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/agentic-flow --skill agentic-jujutsu41
Does it follow best practices?
If you maintain this skill, you can automatically optimize it using the tessl CLI to improve its score:
npx tessl skill review --optimize ./path/to/skillValidation for skill structure
Trajectory lifecycle and self-learning workflow
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%
Without context: $0.7846 · 2m · 33 turns · 10,210 in / 6,398 out tokens
With context: $1.0209 · 2m 36s · 35 turns · 39 in / 9,177 out tokens
Multi-agent concurrent coordination
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%
Without context: $3.4967 · 8m 15s · 94 turns · 274 in / 27,553 out tokens
With context: $0.5167 · 1m 29s · 25 turns · 288 in / 4,709 out tokens
Quantum security and operation tracking
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%
Without context: $0.8645 · 2m 31s · 33 turns · 39 in / 8,182 out tokens
With context: $1.1995 · 3m 22s · 39 turns · 303 in / 12,466 out tokens
Table of Contents
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.