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 agentmods add skills/d-o-hub/github-template-ai-agents/goap-agentnpx skills add d-o-hub/github-template-ai-agents --skill goap-agentgit 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/skills/d-o-hub/github-template-ai-agents/goap-agent)<a href="https://agentmods.dev/skills/d-o-hub/github-template-ai-agents/goap-agent"><img src="https://agentmods.dev/badge/skills/d-o-hub/github-template-ai-agents/goap-agent.svg" alt="Measured on agentmods" 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 | $0.00000 | $0.01520 |
| Opus 5 | $0.00000 | $0.00760 |
| Sonnet 5 | $0.00000 | $0.00304 |
| Haiku 4.5 | $0.00000 | $0.00152 |
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 6d 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 — 201 lines — stays where its author put it; the contents beside it link to each section on GitHub.
GOAP Agent Skill: Goal-Oriented Action Planning
Enable intelligent planning and execution of complex multi-step tasks through systematic decomposition, dependency mapping, and coordinated multi-agent execution.
Always use the plans/ folder for all files. Use plans/GOAP_STATE.md to track persistent state.
Quick Reference
execution-strategies.md- Detailed guide on execution patternsreferences/guide.md- Complete examples, templates, and advanced topics
When to Use
Use this skill when facing:
- Complex Multi-Step Tasks: Tasks requiring 5+ distinct steps
- Cross-Domain Problems: Issues spanning multiple areas
- Optimization Opportunities: Tasks benefiting from parallel execution
- Quality-Critical Work: Projects requiring validation checkpoints
Core GOAP Methodology
The GOAP Planning Cycle
1. ANALYZE → 2. DECOMPOSE → 3. STRATEGIZE → 4. COORDINATE → 5. EXECUTE → 6. SYNTHESIZE
Phase 1: Task Analysis (Analyze & Strategize)
Before decomposing tasks, ensure architectural decisions are sound:
- Analyze: Use
triz-analysisortriz-solverto evaluate the problem and resolve contradictions. - Decide: Formulate an ADR (Architecture Decision Record) detailing Context, Decision, and Consequences.
- Gate: Wait for human approval of the ADR before proceeding to decomposition.
## Task Analysis
**Primary Goal**: [Clear statement of what success looks like]
**Constraints**: [Time, Resources]
**Complexity**: Simple/Medium/Complex
**ADR Link**: [Link to approved Architecture Decision Record]
Context: Use Explore agent, check past patterns, perform TRIZ analysis, and record architectural decisions.
Phase 2: Task Decomposition
Decompose high-level objectives into manageable, testable sub-tasks.
Decomposition Framework
- Requirements Analysis: Extract primary objective, implicit requirements, constraints, success criteria.
- Goal Hierarchy: Top-down decomposition into sub-goals and atomic tasks.
- Dependency Mapping: Sequential (A→B→C), Parallel (A,B,C), Converging (A,B,C→D).
- Success Criteria: Define inputs, outputs, quality standards.
What ships with it
3 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.
- 6d ago First seen · 201 lines · 0 tokens per session scan A 0b023156403b
goap-agent is a skill published in the GitHub repository d-o-hub/github-template-ai-agents (2 stars, last pushed yesterday), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 1,520 tokens. 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 2026-08-31.
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prismflow-memory-read
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