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 agentmods add instructions/jcc-ne/mcp-skill-server/claude-mdgit clone --depth 1 https://github.com/jcc-ne/mcp-skill-serverWrote 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/instructions/jcc-ne/mcp-skill-server/claude-md)<a href="https://agentmods.dev/instructions/jcc-ne/mcp-skill-server/claude-md"><img src="https://agentmods.dev/badge/instructions/jcc-ne/mcp-skill-server/claude-md.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 | $0.00710 | $0.00710 |
| Opus 5 | $0.00355 | $0.00355 |
| Sonnet 5 | $0.00142 | $0.00142 |
| Haiku 4.5 | $0.00071 | $0.00071 |
Grade A, and why
mcp-skill-server CLAUDE.md 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 5d 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 — 87 lines — stays where its author put it; the contents beside it link to each section on GitHub.
CLAUDE.md
This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository.
Project Overview
MCP Skill Server is a local MCP (Model Context Protocol) server for developing and testing skills before deploying them to production agents. It allows rapid iteration on skills locally instead of the deploy → test → iterate cycle.
Commands
# Install dependencies
uv sync --dev
# Run tests
uv run pytest
# Run server with example skills
uv run mcp-skill-server examples/
# Run server with custom skills directory
uv run mcp-skill-server /path/to/skills
# Initialize a new skill (standalone command)
uv run mcp-skill-init ./path/to/skill -n "skill-name" -d "Description"
# Or use as subcommand
uv run mcp-skill-server init ./path/to/skill
# Validate a skill (standalone command)
uv run mcp-skill-validate ./path/to/skill
# Or use as subcommand
uv run mcp-skill-server validate ./path/to/skill
# Install with GCS output handler support
uv sync --extra gcs
Architecture
Core Components
-
server.py: MCP server entry point. Exposes four MCP tools:list_skills,get_skill,run_skill,refresh_skills. Uses stdio transport. -
loader.py: Skill discovery and schema inference. Scans for*/SKILL.mdfiles, parses YAML frontmatter, and dynamically discovers commands/parameters by running--helpon each skill's entry command. -
executor.py: Runs skill commands as subprocesses. Detects output files inoutput/directory or viaOUTPUT_FILE:prefix in stdout. -
models.py: Pydantic request/response models.
Plugin System
Two plugin types in plugins/:
Output Handlers - Process files generated by skills:
OutputHandler(base class): Abstract interface for processing output filesLocalOutputHandler: Default handler, just tracks local file pathsGCSOutputHandler: Optional, uploads to Google Cloud Storage (requiresgcsextra)
Response Formatters - Customize MCP tool response formatting:
ResponseFormatter(base class): Abstract interface for formatting execution resultsDefaultResponseFormatter: Default text-based formatter
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.
- 5d ago First seen · 87 lines · 710 tokens per session scan A 99df40e87e52
mcp-skill-server CLAUDE.md is an instructions file published in the GitHub repository jcc-ne/mcp-skill-server (1 stars, last pushed 3mo ago), licensed MIT. It adds 710 tokens to every session, about $0.0036 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 2026-08-31.
Other instructions, from other repositories
vscode buildNext.instructions.md
Working notes and architecture documentation for the new esbuild-based build system in build/next. Use when making changes to the new build pipeline (transpile/bundle commands, NLS plugin, source-map handling, resource copying, or self-hosting watch tasks).
codex AGENTS.md
AGENTS.md instructions for openai/codex, covering rust/codex-rs, the codex-core crate, code review rules, crate api surface and model visible context.
vscode oss-third-party-notices.instructions.md
Instructions for microsoft/vscode, covering vs code oss third-party-notices pipeline, architecture, pipeline flow in ci, applying the notice (cutover) and fallback chain (never fail the build).
spec-kit AGENTS.md
AGENTS.md instructions for github/spec-kit, covering agents.md, about spec kit and specify, quickstart — add a new integration in 5 steps, integration architecture and integrationmanifest — file tracking.
next.js AGENTS.md
AGENTS.md instructions for vercel/next.js, covering next.js development guide, codebase structure, monorepo overview, core package: packages/next and other important packages.
langchain AGENTS.md
AGENTS.md instructions for langchain-ai/langchain, covering global development guidelines for the langchain monorepo, corridor security analysis, project architecture and context, monorepo structure and development tools & commands.