Rashomon

Know whether your skills actually improve agent behavior — not just look different.

Why rashomon?

Inspired by the Rashomon effect — the idea that the same event can produce different outcomes depending on perspective. rashomon makes those differences explicit and comparable.

• Built a skill but unsure if it actually changes agent behavior?
• Iterating on skills and prompts by gut feel instead of evidence?
• Want repeatable evidence that your changes made things better, not just different?

rashomon evaluates skills and prompts through blind comparison — running tasks with and without your changes in isolated environments, then comparing real outputs without knowing which version produced which.

Who Is This For?

rashomon is designed for:

• Skill authors who want evidence-based validation
• Developers using Claude Code daily
• Teams iterating on complex prompts (coding, analysis, writing)
• Anyone who wants evidence, not vibes, when improving skills and prompts

Not ideal if:

• You want one-shot prompt rewriting without comparison

Quick Example

Skill Evaluation

/recipe-eval-skill create

Creates a skill through interactive dialog, then evaluates effectiveness:

1. Collects domain knowledge, project-specific rules, and trigger phrases
2. Generates optimized skill content (graded A/B/C)
3. Runs up to three valid paired trials with and without the skill, using at most five attempts, then compares the outputs blindly

What the evaluation report looks like:

Skill Quality: Grade A
- Project-specific rules clearly encoded, no critical issues

Trigger Check: pass (loaded through the Skill tool or its SKILL.md file)

Execution Effectiveness:
- Winner: with-skill
- Assessment: repeatable structural improvement across valid pairs
- Key difference: Retry constraints and three-stage catch ordering were applied
  consistently (linked to skill Rules 3 and 6)

Recommendation: ship

/recipe-eval-skill api-error-handling skill's scope needs adjustment

Updates an existing skill, then evaluates old vs new version side by side.

See a real-world example: I Built a Skill Reviewer. Then I Ran It on Itself.

Prompt Evaluation

/recipe-eval-prompt Write a function to sort an array

Analyzes prompt issues, generates an improved version, and runs the original and optimized prompts in up to three blind, paired trials. The report highlights differences that recur across trials.

What You Get

1. Analysis

- BP-002 (already satisfied): No consumer requirement selects a language,
  ordering, or error policy, so those choices remain flexible.

2. Final Prompt

Write a function to sort an array

Result: Original sufficient - Rashomon stops before paired execution because no outcome-relevant ambiguity was found.

Installation

Requires Claude Code (this is a Claude Code plugin)

# 1. Start Claude Code
claude

# 2. Install the marketplace
/plugin marketplace add shinpr/rashomon

# 3. Install plugin
/plugin install rashomon@rashomon

# 4. Restart session (required)
# Exit and restart Claude Code

Usage

Skill Evaluation

/recipe-eval-skill create

Create a new skill and evaluate its effectiveness.

/recipe-eval-skill my-skill-name what to change

Update an existing skill and compare old vs new.

Prompt Evaluation

/recipe-eval-prompt Your prompt here

From a file:

/recipe-eval-prompt Generate code following this skill: ./prompts/my-skill.md

For complex tasks that need more time, just mention it in natural language:

/recipe-eval-prompt Refactor the entire authentication module. This might take a while.

How It Works

Skill Evaluation (/recipe-eval-skill)
    ├── skill-creator (generates/modifies skills)
    ├── skill-reviewer (grades quality A/B/C)
    ├── eval-executor (up to 3 valid pairs, sequential within each pair)
    └── skill-eval-reporter (blind A/B comparison)

Prompt Evaluation (/recipe-eval-prompt)
    ├── prompt-analyzer (analyzes and optimizes)
    ├── prompt-executor (up to 3 valid pairs, parallel within each pair)
    └── report-generator (compares results)

Isolated Execution

rashomon uses git worktrees to run both versions in completely separate environments. A worktree is a Git feature that creates independent working directories from the same repository—this ensures the two executions don't interfere with each other.

Improvement Classification

Not all differences are improvements. rashomon classifies results into four categories:

Classification is based on:

• Whether detected issues were resolved
• Output completeness and constraint adherence
• Agreement between blind quality assessment and observable output differences

Both skill review and prompt analysis check against 8 common patterns:

P1: Critical (Must Fix)

P2: High Impact (Should Fix)

P3: Enhancement (Could Fix)

Prompt optimization keeps analysis, optimization, and balance state in one gated JSON response. It avoids intermediate analysis files, so the skill does not depend on a specific agent or filesystem layout.

Knowledge Base

rashomon learns from your project over time.

Location: .claude/.rashomon/prompt-knowledge.yaml

How it works:

• Automatically enabled when the file exists
• Stores project-specific patterns (not generic best practices)
• Referenced during analysis, updated after comparisons
• Max 20 entries, lowest-confidence ones removed first

Key principle: Old knowledge isn't automatically removed. Patterns that have worked for a long time are often the most valuable.

Troubleshooting

Leftover worktrees

If rashomon exits unexpectedly, temporary directories might remain:

# Worktrees are stored in system temp directory
# Clean up manually if needed:
rm -rf ${TMPDIR:-/tmp}/worktree-rashomon-*

Timeout issues

For complex prompts that need more time, mention it when invoking:

/recipe-eval-prompt Complex task here. This might take longer than usual.

"Not a git repository" error

rashomon requires a git repository. Initialize one with:

git init

Requirements

• Git 2.5+
• Python 3.9+
• Claude Code
• Must run inside a git repository

License

MIT