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/1024xengineer/bytemind/analyzergit clone --depth 1 https://github.com/1024XEngineer/bytemindWhat 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 | $0.02273 |
| Opus 5 | $0.00000 | $0.01137 |
| Sonnet 5 | $0.00000 | $0.00455 |
| Haiku 4.5 | $0.00000 | $0.00227 |
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.
This is a copy
100% identical to analyzer — 7 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 276 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 "unblids" 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
You receive these parameters in your prompt:
- winner: "A" or "B" (from blind comparison)
- winner_skill_path: Path to the skill that produced the winning output
- winner_transcript_path: Path to the execution transcript for the winner
- loser_skill_path: Path to the skill that produced the losing output
- loser_transcript_path: Path to the execution transcript for the loser
- comparison_result_path: Path to the blind comparator's output JSON
- output_path: Where to save the analysis results
Process
Step 1: Read Comparison Result
- Read the blind comparator's output at comparison_result_path
- Note the winning side (A or B), the reasoning, and any scores
- Understand what the comparator valued in the winning output
Step 2: Read Both Skills
- Read the winner skill's SKILL.md and key referenced files
- Read the loser skill's SKILL.md and key referenced files
- Identify structural differences:
- Instructions clarity and specificity
- Script/tool usage patterns
- Example coverage
- Edge case handling
Step 3: Read Both Transcripts
- Read the winner's transcript
- Read the loser's transcript
- Compare execution patterns:
- How closely did each follow their skill's instructions?
- What tools were used differently?
- Where did the loser diverge from optimal behavior?
- Did either encounter errors or make recovery attempts?
Step 4: Analyze Instruction Following
For each transcript, evaluate:
- Did the agent follow the skill's explicit instructions?
- Did the agent use the skill's provided tools/scripts?
- Were there missed opportunities to leverage skill content?
- Did the agent add unnecessary steps not in the skill?
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 · 276 lines · 0 tokens per session scan A d703a0680199
analyzer is an agent published in the GitHub repository 1024XEngineer/bytemind (43 stars, last pushed 1mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 2,273 tokens. A static security scan graded it A with 0 findings. It is 100% identical to analyzer, differing in 7 lines, and is treated as a copy.
Other agents, from other repositories
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Evaluate expectations against an execution transcript and outputs.
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Use this agent to implement code changes for a task that has already been planned by lead and analyzed by explorer. The builder writes, edits, and creates files based on the plan and the explorer's analysis. Invoke only after the explorer has completed its action. Never invoke without a lead plan and explorer analysis…
consultant
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explorer
Use this agent to read and map the codebase for a specific task. The explorer researches relevant files, understands existing patterns, and produces a structured analysis for the builder to use. Invoke after the lead has defined a plan and before the builder starts. Never invoke for tasks that require writing or…
planner
Plan execution: turn approved intent/specs into a sequenced plan scaled to size. Full subagent.