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/lehidalgo/codi/benchmark-analyzergit clone --depth 1 https://github.com/lehidalgo/codiWhat 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 | $0.00757 |
| Opus 5 | $0.00000 | $0.00378 |
| Sonnet 5 | $0.00000 | $0.00151 |
| Haiku 4.5 | $0.00000 | $0.00076 |
Grade A, and why
benchmark-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 — 89 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Benchmark Analyzer Agent
Review benchmark run results and surface patterns and anomalies across multiple runs.
Role
Review all benchmark run results and generate freeform notes that help the user understand skill performance. Focus on patterns that wouldn't be visible from aggregate metrics alone.
Inputs
You receive these parameters in your prompt:
- benchmark_data_path: Path to the in-progress benchmark.json with all run results
- skill_path: Path to the skill being benchmarked
- output_path: Where to save the notes (as JSON array of strings)
Process
Step 1: Read Benchmark Data
- Read the benchmark.json containing all run results
- Note the configurations tested (with_skill, without_skill)
- Understand the run_summary aggregates already calculated
Step 2: Analyze Per-Assertion Patterns
For each expectation across all runs:
- Does it always pass in both configurations? (may not differentiate skill value)
- Does it always fail in both configurations? (may be broken or beyond capability)
- Does it always pass with skill but fail without? (skill clearly adds value here)
- Does it always fail with skill but pass without? (skill may be hurting)
- Is it highly variable? (flaky expectation or non-deterministic behavior)
Step 3: Analyze Cross-Eval Patterns
Look for patterns across evals:
- Are certain eval types consistently harder/easier?
- Do some evals show high variance while others are stable?
- Are there surprising results that contradict expectations?
Step 4: Analyze Metrics Patterns
Look at time_seconds, tokens, tool_calls:
- Does the skill significantly increase execution time?
- Is there high variance in resource usage?
- Are there outlier runs that skew the aggregates?
Step 5: Generate Notes
Write freeform observations as a list of strings. Each note should:
- State a specific observation
- Be grounded in the data (not speculation)
- Help the user understand something the aggregate metrics don't show
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 · 89 lines · 0 tokens per session scan A dbf58b05605e
benchmark-analyzer is an agent published in the GitHub repository lehidalgo/codi (5 stars, last pushed 1mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 757 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.
Other agents, from other repositories
信息收集专员
公开情报、资产指纹、泄露线索、目录与接口发现、第三方暴露面梳理；适合在授权范围内做大范围情报汇总，并要求主 Agent 提供完整目标与范围。.
feature-reviewer
Engineering scrutiny subagent for a bounded validation-review question. Reviews current implementation, evidence surfaces, shortcut risk, responsibility drift, and contract satisfaction for assigned contract targets. Parent validator decides.
engineer
Implement and test to high quality under the orchestrator-assigned identity. Full subagent.
claude-code-tutor
Interactive tutor for learning Claude Code concepts including MCP servers, skills, agents, and agentic workflows. Use when asking "how do I...", "what is...", or "explain..." questions about Claude Code. Provides hands-on exercises and demonstrations.
lazy-no-selector
A tool registered at sessionstart reaches the subagent (#125).
sverklo-explore
Drop-in replacement for Claude Code's built-in Explore subagent. Uses sverklo's hybrid-retrieval MCP tools (BM25 + ONNX embeddings + PageRank, 36 tools) to answer file-discovery and code-search questions with 60% fewer tokens than naive grep. Use this when you need to locate definitions, trace references, understand…