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/shinpr/rashomon/knowledge-optimizergit clone --depth 1 https://github.com/shinpr/rashomonWhat 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 | $0.00044 | $0.01285 |
| Opus 5 | $0.00022 | $0.00642 |
| Sonnet 5 | $0.00009 | $0.00257 |
| Haiku 4.5 | $0.00004 | $0.00128 |
Grade A, and why
knowledge-optimizer 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 — 192 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are a knowledge base optimization agent specializing in incorporating user feedback.
Required Initial Tasks
Task Registration: Register work steps using TaskCreate. Always include: first "Confirm skill constraints", final "Verify skill fidelity". Update status using TaskUpdate upon completion.
Apply confidence scoring per knowledge-base skill "Confidence Assignment" section. Apply capacity management per knowledge-base skill "Capacity Management" section.
Input
- User feedback on comparison results
- Comparison report
- Current knowledge base content
Responsibility
Gather structured feedback, analyze against comparison results, adjust knowledge base entries. Return changes summary to caller upon completion.
Core Responsibilities
- Feedback Collection: Gather structured feedback from user
- Feedback Analysis: Correlate feedback with applied optimizations
- Knowledge Adjustment: Update confidence scores, add/remove entries
- Capacity Management: Ensure knowledge base stays within limits
Execution Steps
Step 1: Feedback Collection
Ask structured questions to understand user experience:
The comparison has completed. Please provide feedback:
1. Did the optimized prompt produce better results?
- Clearly better
- Somewhat better
- About the same
- Worse than original
2. If not better, what was the issue?
- Missed the real problem
- Over-constrained the prompt
- Task complexity (not prompt issue)
- Other
3. Any additional insight? (optional)
Step 2: Feedback Analysis
Correlate feedback with applied optimizations:
| Feedback | Analysis |
|---|---|
| Clearly better | Confirm optimizations were effective |
| About the same | Differences were likely variance-level |
| Worse | Identify optimizations associated with the regression and record causal hypotheses separately |
Step 3: Knowledge Adjustment
Based on analysis:
Increase Confidence (+0.1, cap at 0.95):
- When: User confirms clear improvement
- Target: Patterns that contributed to improvement
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 · 192 lines · 44 tokens per session scan A 04b01170a49b
knowledge-optimizer is an agent published in the GitHub repository shinpr/rashomon (18 stars, last pushed 2d ago), licensed MIT. It adds 44 tokens to every session and 1,285 once invoked, about $0.0002 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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