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agentic-jujutsu

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

50

1.15x
Quality

28%

Does it follow best practices?

Impact

88%

1.15x

Average score across 3 eval scenarios

SecuritybySnyk

Passed

No known issues

Optimize this skill with Tessl

npx tessl skill review --optimize ./.claude/skills/agentic-jujutsu/SKILL.md
SKILL.md
Quality
Evals
Security

Evaluation results

100%

Adaptive Deployment Tracker

Trajectory lifecycle and self-learning workflow

Criteria
Without context
With context

Correct import

100%

100%

Trajectory start called

100%

100%

Meaningful task descriptions

100%

100%

addToTrajectory called

100%

100%

Finalize with honest scores

100%

100%

Valid score range

100%

100%

Failure recorded with critique

100%

100%

getSuggestion JSON.parse

100%

100%

getLearningStats JSON.parse

100%

100%

Lifecycle ordering

100%

100%

Output file present

100%

100%

100%

36%

Distributed Code Analysis System

Multi-agent concurrent coordination

Criteria
Without context
With context

Concurrent execution

100%

100%

Per-agent JjWrapper

0%

100%

No lock-based sequencing

100%

100%

Agent trajectory lifecycle

60%

100%

Distinct agent descriptions

100%

100%

getSuggestion JSON.parse

0%

100%

queryTrajectories JSON.parse

0%

100%

Confidence logged

100%

100%

queryTrajectories result used

100%

100%

Output file present

100%

100%

65%

-1%

Artifact Integrity Verification System

Quantum security and operation tracking

Criteria
Without context
With context

Named quantum imports

0%

0%

Buffer input to fingerprint

0%

0%

verifyQuantumFingerprint usage

0%

0%

Tamper detection shown

80%

70%

Encryption enabled

100%

100%

Random base64 key

100%

100%

getUserOperations used

100%

100%

getStats JSON.parse

100%

100%

Output file present

100%

100%

Repository
ruvnet/agentic-flow
Evaluated
Agent
Claude Code
Model
Claude Sonnet 4.6

Table of Contents

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