SkillsBench is a benchmark for measuring how effectively AI agents use modular skills—folders containing instructions, scripts, and resources—to complete specialized tasks. It helps researchers and developers evaluate both skill quality and agent behavior, including tasks that require combining multiple skills. The catalogue’s skills and instructions are evaluated as part of this workflow.
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 benchflow-ai/skillsbench --skill pddl-skillsgit clone --depth 1 https://github.com/benchflow-ai/skillsbenchWrote 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/benchflow-ai/skillsbench/pddl-skills)<a href="https://agentmods.dev/skills/benchflow-ai/skillsbench/pddl-skills"><img src="https://agentmods.dev/badge/skills/benchflow-ai/skillsbench/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/benchflow-ai/skillsbench/pddl-skills"><img src="https://agentmods.dev/badge/skills/benchflow-ai/skillsbench/pddl-skills.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 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.
Copies of this mod
2 near-identical copies found in the catalogue:
- pddl-skills — 100% identical, 0 lines differ
- skill-117 — 98% identical, 3 lines differ
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.
- 7d ago First seen · 142 lines · 44 tokens per session scan A adc4db64d255
pddl-skills is a skill published in the GitHub repository benchflow-ai/skillsbench (1,760 stars, last pushed 1mo ago), licensed Apache-2.0. 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. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
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