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 Ghosteken/agent-harness --skill autonomous-agentsgit clone --depth 1 https://github.com/Ghosteken/agent-harnessWrote 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/ghosteken/agent-harness/autonomous-agents)<a href="https://agentmods.dev/skills/ghosteken/agent-harness/autonomous-agents"><img src="https://agentmods.dev/badge/skills/ghosteken/agent-harness/autonomous-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/ghosteken/agent-harness/autonomous-agents"><img src="https://agentmods.dev/badge/skills/ghosteken/agent-harness/autonomous-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 | $0.00053 | $0.06449 |
| Opus 5 | $0.00026 | $0.03224 |
| Sonnet 5 | $0.00011 | $0.01290 |
| Haiku 4.5 | $0.00005 | $0.00645 |
Grade A, and why
autonomous-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 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.
This is a copy
100% identical to autonomous-agents — 1,020 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 — 1,085 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Autonomous Agents
Autonomous agents are AI systems that can independently decompose goals, plan actions, execute tools, and self-correct without constant human guidance. The challenge isn't making them capable - it's making them reliable. Every extra decision multiplies failure probability.
This skill covers agent loops (ReAct, Plan-Execute), goal decomposition, reflection patterns, and production reliability. Key insight: compounding error rates kill autonomous agents. A 95% success rate per step drops to 60% by step 10. Build for reliability first, autonomy second.
2025 lesson: The winners are constrained, domain-specific agents with clear boundaries, not "autonomous everything." Treat AI outputs as proposals, not truth.
Principles
- Reliability over autonomy - every step compounds error probability
- Constrain scope - domain-specific beats general-purpose
- Treat outputs as proposals, not truth
- Build guardrails before expanding capabilities
- Human-in-the-loop for critical decisions is non-negotiable
- Log everything - every action must be auditable
- Fail safely with rollback, not silently with corruption
Capabilities
- autonomous-agents
- agent-loops
- goal-decomposition
- self-correction
- reflection-patterns
- react-pattern
- plan-execute
- agent-reliability
- agent-guardrails
Scope
- multi-agent-systems → multi-agent-orchestration
- tool-building → agent-tool-builder
- memory-systems → agent-memory-systems
- workflow-orchestration → workflow-automation
Tooling
Frameworks
- LangGraph - When: Production agents with state management Note: 1.0 released Oct 2025, checkpointing, human-in-loop
- AutoGPT - When: Research/experimentation, open-ended exploration Note: Needs external guardrails for production
- CrewAI - When: Role-based agent teams Note: Good for specialized agent collaboration
- Claude Agent SDK - When: Anthropic ecosystem agents Note: Computer use, tool execution
Patterns
- ReAct - When: Reasoning + Acting in alternating steps Note: Foundation for most modern agents
- Plan-Execute - When: Separate planning from execution Note: Better for complex multi-step tasks
- Reflection - When: Self-evaluation and correction Note: Evaluator-optimizer loop
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 · 1,085 lines · 53 tokens per session scan A 785ea967ffa0
autonomous-agents is a skill published in the GitHub repository Ghosteken/agent-harness (2 stars, last pushed 6d ago), licensed MIT. It adds 53 tokens to every session and 6,449 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to autonomous-agents, differing in 1,020 lines, and is treated as a copy.
Other skills, from other repositories
agent-self-scheduling
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serial
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eide
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probe-rs
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