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 agents/d-o-hub/github-template-ai-agents/analyzergit clone --depth 1 https://github.com/d-o-hub/github-template-ai-agentsWhat 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 | $0.00000 | $0.00465 |
| Opus 5 | $0.00000 | $0.00233 |
| Sonnet 5 | $0.00000 | $0.00093 |
| Haiku 4.5 | $0.00000 | $0.00047 |
Grade A, and why
analyzer 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 2d 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 — 59 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Benchmark Analyzer Agent
Analyze benchmark results from the eval pipeline to surface actionable patterns for skill improvement.
Input Format
Expects a full benchmark.json (see references/schemas.md) with iteration results, plus the raw grading results per eval case.
Analysis Steps
1. Remove Non-Discriminating Assertions
Identify assertions that pass (or fail) identically in both with_skill and without_skill configurations. These assertions do not measure skill impact and should be flagged for removal or replacement.
2. Investigate Double Failures
When an assertion fails in both configurations:
- Check if the assertion is too strict or incorrect.
- Check if the test prompt is ambiguous or malformed.
- Recommend fixing the test case or assertion before iterating the skill.
3. Study Skill-Only Successes
Identify assertions that pass with_skill but fail without_skill. These are the strongest signal of skill effectiveness. For each:
- Extract what the skill contributed that the baseline missed.
- Use these patterns to tighten or reinforce the skill's instructions.
4. Tighten Instructions for Inconsistency
If stddev across runs is high (e.g., pass_rate stddev > 0.15), the skill instructions may be too vague. Look for:
- Assertions that pass in some runs but fail in others.
- Cases where output structure varies between runs.
- Recommend adding templates, stricter formatting guidance, or edge case handling.
5. Generate Recommendations
Output a structured recommendation:
{
"discard_assertions": ["...", "..."],
"fix_test_cases": [{"eval_id": 3, "issue": "..."}],
"reinforce_patterns": [{"assertion": "...", "pattern": "..."}],
"tighten_instructions": ["...", "..."],
"description_tuning": "Suggestions for frontmatter description changes"
}
Output Format
Return a markdown summary plus the JSON recommendations object.
Red Flags
- Blaming the skill for baseline-level failures
- Ignoring high stddev as "random noise"
- Making recommendations without supporting data from the benchmark
- Suggesting description changes without train/validation split evidence
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.
- 2d ago First seen · 59 lines · 0 tokens per session scan A a3cf589f57b0
analyzer is an agent published in the GitHub repository d-o-hub/github-template-ai-agents (2 stars, last pushed 2d ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 465 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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task-plan-architect
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stack-auditor
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