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/dswane/sports-context-protocol/agents-mdgit clone --depth 1 https://github.com/Dswane/Sports-Context-ProtocolWrote 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/dswane/sports-context-protocol/agents-md)<a href="https://agentmods.dev/instructions/dswane/sports-context-protocol/agents-md"><img src="https://agentmods.dev/badge/instructions/dswane/sports-context-protocol/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 | $0.00460 | $0.00460 |
| Opus 5 | $0.00230 | $0.00230 |
| Sonnet 5 | $0.00092 | $0.00092 |
| Haiku 4.5 | $0.00046 | $0.00046 |
Grade A, and why
Sports-Context-Protocol 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 4d 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 — 43 lines — stays where its author put it; the contents beside it link to each section on GitHub.
SCP Golf — Build Instructions for Codex
Project goal
Build and extend SCP Golf, a local MCP server for golf operations. SCP Golf gives AI golf agents course context before they book, price, move, message, or recommend anything. The alpha uses only synthetic demo data. Do not reference real courses, real tee-sheet providers, real golfer data, or real customers.
Core product principle
Before a golf agent acts, it checks SCP. Then SCP learns from what happened.
Technical choices
- TypeScript, Node.js (ESM, NodeNext module resolution)
- @modelcontextprotocol/sdk (high-level McpServer + registerTool/Resource/Prompt)
- zod for input schemas
- JSON file storage in src/data/ for the alpha — no database
- MCP stdio transport
- No external APIs, no dashboard, no auth in the alpha
Hard rules
- NEVER write logs to stdout. stdio transport uses stdout for JSON-RPC. All logging goes to stderr (see the log() helper in src/index.ts).
- Golfer-facing output must never expose internal policy language (no "protected member inventory", "league block", "operator override"). The explain.ts golferSafe() filter is the safety net; do not bypass it.
- "Learning" means updating operational memory from feedback. Never implement model training, embeddings, or a nightly retrain.
- Every decision tool call must write a DecisionEvent to the ledger.
- Run
npm run typecheckandnpm run testafter any change. - Update docs/ when behavior changes.
The learning loop is the product
The single most important behavior: ask for Saturday ~09:00, have an operator override it once, ask again, and SCP shifts its recommendation. The decision fingerprint (src/core/fingerprint.ts) is the contract that makes this deterministic — do not change the fingerprint key field order without a migration. See docs/LEARNING_LOOP.md.
Quality expectations
- Small, pure, readable functions. Friendly errors for missing data files.
- Keep the alpha synthetic-only. New features go behind the same JSON-file storage layer until Phase 2.
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.
- 4d ago First seen · 43 lines · 460 tokens per session scan A 03b5e2d3ebda
Sports-Context-Protocol AGENTS.md is an instructions file published in the GitHub repository Dswane/Sports-Context-Protocol (0 stars, last pushed 3mo ago), licensed MIT. It adds 460 tokens to every session, about $0.0023 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).
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.
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).
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.
deepseek-harness AGENTS.md
AGENTS.md instructions for deepseek-ai/deepseek-harness, covering agents.md, pre-release stance: foundation over blast radius, repository layout, commands and host sandbox failures.