agentic-jujutsu

Quantum-resistant, self-learning version control for AI agents with ReasoningBank intelligence and multi-agent coordination

1.26x

Quality

21%

Does it follow best practices?

Impact

81%

1.26x

Average score across 3 eval scenarios

Securityby

Passed

No known issues

Optimize this skill with Tessl

npx tessl skill review --optimize ./.agents/skills/agentic-jujutsu/SKILL.md

Evaluation results

100%

Automated Deployment Pipeline with Learning

Self-learning trajectory workflow

Criteria

Without context

With context

Correct import

100%

startTrajectory called

100%

addToTrajectory called

100%

finalizeTrajectory called

100%

Meaningful task descriptions

87%

100%

Varied honest scores

100%

Failure recorded with details

100%

getSuggestion usage

100%

getLearningStats usage

100%

Operations before finalizing

100%

output.json written

100%

88%

Parallel Feature Branch Management

Multi-agent concurrent coordination

Criteria

Without context

With context

Concurrent execution

100%

Separate JjWrapper per agent

100%

Branch naming convention

queryTrajectories usage

100%

getPatterns usage

100%

Trajectory lifecycle per agent

100%

No lock-waiting pattern

100%

Descriptive trajectory names

70%

100%

results.json written

100%

56%

47%

Secure Artifact Integrity Verification System

Quantum security and operation auditing

Criteria

Without context

With context

Quantum fingerprint import

generateQuantumFingerprint usage

verifyQuantumFingerprint usage

Encryption enabled

100%

Base64 key generation

100%

getStats usage

100%

getOperations usage

100%

Trajectory lifecycle complete

100%

audit-report.json written

100%

Fingerprint as hex

Repository: ruvnet/ruflo
Commit: 398f7c2

Evaluated: about 1 month ago
Agent: Claude Code
Model: Claude Sonnet 4.6

Table of Contents

Automated Deployment Pipeline with Learning Parallel Feature Branch Management Secure Artifact Integrity Verification System

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