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/goal-workflow --skill code-to-specgit clone --depth 1 https://github.com/smallnest/goal-workflowWrote 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/goal-workflow/code-to-spec)<a href="https://agentmods.dev/skills/smallnest/goal-workflow/code-to-spec"><img src="https://agentmods.dev/badge/skills/smallnest/goal-workflow/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/goal-workflow/code-to-spec"><img src="https://agentmods.dev/badge/skills/smallnest/goal-workflow/code-to-spec.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk 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.
This is a copy
100% identical to code-to-spec — 0 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.
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/goal-workflow (293 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. It is 100% identical to code-to-spec, differing in 0 lines, and is treated as a copy.
Other skills, from other repositories
maintaining-agent-context
You MUST use this when creating, auditing, optimizing, or maintaining repository agent instructions - AGENTS.md, CLAUDE.md, .claude/rules/, and instruction-related SKILL.md content - covering context architecture, always-loaded context cost, stale, duplicated, or conflicting instructions, and keeping agent memory…
negafix
You MUST use this when writing or substantively editing prose in a project (docs, READMEs, marketing copy) and when asked to audit, score, or clean up negative parallelism, the "it's not just X, it's Y" construction. Not for ordinary factual negation.
debugging
You MUST use this when investigating bugs, regressions, failing tests, build or integration failures, flaky behavior, performance anomalies, or other unexpected technical behavior.
parallel-agents
You MUST use this when several units of work might run concurrently through agents - independent investigations, specialist reviews, repository analyses, or plan tasks that look unrelated - covering whether they are genuinely independent, which mutable state needs isolation, how wide the wave should be, and how…
review-resolution
You MUST use this when code-review findings, PR comments, CI review output, or reviewer suggestions need technical validation before any fix or disposition, including feedback from human reviewers, subagents, GitHub, static-analysis tools, or external review systems.
verification-gate
You MUST use this when work is about to be called complete, done, fixed, ready, or mergeable - before a completion claim, a merge or pull request, a branch finish, or a handoff report.