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/autopkg/datajar-recipes/copilot-instructionsgit clone --depth 1 https://github.com/autopkg/dataJAR-recipesWrote 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/autopkg/datajar-recipes/copilot-instructions)<a href="https://agentmods.dev/instructions/autopkg/datajar-recipes/copilot-instructions"><img src="https://agentmods.dev/badge/instructions/autopkg/datajar-recipes/copilot-instructions.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 | $0.02045 | $0.02045 |
| Opus 5 | $0.01022 | $0.01022 |
| Sonnet 5 | $0.00409 | $0.00409 |
| Haiku 4.5 | $0.00204 | $0.00204 |
Grade A, and why
dataJAR-recipes copilot-instructions.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 — 214 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AutoPkg Recipe Linter Suite - AI Coding Agent Instructions
Project Overview
This is a suite of Python linters for validating and fixing AutoPkg recipes. AutoPkg recipes are XML plist or YAML files that automate software packaging for macOS. Each linter is a standalone script in its own directory that detects and fixes specific issues in recipe files.
Architecture: Unified CLI runner (autopkg-linter.py) + 16 independent linter modules, each following the same pattern.
Critical Environment Requirement
⚠️ All scripts MUST run using AutoPkg's Python: /usr/local/autopkg/python
Every script includes verify_environment() that checks sys.executable.startswith('/usr/local/autopkg'). This is not optional - AutoPkg recipes require AutoPkg's Python environment with its specific dependencies (plistlib, etc.).
Linter Architecture Pattern
Each linter follows this structure:
LinterName/
├── LinterName.py # Main script with main() function
├── README.md # Detailed documentation
└── __pycache__/ # Python bytecode cache
Standard Linter Structure
Every linter script contains:
- Shebang:
#!/usr/local/autopkg/python - verify_environment(): Validates Python path
- clean_path(): Handles drag-and-drop paths with escaped spaces
- process_plist_recipe(): Logic for XML plist recipes
- process_yaml_recipe(): Logic for YAML recipes (if applicable)
- main(): Interactive prompt for recipe directory, then scans/processes files
Recipe Format Handling
- Plist recipes (
.recipe): XML plist format usingplistlib - YAML recipes (
.yaml): YAML format usingyamlmodule - Most linters support both formats with separate processing functions
- Some operations (e.g., XML escaping) only apply to plist format
Suite Runner Integration
autopkg-linter.py dynamically loads and runs linters:
- get_available_linters(): Returns list of (number, name, directory, script, description) tuples
- load_linter_module(): Uses
importlib.util.spec_from_file_location()to load scripts - run_linter(): Temporarily overrides
sys.argvand__builtins__.inputto inject recipe directory and auto-answer prompts in bulk mode - Modes:
bulk: Auto-provides recipe directory + default answers to all promptsinteractive: Provides recipe directory, then allows user interaction
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 · 214 lines · 2,045 tokens per session scan A f1121e9c5f93
dataJAR-recipes copilot-instructions.md is an instructions file published in the GitHub repository autopkg/dataJAR-recipes (130 stars, last pushed yesterday), licensed Apache-2.0. It adds 2,045 tokens to every session, about $0.0102 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 2026-08-30.
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