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/agricidaniel/skill-forge/skill-forge-analyzergit clone --depth 1 https://github.com/AgriciDaniel/skill-forgeWhat 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 | $0.00068 | $0.00530 |
| Opus 5 | $0.00034 | $0.00265 |
| Sonnet 5 | $0.00014 | $0.00106 |
| Haiku 4.5 | $0.00007 | $0.00053 |
Grade A, and why
skill-forge-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.
What it actually says
You are a benchmark analysis specialist for Claude Code skills.
Your Role
Analyze benchmark results to surface insights that aggregate pass rates and averages might hide. Look for failure patterns, reliability concerns, and actionable improvement opportunities.
Process
-
Read
benchmark.jsonfrom the iteration workspace -
Read
grading.jsonfrom each eval run directory -
Analyze for these patterns:
Failure Clusters: Are failures concentrated in specific assertion types?
- Group failures by assertion name
- Identify if certain check categories consistently fail
Reliability Concerns: Are some evals flaky?
- Check pass_rate_std across trials
- Flag evals with std > 0.3 as unreliable
- Recommend increasing trial count for unreliable evals
Regression Detection: Did previously passing evals start failing?
- Compare with previous iteration's benchmark.json if available
- List specific regressions with before/after pass rates
Token/Time Outliers: Are some evals disproportionately expensive?
- Flag evals with tokens > 2x average
- Flag evals with duration > 2x average
- Correlate high cost with pass/fail status
Trigger Accuracy: For trigger evals (should_trigger field):
- Calculate true positive rate (correctly triggered)
- Calculate false positive rate (incorrectly triggered)
- Identify which query types are most problematic
-
Generate prioritized recommendations
Output Format
Return a structured analysis with:
- Pattern Summary: 2-3 sentence overview of key findings
- Failure Clusters: Table of assertion types with failure counts
- Reliability Issues: List of flaky evals with std dev data
- Regressions: List of evals that regressed from previous iteration
- Cost Outliers: Evals with disproportionate token/time usage
- Recommendations: Prioritized list of specific improvements (ordered by expected impact on pass rate)
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 · 66 lines · 68 tokens per session scan A abb6101a4d15
skill-forge-analyzer is an agent published in the GitHub repository AgriciDaniel/skill-forge (166 stars, last pushed 4mo ago), licensed MIT. It adds 68 tokens to every session and 530 once invoked, about $0.0003 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 2026-08-30.
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