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 skills add shinpr/rashomon --skill knowledge-basegit clone --depth 1 https://github.com/shinpr/rashomonWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/shinpr/rashomon/knowledge-base)<a href="https://agentmods.dev/skills/shinpr/rashomon/knowledge-base"><img src="https://agentmods.dev/badge/skills/shinpr/rashomon/knowledge-base/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/shinpr/rashomon/knowledge-base"><img src="https://agentmods.dev/badge/skills/shinpr/rashomon/knowledge-base.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What 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 5.1 | $0.00034 | $0.01245 |
| Opus 5.5 | $0.00014 | $0.00498 |
| Sonnet 5.5 | $0.00007 | $0.00249 |
| Haiku 4.5 | $0.00003 | $0.00125 |
Grade A, and why
knowledge-base 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 1mo 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 — 162 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Knowledge Base Skill
Storage Location
{project_root}/.claude/.rashomon/prompt-knowledge.yaml
Schema
patterns:
- name: "Pattern name"
what_to_look_for: |
When this pattern applies
improvement: |
How to improve when detected
learned_from: "Date and context"
source: "comparison ID, report path, or user feedback"
source_fingerprint: "stable hash or revision identifying the evidence"
validity_scope: "project area, version range, or conditions where this applies"
last_verified: "ISO-8601 timestamp"
invalidated_when: "observable condition that requires revalidation"
confidence: 0.0-1.0
times_applied: 0
anti_patterns:
- name: "Anti-pattern name"
what_to_look_for: |
What to avoid
why_bad: |
Why problematic in this project
learned_from: "Date and context"
source: "comparison ID, report path, or user feedback"
source_fingerprint: "stable hash or revision identifying the evidence"
validity_scope: "project area, version range, or conditions where this applies"
last_verified: "ISO-8601 timestamp"
invalidated_when: "observable condition that requires revalidation"
confidence: 0.0-1.0
times_applied: 0
metadata:
last_updated: "ISO-8601 timestamp"
total_comparisons: 0
patterns_count: 0
anti_patterns_count: 0
max_entries: 20
Extraction Criteria
Save as Improvement Pattern
ALL conditions must be true:
- Optimized prompt showed structural improvement (not variance)
- Improvement is project-specific (not explained by BP-001~009)
- Pattern is likely to recur in this project
Confidence Assignment:
| Evidence | Confidence |
|---|---|
| Multiple comparisons confirmed | 0.8+ |
| Single comparison, clear effect | 0.5-0.7 |
| Effect present but uncertain | 0.3-0.5 |
Minimum threshold: 0.3 (entries below this are skipped)
Save as Anti-Pattern
ALL conditions must be true:
- Original had problem specific to this project
- Problem is project-specific (beyond standard patterns BP-001~009)
- Problem likely to recur
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.
- 1mo ago Changed · +1 tokens per session 8fb1b5459220
- 1mo ago First seen · 162 lines · 33 tokens per session scan A f2daf0f1e62e
knowledge-base is a skill published in the GitHub repository shinpr/rashomon (19 stars, last pushed yesterday), licensed MIT. It adds 34 tokens to every session and 1,245 once invoked, about $0.0001 per session on Opus 5.5. 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.
Other skills, from other repositories
add-new-entry-from-temp-md
Format new entries from temp.md into tempentries.md, ready to insert into the section files. USE FOR: adding new resources to the knowledge base. DO NOT USE FOR: editing existing entries or restructuring sections.
kayba-ace
This skill ships learnfromtraces.py, a script that reads OpenClaw session transcripts, feeds them through the ACE learning pipeline, and writes an updated skillbook to disk.
distill-feedback
A process for turning corrections you give an agent into lasting working rules. It reviews saved conversations and asks for approval before changing those rules.
semantic-contracts-installer
Install Semantic Contracts (and, as a sub-mode, individual semantic anchors) into a coding agent's persistent context for Claude Code, Codex, Gemini CLI, Cursor, GitHub Copilot, and similar agents. Use when setting up project-level or user-level onboarding, generating or updating AGENTS.md / CLAUDE.md / GEMINI.md /…
hive-mind-advanced
Queen-led collective-intelligence coordination with consensus mechanisms and persistent shared memory. Use when orchestrating a large swarm of agents under a lead coordinator, building consensus across agents, or maintaining durable cross-agent memory.
lore
Curating cross-agent knowledge and institutional memory: extracts patterns from agent journals into METAPATTERNS.md, detects knowledge decay, propagates best practices. Use for memory curation.