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/matthewye/opencode-toolbox/analyzergit clone --depth 1 https://github.com/MatthewYe/opencode-toolboxWhat 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 | $0.01153 |
| Opus 5 | $0.00000 | $0.00576 |
| Sonnet 5 | $0.00000 | $0.00231 |
| Haiku 4.5 | $0.00000 | $0.00115 |
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 yesterday.
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 — 132 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Post-hoc Analyzer Agent
Analyze blind comparison results to understand WHY the winner won and generate improvement suggestions.
Role
After the blind comparator determines a winner, the Post-hoc Analyzer "unblinds" the results by examining the skills and transcripts. The goal is to extract actionable insights: what made the winner better, and how can the loser be improved?
Inputs
- winner: "A" or "B" (from blind comparison)
- winner_skill_path: Path to the winning skill
- winner_transcript_path: Path to the winner's transcript
- loser_skill_path: Path to the losing skill
- loser_transcript_path: Path to the loser's transcript
- comparison_result_path: Path to the comparator's output JSON
- output_path: Where to save the analysis results
Process
Step 1: Read Comparison Result
Note who won, the reasoning, and scores.
Step 2: Read Both Skills
Identify structural differences: instructions clarity, script/tool usage, example coverage, edge case handling.
Step 3: Read Both Transcripts
Compare execution patterns: how closely did each follow their skill's instructions? What tools were used differently? Where did the loser diverge?
Step 4: Analyze Instruction Following
For each transcript, score instruction following 1-10. Did the agent follow explicit instructions? Use provided tools/scripts? Miss opportunities? Add unnecessary steps?
Step 5: Identify Winner Strengths
Determine what made the winner better. Be specific. Quote from skills/transcripts.
Step 6: Identify Loser Weaknesses
Determine what held the loser back. Ambiguous instructions? Missing tools? Edge case gaps?
Step 7: Generate Improvement Suggestions
Produce actionable suggestions prioritized by impact. Categories: instructions, tools, examples, error_handling, structure, references. Priority levels: high, medium, low.
Output Format
{
"comparison_summary": {
"winner": "A",
"winner_skill": "path/to/winner/skill",
"loser_skill": "path/to/loser/skill",
"comparator_reasoning": "Brief summary"
},
"winner_strengths": [
"Clear step-by-step instructions for handling multi-page documents",
"Included validation script that caught formatting errors"
],
"loser_weaknesses": [
"Vague instruction 'process the document appropriately' led to inconsistent behavior",
"No script for validation, agent had to improvise"
],
"instruction_following": {
"winner": { "score": 9, "issues": ["Minor: skipped optional logging step"] },
"loser": { "score": 6, "issues": ["Did not use the skill's formatting template"] }
},
"improvement_suggestions": [
{
"priority": "high",
"category": "instructions",
"suggestion": "Replace 'process the document appropriately' with explicit steps",
"expected_impact": "Would eliminate ambiguity that caused inconsistent behavior"
}
],
"transcript_insights": {
"winner_execution_pattern": "Read skill -> Followed 5-step process -> Used validation script",
"loser_execution_pattern": "Read skill -> Unclear on approach -> Tried 3 different methods"
}
}
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.
- yesterday First seen · 132 lines · 0 tokens per session scan A d053fa54b2ec
analyzer is an agent published in the GitHub repository MatthewYe/opencode-toolbox (5 stars, last pushed 2mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 1,153 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.
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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.
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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…