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 smallnest/pigo --skill code-to-specgit clone --depth 1 https://github.com/smallnest/pigoWrote 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/smallnest/pigo/code-to-spec)<a href="https://agentmods.dev/skills/smallnest/pigo/code-to-spec"><img src="https://agentmods.dev/badge/skills/smallnest/pigo/code-to-spec/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/smallnest/pigo/code-to-spec"><img src="https://agentmods.dev/badge/skills/smallnest/pigo/code-to-spec.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 | $0.00081 | $0.02398 |
| Opus 5.5 | $0.00032 | $0.00959 |
| Sonnet 5.5 | $0.00016 | $0.00480 |
| Haiku 4.5 | $0.00008 | $0.00240 |
Grade A, and why
code-to-spec 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 1mo 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- code-to-spec — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 342 lines — stays where its author put it; the contents beside it link to each section on GitHub.
to-spec — Reverse-Engineer Project Specification
Analyze an existing codebase and produce a structured SPEC document that captures what the project does, how it's built, and what contracts it exposes. The output is a living specification that could be used to rebuild the project from scratch or onboard new contributors.
When to Use
- You want a comprehensive understanding of an existing project
- Onboarding new team members who need a high-level overview
- Documenting a project that was built without a spec
- Comparing actual implementation against intended design
- Preparing for a rewrite or major refactor
- Auditing what a project actually does vs. what people think it does
The Job
- Scope confirmation — ask user what to analyze (entire repo, specific directory, or specific aspect)
- Deep scan — systematically read project structure, entry points, config, tests, and core logic
- Synthesize — produce a structured SPEC document
- Review — present to user for feedback and iteration
- Save — write final SPEC to agreed location
Step 1: Scope Confirmation
Before scanning, ask the user:
What should I analyze?
A. Entire repository (recommended for small-medium projects)
B. Specific directory or module: [path]
C. Specific aspect only (e.g., API surface, data model, auth flow)
Depth level:
1. Overview — high-level architecture + tech stack + key features (fast, ~5 min)
2. Standard — includes API contracts, data models, config, dependencies (default)
3. Deep — adds internal module interactions, error handling patterns, test coverage analysis
If the project is large (>500 files), recommend starting with Overview or a specific module.
Step 2: Deep Scan
Systematically analyze the following (adapt to what exists):
2.1 Project Identity
package.json,go.mod,Cargo.toml,pyproject.toml,pom.xml, etc.- README, LICENSE
- Git history (first commit date, recent activity, contributor count)
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.
- 1mo ago First seen · 342 lines · 81 tokens per session scan A 523fd108b00d
code-to-spec is a skill published in the GitHub repository smallnest/pigo (483 stars, last pushed 1mo ago), licensed MIT. It adds 81 tokens to every session and 2,398 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 2026-08-30.
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