Borrowing it
Nothing to install: this file belongs to abcorrea/pddl-skills. 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/abcorrea/pddl-skills/main/AGENTS.mdgit clone --depth 1 https://github.com/abcorrea/pddl-skillsWrote 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/abcorrea/pddl-skills/agents-md)<a href="https://agentmods.dev/instructions/abcorrea/pddl-skills/agents-md"><img src="https://agentmods.dev/badge/instructions/abcorrea/pddl-skills/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 | $0.01741 | $0.01741 |
| Opus 5 | $0.00870 | $0.00870 |
| Sonnet 5 | $0.00348 | $0.00348 |
| Haiku 4.5 | $0.00174 | $0.00174 |
Grade A, and why
pddl-skills 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 7d 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 — 137 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AGENTS.md
Guidance for AI coding agents working on the pddl-skills codebase.
Project overview
pddl-skills is a collection of Claude Code skills for classical PDDL planning:
| Skill | Purpose |
|---|---|
pddl-solver |
Given a domain and problem, produce a validated plan |
pddl-applicable-actions |
Given a domain, problem, and optional partial plan, enumerate all applicable grounded actions in the resulting state |
Each skill is a self-contained directory with a SKILL.md, Python helper scripts, and bundled reference material. Skills have no runtime dependencies on each other.
Repository layout
pddl-skills/
├── skills/
│ ├── pddl-solver/
│ │ ├── SKILL.md # Agent methodology (loaded on trigger)
│ │ ├── scripts/ # stdlib only — no install step needed
│ │ │ ├── parse_pddl.py # Tokenizes + parses domain/problem files
│ │ │ └── validate_plan.py # State-simulation plan validator
│ │ ├── references/
│ │ │ ├── pddl_syntax.md # Classical PDDL syntax reference
│ │ │ └── debugging_guide.md # 10 common failure modes + fixes
│ │ └── assets/
│ │ ├── plan_template.txt
│ │ └── few_shot_examples/blocksworld_solved.txt
│ └── pddl-applicable-actions/
│ ├── SKILL.md
│ ├── scripts/ # stdlib only — no install step needed
│ │ ├── applicable_actions.py # Enumerate applicable grounded actions
│ │ ├── simulate_plan.py # Simulate plan + print resulting state
│ │ ├── parse_pddl.py # copy from pddl-solver
│ │ └── validate_plan.py # copy from pddl-solver
│ ├── references/ # pddl_syntax.md, debugging_guide.md (copies)
│ └── assets/
├── eval/
│ ├── pddl-solver/ # tasks.csv, conditions.json, scripts/, prompts/
│ └── pddl-applicable-actions/ # tasks.csv, conditions.json, ground_truth/,
│ # partial_plans/, scripts/
├── benchmarks/ # Classical planning benchmark domains
├── tests/
│ ├── conftest.py # sys.path setup + shared fixtures
│ ├── test_parse_pddl.py # 45 tests
│ ├── test_validate_plan.py # 46 tests
│ └── test_applicable_actions.py # 35 tests (15 unit + 20 FD cross-validation)
└── pyproject.toml # dev deps (pytest)
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.
- 7d ago First seen · 137 lines · 1,741 tokens per session scan A 4dca9f18d5b9
pddl-skills AGENTS.md is an instructions file published in the GitHub repository abcorrea/pddl-skills (5 stars, last pushed 3mo ago), licensed MIT. It adds 1,741 tokens to every session, about $0.0087 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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