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 EliasOulkadi/shokunin --skill initgit clone --depth 1 https://github.com/EliasOulkadi/shokuninWrote 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/eliasoulkadi/shokunin/init)<a href="https://agentmods.dev/skills/eliasoulkadi/shokunin/init"><img src="https://agentmods.dev/badge/skills/eliasoulkadi/shokunin/init/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/eliasoulkadi/shokunin/init"><img src="https://agentmods.dev/badge/skills/eliasoulkadi/shokunin/init.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Excessive Agency · line 115 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
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 | $0.00032 | $0.01868 |
| Opus 5 | $0.00016 | $0.00934 |
| Sonnet 5 | $0.00006 | $0.00374 |
| Haiku 4.5 | $0.00003 | $0.00187 |
Grade A, and why
init 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 9d 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 — 194 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AGENTS.md Generator
Analyze a codebase and generate a concise, accurate AGENTS.md contributor guide.
Target: the current working directory, unless user provided another folder as input.
Gather Information
Collect facts about the repository. Only record what is actually found — never invent information.
These probes are independent — run them in parallel (e.g., dispatch subagents) when the tooling supports it.
Repository structure
Map the repo structure (3 levels deep, excluding .git, node_modules, dist, build, pycache, .venv and other autogenerated folders). This is analysis input — the output AGENTS.md should describe non-obvious architecture, not list directories. Focus on the "big picture" that requires reading multiple files to understand.
Build & dev commands
Extract actual command definitions from the project's build system: package.json scripts, Makefile targets, pyproject.toml scripts, Cargo.toml bins/workspace members, go.mod module path, docker-compose.yml services, etc.
Coding conventions
Check for and record key settings of:
- Linter configs
- Formatter configs
- Type checking (note strict mode if applicable)
- Agent rules:
.cursorrules,.cursor/rules/,.github/copilot-instructions.md - Pre-commit hooks
If agent rules files exist, read and extract the important parts — focus on conventions that matter for code generation, not verbatim content.
Git history
Review the last 20 commits to identify commit message conventions and patterns. If this fails (not a git repo or shallow clone), note the limitation and skip.
Existing documentation
- If
README.mdexists, read it for context about the repo's purpose and setup. - If
AGENTS.mdalready exists, read it — you'll be improving it rather than starting fresh. - If
CLAUDE.mdexists, read it for additional context.
Generate AGENTS.md
Document requirements
- Title:
# Repository Guidelines - 200-400 words (exceed only if complexity genuinely demands it)
- Direct, instructional tone
- No repetition across sections
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.
- 9d ago First seen · 194 lines · 32 tokens per session scan A 4a7dc66ee852
init is a skill published in the GitHub repository EliasOulkadi/shokunin (113 stars, last pushed 1mo ago), licensed MIT. It adds 32 tokens to every session and 1,868 once invoked, about $0.0002 per session on Opus 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.
Other skills, from other repositories
memcord
Privacy-first, self-hosted chat memory for OpenClaw — save and recall conversation history across sessions without any cloud dependency.
python-docs
Create, update, and sync Python project documentation from source code. Use when asked to document a module, generate API references, create architecture diagrams with Mermaid, update docs after code changes, or keep documentation in sync with source. Triggers include "document", "write docs", "update docs", "sync…
code-refactor
Refactor Python code to match this repo's coding standards and style. Use when the user asks to "refactor", "clean up", "fix coding standards", or "apply best practices" to a module, file, or directory in this repo. Applies python-best-practices, pydantic v2, pytorch-lightning, and python-docs conventions as relevant…
quality
PALADIN quality gatekeeper. Two-stage system: Stage 1 checks spec compliance (pass/fail, blocks everything on failure). Stage 2 scores project health 0-100 across 6 tiers. Issues verdict: SHIP IT / SHIP WITH CAUTION / NOT READY / BLOCKED. Evidence Before Claims: every finding must cite the file, line, and what was…
refresh
Use when an existing contextualizer's references may have drifted from current upstream state — typically weekly, or whenever a few days of upstream changes have accumulated — to bring them back into agreement.
distill-habits
Review recent Claude Code conversations, distill the user's stable habits and preferences, persist them into a long-term habit ledger, and maintain a "My Habits" block in the global CLAUDE.md within a character budget. The more you use Claude Code, the more it knows you. Trigger manually with /distill-habits, or on a…