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/commands/d-o-hub/github-template-ai-agents/swarm-analyze)<a href="https://agentmods.dev/commands/d-o-hub/github-template-ai-agents/swarm-analyze"><img src="https://agentmods.dev/badge/commands/d-o-hub/github-template-ai-agents/swarm-analyze.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 | $0.00019 | $0.00209 |
| Opus 5 | $0.00010 | $0.00105 |
| Sonnet 5 | $0.00004 | $0.00042 |
| Haiku 4.5 | $0.00002 | $0.00021 |
Grade A, and why
swarm-analyze 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 3d 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.
What it actually says
Perform a comprehensive swarm analysis of the codebase using the analysis-swarm agent.
Analyze from these perspectives:
- Feature Gaps - Missing capabilities users would expect
- Implementation Completeness - Stubs, TODOs, missing error handling
- Documentation Gaps - Undocumented APIs, complex logic, setup instructions
- Test Coverage - Untested code, missing integration tests
- Architecture & Patterns - Design patterns, coupling, extensibility
- Security & Quality - Security concerns, code quality issues
For each gap found, report:
- What's missing or incomplete
- Why it matters
- Priority (Critical/High/Medium/Low)
- Suggested fix or approach
Also identify quick wins, issues confirmed by multiple perspectives, and dependencies between gaps.
Save findings to analysis/SWARM_ANALYSIS.md.
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.
- 3d ago First seen · 25 lines · 19 tokens per session scan A d33474a4b99f
swarm-analyze is a command published in the GitHub repository d-o-hub/github-template-ai-agents (2 stars, last pushed today), licensed MIT. It adds 19 tokens to every session and 209 once invoked, about $0.0001 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.
Other commands, from other repositories
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final-review
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review
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code-permutation-tester
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boundary-heatmap
Generate a visual heatmap of knowledge boundaries showing safe zones, risk areas, and semantic coverage.