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 skills add xuansenpa1/skillrevise --skill pddl-skillsgit clone --depth 1 https://github.com/xuansenpa1/skillreviseWrote 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/skills/xuansenpa1/skillrevise/pddl-skills)<a href="https://agentmods.dev/skills/xuansenpa1/skillrevise/pddl-skills"><img src="https://agentmods.dev/badge/skills/xuansenpa1/skillrevise/pddl-skills/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/xuansenpa1/skillrevise/pddl-skills"><img src="https://agentmods.dev/badge/skills/xuansenpa1/skillrevise/pddl-skills.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00044 | $0.00718 |
| Opus 5 | $0.00022 | $0.00359 |
| Sonnet 5 | $0.00009 | $0.00144 |
| Haiku 4.5 | $0.00004 | $0.00072 |
Grade A, and why
pddl-skills 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 8d 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.
This is a copy
100% identical to pddl-skills — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 142 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Requirements for Outputs
General Guidelines
PDDL Files
- Domain files must follow PDDL standard syntax.
- Problem files must reference the correct domain.
- Plans must be sequential classical plans.
Planner Behavior
- Planning must terminate within timeout.
- If no plan exists, return an empty plan or explicit failure flag.
- Validation must confirm goal satisfaction.
PDDL Skills
1. Load Domain and Problem
load-problem(domain_path, problem_path)
Description:
Loads a PDDL domain file and problem file into a unified planning problem object.
Parameters:
domain_path(str): Path to PDDL domain file.problem_path(str): Path to PDDL problem file.
Returns:
problem_object: Aunified_planning.model.Probleminstance.
Example:
problem = load_problem("domain.pddl", "task01.pddl")
Notes:
- Uses unified_planning.io.PDDLReader.
- Raises an error if parsing fails.
2. Plan Generation
generate-plan(problem_object)
Description: Generates a plan for the given planning problem using a classical planner.
Parameters:
problem_object: A unified planning problem instance.
Returns:
plan_object: A sequential plan.
Example:
plan = generate_plan(problem)
Notes:
- Uses
unified_planning.shortcuts.OneshotPlanner. - Default planner:
pyperplan. - If no plan exists, returns None.
3. Plan Saving
save-plan(plan_object, output_path)
Description: Writes a plan object to disk in standard PDDL plan format.
Parameters:
-
plan_object: A unified planning plan. -
output_path(str): Output file path.
Example:
save_plan(plan, "solution.plan")
Notes:
- Uses
unified_planning.io.PDDLWriter. - Output is a text plan file.
4. Plan Validation
validate(problem_object, plan_object)
Description: Validates that a plan correctly solves the given PDDL problem.
Parameters:
problem_object: The planning problem.plan_object: The generated plan.
What ships with it
4 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 8d ago First seen · 142 lines · 44 tokens per session scan A adc4db64d255
pddl-skills is a skill published in the GitHub repository xuansenpa1/skillrevise (56 stars, last pushed 6d ago), licensed MIT. It adds 44 tokens to every session and 718 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to pddl-skills, differing in 0 lines, and is treated as a copy.
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