Borrowing it
Nothing to install: this file belongs to ddalgrande/dining-places-skill. 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/ddalgrande/dining-places-skill/main/AGENTS.mdgit clone --depth 1 https://github.com/ddalgrande/dining-places-skillWrote 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/ddalgrande/dining-places-skill/agents-md)<a href="https://agentmods.dev/instructions/ddalgrande/dining-places-skill/agents-md"><img src="https://agentmods.dev/badge/instructions/ddalgrande/dining-places-skill/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 | $0.00552 | $0.00552 |
| Opus 5 | $0.00276 | $0.00276 |
| Sonnet 5 | $0.00110 | $0.00110 |
| Haiku 4.5 | $0.00055 | $0.00055 |
Grade A, and why
dining-places-skill 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 2d 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 — 50 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AGENTS.md
This repo is a portable Agent Skill: it turns a user's exported Google Maps saved places into a personal dining knowledge base and gives calibrated eating-out advice. It is not an application — there is nothing to build, serve, or deploy.
SKILL.md is the operational spec. Any coding agent — Claude Code, Codex, Cursor,
Gemini CLI, Kimi, GLM-based agents, or a generic assistant — can use this repo by
reading SKILL.md and following it. The name: / description: frontmatter in
SKILL.md follows the open Agent Skills standard.
How to use it, by agent capability
-
Your agent supports skills (Claude Code, Codex, Cursor, Gemini CLI, …): install the skill folder where your agent discovers skills. See
README.md→ Install. After that, nothing is invoked by hand — the agent loadsSKILL.mdautomatically when the user asks about eating out. -
Your agent has no skills mechanism (Kimi CLI, many Claude Code-compatible clients, plain chat assistants): treat
SKILL.mdas a standing instruction set. When the user asks where to eat / drink, for a restaurant or bar recommendation, for somewhere near a location, or references their saved / want-to-go / starred places — openSKILL.mdand follow its workflow.
The one command
python3 scripts/parse_takeout_maps.py <folder> --out references/saved_places.json --dedupe
<folder> is a Takeout .zip, a folder of zips, or an extracted folder. The
script is Python 3.9+, standard library only — no third-party packages, no
network access. Enrichment (cuisine, rating, price, hours, map) happens at
advice-time through whatever Places/maps tool the agent has; it is not part of
this repo.
Ground rules
- Never commit personal data.
references/saved_places.jsonandreferences/known_ids.mdare git-ignored. The*.examplefiles show the shape with dummy data. - Don't run the parser during install. It runs only when the user asks for a recommendation and has provided their Takeout export.
- Booking is side-effecting. Confirm place / date / party size with the user before submitting anything; never auto-submit.
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.
- 2d ago First seen · 50 lines · 552 tokens per session scan A 74a143b443e4
dining-places-skill AGENTS.md is an instructions file published in the GitHub repository ddalgrande/dining-places-skill (1 stars, last pushed 5d ago), licensed MIT. It adds 552 tokens to every session, about $0.0028 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 2026-09-04.
Other instructions, from other repositories
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vscode buildNext.instructions.md
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langchain AGENTS.md
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spec-kit AGENTS.md
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