendly-context— generated document handoffs
Invocation rule: Start each named agent with spawn_agent and fork_turns="none". When invoking the same named agent again within this flow, use followup_task on its existing instance with the inputs specified for the current invocation.
Context: Reverse engineering workflow to create documentation from existing code
Target: $ARGUMENTS
Orchestrator Definition
Core Identity: Coordinate reverse engineering, perform lightweight artifact routing directly, and invoke specialists for discovery, document generation, and semantic review.
Execution Protocol:
- Invoke the named specialists for discovery, generation, and semantic review; perform artifact selection, routing, deterministic transformations, and status updates directly
- Process one step at a time: Execute steps sequentially within each unit (2 -> 3 -> 4 -> 5). Each step's output is the required input for the next step. Complete all steps for one unit before starting the next
- Pass
$STEP_N_OUTPUTas-is to sub-agents -- the orchestrator bridges data without processing or filtering it, except for steps that explicitly define a deterministic transformation with an input schema, output schema, and mapping rules
Execution Plan: Reuse the active execution plan. When the workflow has multiple dependent actions and no plan exists, create one that tracks them through final verification.
Step 0: Initial Configuration
0.1 Scope Confirmation
Resolve the target path and requested document depth from $ARGUMENTS. Ask only for either value that remains ambiguous. Use reference_architecture: none unless the user supplied an architecture hint. Enable human review when requested; otherwise continue autonomously.
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.
- 16d ago Changed ec622cf3a52e
- 1mo ago Changed · -15 lines b1d915f5358c
- 1mo ago First seen · 247 lines · 26 tokens per session scan A 4e3812a8b786
recipe-reverse-engineer is a skill published in the GitHub repository shinpr/codex-workflows (39 stars, last pushed 2d ago), licensed MIT. It adds 26 tokens to every session and 2,975 once invoked, about $0.0001 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.
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