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 vasilyu1983/AI-Agents-public --skill foundations-ai-planning-searchgit clone --depth 1 https://github.com/vasilyu1983/AI-Agents-publicWrote 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/vasilyu1983/ai-agents-public/foundations-ai-planning-search)<a href="https://agentmods.dev/skills/vasilyu1983/ai-agents-public/foundations-ai-planning-search"><img src="https://agentmods.dev/badge/skills/vasilyu1983/ai-agents-public/foundations-ai-planning-search/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/vasilyu1983/ai-agents-public/foundations-ai-planning-search"><img src="https://agentmods.dev/badge/skills/vasilyu1983/ai-agents-public/foundations-ai-planning-search.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 | $0.00052 | $0.03567 |
| Opus 5 | $0.00026 | $0.01784 |
| Sonnet 5 | $0.00010 | $0.00713 |
| Haiku 4.5 | $0.00005 | $0.00357 |
Grade A, and why
foundations-ai-planning-search 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 9d 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 — 251 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AI Planning And Search Foundations
10 applied AI planning and search primitives for turning a problem into states, actions, constraints, heuristics, and plans. Use this when the hard part is problem formulation or explicit search over alternatives, not language fluency.
Contents
- Quick Reference
- When to Apply
- Primitive Index
- Formal Supporting Theory
- Anti-Patterns
- Misuse Boundaries
- Decision Checklist
- Composition Recipes
- Workflow
- ASCII Flow
- Navigation
- Fact-Checking
Quick Reference
| Primitive | Problem It Solves | Key Parameters |
|---|---|---|
| Problem Formulation | Vague tasks cannot be searched or verified | State, actions, transition model, goal test, path cost |
| Uninformed Search | Need complete baseline without domain heuristic | Branching factor b; depth d; frontier policy |
| Heuristic Search | Large state spaces need directed exploration | Heuristic h(n); admissibility; consistency |
| Local Search | State is large but path is irrelevant | Neighborhood; objective; restart/schedule |
| Constraint Satisfaction | Need assignments satisfying hard constraints | Variables, domains, constraints, MRV/LCV, arc consistency |
| Adversarial Search | Opponent actions affect outcomes | Utility, depth, alpha-beta bounds, rollout policy |
| Classical Planning | Need valid action sequence from symbolic preconditions/effects | STRIPS/PDDL, progression/regression, plan graph |
| Hierarchical Planning | Tasks decompose into reusable subplans | Methods, subtasks, ordering constraints |
| Contingent / Belief-State Planning | Partial observability or nondeterministic actions | Belief state, sensing actions, policy vs sequence |
| Planner-Agent Integration | LLM agent needs explicit plan validity and search boundaries | Planner tool, state abstraction, verifier, replanning trigger |
What ships with it
7 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.
- 9d ago First seen · 251 lines · 52 tokens per session scan A ce368e562ea7
foundations-ai-planning-search is a skill published in the GitHub repository vasilyu1983/AI-Agents-public (87 stars, last pushed 10d ago), licensed MIT. It adds 52 tokens to every session and 3,567 once invoked, about $0.0003 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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