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/ar9av/obsidian-wiki/analyzergit clone --depth 1 https://github.com/Ar9av/obsidian-wikiWhat 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 | $0.02271 |
| Opus 5 | $0.00000 | $0.01136 |
| Sonnet 5 | $0.00000 | $0.00454 |
| 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 — 0 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 — 275 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 · 275 lines · 0 tokens per session scan A bf68f4cac5a5
analyzer is an agent published in the GitHub repository Ar9av/obsidian-wiki (3,325 stars, last pushed yesterday), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 2,271 tokens. A static security scan graded it A with 0 findings. It is 100% identical to analyzer, differing in 0 lines, and is treated as a copy.
Other agents, from other repositories
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desk
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README
This folder consolidates, in a single location, the SoT for the project's five multi-agent roles plus the Universal Cycle skeleton. It is modeled on the role system of a Korean newspaper, and each role is self-contained in its capability boundary, I/O contract, and prompt template.
editor-in-chief
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columnist
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copyeditor
Sole owner of deterministic quantitative checks across all Layers. Runs the 10 tools/lint.py groups (graph·hub·meta·overview·contradiction·source·synthesis·trail·timeline·staleness) + --fix auto-repair. PASS/FAIL exit code + lint-report.md + graph/health-log.jsonl. No qualitative evaluation.