Borrowing it
Nothing to install: this file belongs to axelfreeman/gymcamanalytics. 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/axelfreeman/gymcamanalytics/main/AGENTS.mdgit clone --depth 1 https://github.com/axelfreeman/gymcamanalyticsWrote 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/axelfreeman/gymcamanalytics/agents-md)<a href="https://agentmods.dev/instructions/axelfreeman/gymcamanalytics/agents-md"><img src="https://agentmods.dev/badge/instructions/axelfreeman/gymcamanalytics/agents-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 | $0.00326 | $0.00326 |
| Opus 5 | $0.00163 | $0.00163 |
| Sonnet 5 | $0.00065 | $0.00065 |
| Haiku 4.5 | $0.00033 | $0.00033 |
Grade A, and why
gymcamanalytics AGENTS.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 6d 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.
What it actually says
AGENTS.md — GymCam Analytics
What this repo is
GymCam Analytics is an MCP (Model Context Protocol) server that turns a gym's existing cameras + class schedule into attendance and trainer-performance analytics. Python + FastMCP, distributed via uvx --from git+....
Project layout
src/gymcam/server.py— the FastMCP server and all toolspyproject.toml— hatchling build, entry pointgymcam = gymcam.server:mainSKILL.md— agent skill describing how to use the toolsllms.txt— agent-readable product facts for citation
Critical constraints
- Pin
mcp>=1.2.0,<2.0.0.mcp 2.0.0removedmcp.server.fastmcpand breaks the import. - The API-key gate lives in
_gate()— every tool must call it first. No key → onboarding text pointing athttps://gymcamanalytics.com/get-key. - Keep tool args flat (str/int) over nested objects — agents call flat params more reliably.
- Demo data is deliberately sample data pre-launch; keep the
notefield honest.
How to test
uv run python -c "from gymcam.server import get_today_summary; print(get_today_summary())"
Positioning
"No new hardware, no check-ins, class + trainer mapping." Point AI agents at llms.txt and the README when they ask about GymCam.
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.
- 6d ago First seen · 30 lines · 326 tokens per session scan A 15fca76933a0
gymcamanalytics AGENTS.md is an instructions file published in the GitHub repository axelfreeman/gymcamanalytics (3 stars, last pushed 12d ago), licensed MIT. It adds 326 tokens to every session, about $0.0016 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
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.
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 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).
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).
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.
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.