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 arturseo-geo/claude-code-skills --skill agentsgit clone --depth 1 https://github.com/arturseo-geo/claude-code-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/skills/arturseo-geo/claude-code-skills/agents)<a href="https://agentmods.dev/skills/arturseo-geo/claude-code-skills/agents"><img src="https://agentmods.dev/badge/skills/arturseo-geo/claude-code-skills/agents/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/arturseo-geo/claude-code-skills/agents"><img src="https://agentmods.dev/badge/skills/arturseo-geo/claude-code-skills/agents.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 | $0.00185 | $0.04248 |
| Opus 5 | $0.00093 | $0.02124 |
| Sonnet 5 | $0.00037 | $0.00850 |
| Haiku 4.5 | $0.00018 | $0.00425 |
Grade A, and why
agents 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 10d 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 — 532 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Agents Skill
Agent Architectures
ReAct (Reason + Act) — Default Pattern
Thought: What do I need to figure out?
Action: [tool_name] with [parameters]
Observation: [tool result]
Thought: What does this tell me? What's next?
Action: ...
Final Answer: [synthesized result]
Best for: Research, data gathering, multi-step Q&A
Plan-and-Execute
Step 1: Planner agent creates a full plan (list of steps)
Step 2: Executor agent runs each step in order
Step 3: Replanner reviews results and updates plan if needed
Best for: Complex tasks with many interdependent steps
Reflection / Self-Critique Loop
Generate output -> Critique output -> Revise -> Repeat until quality threshold met
Best for: Writing, code generation, analysis quality improvement
Key implementation detail: define explicit quality criteria before entering the loop. Without measurable criteria, reflection loops spin without converging. Common criteria: factual accuracy, completeness against a checklist, adherence to a style guide, or passing a test suite.
Tree of Thoughts (ToT)
Step 1: Generate N candidate next-steps (branches)
Step 2: Evaluate each branch with a scoring heuristic
Step 3: Expand the top-K branches
Step 4: Repeat until a solution branch reaches the goal
Best for: Problems with large search spaces — math proofs, puzzle solving, strategic planning. More expensive than ReAct (requires multiple LLM calls per step), so use only when single-path reasoning fails.
Language Agent Tree Search (LATS)
Step 1: Select a node using UCT (Upper Confidence bound for Trees)
Step 2: Expand by generating candidate actions
Step 3: Evaluate with environment feedback + LLM self-reflection
Step 4: Backpropagate scores up the tree
Step 5: Repeat until budget exhausted or solution found
Best for: Complex reasoning tasks where you want Monte Carlo Tree Search-style exploration combined with LLM reasoning. Produces higher-quality results than ReAct on hard problems at the cost of more LLM calls.
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.
- 10d ago First seen · 532 lines · 185 tokens per session scan A 0868594cfa93
agents is a skill published in the GitHub repository arturseo-geo/claude-code-skills (11 stars, last pushed 5mo ago), licensed MIT. It adds 185 tokens to every session and 4,248 once invoked, about $0.0009 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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