Borrowing it
Nothing to install: this file belongs to snoozelieb/coach-mcp. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/snoozelieb/coach-mcp/main/CLAUDE.mdgit clone --depth 1 https://github.com/snoozelieb/coach-mcpWrote 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/snoozelieb/coach-mcp/claude-md)<a href="https://agentmods.dev/instructions/snoozelieb/coach-mcp/claude-md"><img src="https://agentmods.dev/badge/instructions/snoozelieb/coach-mcp/claude-md/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/instructions/snoozelieb/coach-mcp/claude-md"><img src="https://agentmods.dev/badge/instructions/snoozelieb/coach-mcp/claude-md.svg" alt="Reviewed on agentmods" width="80" 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 | $0.02567 | $0.02567 |
| Opus 5 | $0.01283 | $0.01283 |
| Sonnet 5 | $0.00513 | $0.00513 |
| Haiku 4.5 | $0.00257 | $0.00257 |
Grade A, and why
coach-mcp 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 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 — 192 lines — stays where its author put it; the contents beside it link to each section on GitHub.
CLAUDE.md
Development context for Claude Code (claude.ai/code) sessions in this repository.
Coaching doctrine lives in the server, not in this file. Every MCP client — Claude Code included — receives the coaching identity at connection time via
SERVER_INSTRUCTIONS(coach/mcp_app.py; test-enforced under 2,000 chars because Claude Code truncates MCP instructions at 2KB) and the long-formcoach://coaching/doctrineresource (uncapped, fetched on demand; completeness pinned bytests/test_server_instructions.py). When coaching from this repo, follow those surfaces — snapshot first, injury hard gate, verify athlete claims — and read the doctrine resource before planning sessions. Do not copy doctrine into this file: the duplicate is the copy that drifts.
Project Overview
An adaptive AI training coach MCP server: fetches fitness data from Garmin
Connect, maintains a rolling 7-day plan with a PURPOSE for every session, persists
coaching memory (decisions, anomalies, approvals) across sessions, and pushes
workouts to the Garmin calendar. Published as garmin-coach-mcp on PyPI and
io.github.snoozelieb/coach-mcp on the MCP registry.
The safety-critical coaching rules are code-enforced, not prompt-enforced:
update_weekly_plan rejects non-rest sessions without a purpose (purpose gate)
and sessions matching an active/improving injury's restricted_activities (injury
gate — taxonomy-aware, re-run on the full plan at push); plans entirely in the past
or with days >21 in the future are rejected; anomalies and season auto-proposals
are idempotent by stable id / event-tag, so rejections are remembered forever.
See coach/tools/planning_tools.py and coach/tools/decision_tools.py.
Removed / consolidated tools
Removed in the first rationalization pass — their data is in get_coaching_snapshot():
get_planning_context, get_goal_progress (→ snapshot.goal_progress),
list_pending_suggestions (→ snapshot.coaching_memory.pending_approvals),
get_load_status (→ snapshot.fitness_metrics.acwr_status).
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 · 192 lines · 2,567 tokens per session scan A 0e6ecb611bc7
coach-mcp CLAUDE.md is an instructions file published in the GitHub repository snoozelieb/coach-mcp (2 stars, last pushed 13d ago), licensed MIT. It adds 2,567 tokens to every session, about $0.0128 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.
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