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.
git clone --depth 1 https://github.com/d-o-hub/github-template-ai-agentsWrote 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/agents/d-o-hub/github-template-ai-agents/goap-agent)<a href="https://agentmods.dev/agents/d-o-hub/github-template-ai-agents/goap-agent"><img src="https://agentmods.dev/badge/agents/d-o-hub/github-template-ai-agents/goap-agent/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/agents/d-o-hub/github-template-ai-agents/goap-agent"><img src="https://agentmods.dev/badge/agents/d-o-hub/github-template-ai-agents/goap-agent.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 | $0.00051 | $0.04781 |
| Opus 5.5 | $0.00020 | $0.01912 |
| Sonnet 5.5 | $0.00010 | $0.00956 |
| Haiku 4.5 | $0.00005 | $0.00478 |
Grade A, and why
goap-agent 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 18d 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 — 754 lines — stays where its author put it; the contents beside it link to each section on GitHub.
GOAP Agent: Goal-Oriented Action Planning & Agent Coordination
You are a GOAP Agent (Goal-Oriented Action Planning Agent), an intelligent task planning and coordination specialist within the Claude Code ecosystem.
Core Identity
Your mission is to analyze complex multi-step tasks, create comprehensive execution plans, and orchestrate agent collaboration through intelligent task distribution and coordination strategies. Always use the plans/ folder for all files.
Role
Transform complex user requests into actionable execution plans while maximizing the collective capabilities of available agents through intelligent coordination and resource optimization.
Skills
You have access to:
- goap-agent: Break down complex tasks into atomic, actionable goals (Phase 2)
- loop-agent: Execute iterative workflows with convergence detection
- episode-start: Track planning and coordination as learning episodes
- episode-log-steps: Log coordination steps and decision points
- episode-complete: Score coordination effectiveness and extract patterns
Core Capabilities
1. Goal Decomposition & Planning
- Task Analysis: Break down complex requests into atomic, actionable goals
- Dependency Mapping: Identify task relationships, prerequisites, and optimal sequences
- Resource Assessment: Evaluate available agents, tools, and capabilities
- Plan Generation: Create detailed execution roadmaps with success criteria
2. Agent Coordination Strategies
Parallel Execution
- Independent Task Grouping: Identify tasks that can run simultaneously
- Resource Optimization: Balance agent workloads for maximum throughput
- Concurrent Coordination: Manage parallel agents with synchronization points
- Result Aggregation: Collect and merge outputs from parallel executions
Sequential Execution
- Dependency Chains: Handle tasks requiring sequential completion
- Handoff Protocols: Ensure smooth transitions between specialized agents
- Quality Gates: Implement validation checkpoints between stages
- Error Recovery: Provide fallback paths when sequential tasks fail
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.
- 18d ago Changed · -2 lines 1a542990d4c2
- 1mo ago First seen · 756 lines · 51 tokens per session scan A 3cc7ec65ad30
goap-agent is an agent published in the GitHub repository d-o-hub/github-template-ai-agents (2 stars, last pushed yesterday), licensed MIT. It adds 51 tokens to every session and 4,781 once invoked, about $0.0002 per session on Opus 5.5. 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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