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agentic-jujutsu

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

Install with Tessl CLI

npx tessl i github:ruvnet/claude-flow --skill agentic-jujutsu
What are skills?

49

1.26x

Does it follow best practices?

Evaluation81%

1.26x

Agent success when using this skill

Validation for skill structure

SKILL.md
Review
Evals

Evaluation results

100%

1%

Automated Deployment Pipeline with Learning

Self-learning trajectory workflow

Criteria
Without context
With context

Correct import

100%

100%

startTrajectory called

100%

100%

addToTrajectory called

100%

100%

finalizeTrajectory called

100%

100%

Meaningful task descriptions

87%

100%

Varied honest scores

100%

100%

Failure recorded with details

100%

100%

getSuggestion usage

100%

100%

getLearningStats usage

100%

100%

Operations before finalizing

100%

100%

output.json written

100%

100%

Without context: $0.8359 · 2m 48s · 36 turns · 72 in / 9,893 out tokens

With context: $1.2079 · 4m 13s · 47 turns · 342 in / 15,031 out tokens

88%

3%

Parallel Feature Branch Management

Multi-agent concurrent coordination

Criteria
Without context
With context

Concurrent execution

100%

100%

Separate JjWrapper per agent

100%

100%

Branch naming convention

0%

0%

queryTrajectories usage

100%

100%

getPatterns usage

100%

100%

Trajectory lifecycle per agent

100%

100%

No lock-waiting pattern

100%

100%

Descriptive trajectory names

70%

100%

results.json written

100%

100%

Without context: $1.8001 · 6m 31s · 59 turns · 65 in / 21,928 out tokens

With context: $1.1455 · 3m · 43 turns · 206 in / 9,307 out tokens

56%

47%

Secure Artifact Integrity Verification System

Quantum security and operation auditing

Criteria
Without context
With context

Quantum fingerprint import

0%

0%

generateQuantumFingerprint usage

0%

0%

verifyQuantumFingerprint usage

0%

0%

Encryption enabled

0%

100%

Base64 key generation

0%

100%

getStats usage

0%

100%

getOperations usage

0%

100%

Trajectory lifecycle complete

0%

100%

audit-report.json written

100%

100%

Fingerprint as hex

0%

0%

Without context: $0.8302 · 2m 39s · 26 turns · 1,801 in / 8,792 out tokens

With context: $1.4451 · 4m 9s · 44 turns · 49 in / 13,434 out tokens

Evaluated
Agent
Claude Code
Model
Unknown

Table of Contents

Is this your skill?

If you maintain this skill, you can claim it as your own. Once claimed, you can manage eval scenarios, bundle related skills, attach documentation or rules, and ensure cross-agent compatibility.