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agentic-jujutsu

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

62

1.26x
Quality

Does it follow best practices?

Impact

81%

1.26x

Average score across 3 eval scenarios

SecuritybySnyk

Passed

No known issues

SKILL.md
Quality
Evals
Security

Quality

Content

50%

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

The body is highly actionable—rich in concrete, executable code and complete API tables—but it is also a verbose, monolithic document that repeats the same workflow many times and stuffs API reference, use cases, and examples into one file with no progressive disclosure. Workflow sequencing is present but lacks validation checkpoints for destructive VCS operations.

Suggestions

Collapse the repeated startTrajectory→addToTrajectory→finalizeTrajectory examples into one canonical walkthrough and remove marketing claims ('23x faster', '∞') to cut the body's length and redundancy.

Split the API reference tables, advanced use cases, and examples into separate reference files (e.g., API.md, EXAMPLES.md) and have SKILL.md link to them one level deep for proper progressive disclosure.

Add explicit validate→fix→retry checkpoints around destructive operations (merges, rebases, commits) so the workflow has real feedback loops.

DimensionReasoningScore

Conciseness

The ~640-line body repeats the same startTrajectory→addToTrajectory→finalizeTrajectory workflow across Quick Start, Core Capability 1, Use Cases 1/3/4, and Examples 1/2, padded with marketing claims ('23x faster than Git', '∞', 'Production Ready') and scattered version annotations not in a deprecation section. Not 2 because the redundancy and filler are pervasive rather than incidental.

1 / 3

Actionability

Provides concrete, executable JavaScript with real require statements, exact method signatures, realistic parameters, and complete API reference tables that are copy-paste ready. A few illustrative placeholder helpers (verifyDeployment(), executeTask()) exist in advanced examples but the library API itself is fully concrete. Not 2 because the bulk is genuinely executable rather than pseudocode.

3 / 3

Workflow Clarity

The learning trajectory sequence is clear and error-handling examples (try/catch → record failure) exist, but there are no explicit validate→fix→retry checkpoints for destructive or batch VCS operations like commits, merges, and rebases, which caps the score at 2. Not 3 because validation checkpoints are missing; not 1 because the sequence and error handling are present.

2 / 3

Progressive Disclosure

Section headers provide some organization and external doc links appear at the bottom, but it is a single monolithic 640-line file with no bundle files, and the API reference, advanced use cases, and examples that should be split out are all inline. Not 3 because there are no well-signaled one-level-deep references; not 1 because section structure is reasonable.

2 / 3

Total

8

/

12

Passed

Description

50%

Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.

The description states a clear domain and a few capabilities but is weighed down by buzzwords ('Quantum-resistant', 'ReasoningBank intelligence') and omits any explicit 'Use when...' trigger guidance. It answers 'what' adequately but not 'when', and trigger terms skew toward jargon over natural user language.

Suggestions

Add an explicit 'Use when...' trigger clause naming concrete situations (e.g., multiple AI agents editing the same codebase, resolving concurrent commit conflicts).

Replace buzzwords with concrete actions users would say: mention commits, branches, merges, and conflict resolution rather than 'ReasoningBank intelligence' and 'quantum-resistant'.

Include common natural variations such as 'git', 'version control', and 'merge conflicts' to improve trigger-term coverage and reduce overlap with generic VCS skills.

DimensionReasoningScore

Specificity

Names the domain ('version control') and qualities like 'self-learning' and 'multi-agent coordination', but lists few concrete actions (no commit/branch/merge) and leans on buzzwords ('Quantum-resistant', 'ReasoningBank intelligence'). Not 3 because it lacks multiple specific concrete actions; not 1 because it does name a domain and some actions.

2 / 3

Completeness

States what the skill does ('version control for AI agents...') but provides no 'Use when...' clause or explicit trigger guidance, which caps completeness at 2 per the rubric. Not 3 because the 'when' is entirely missing; not 1 because the 'what' is reasonably stated.

2 / 3

Trigger Term Quality

'version control' and 'AI agents' are natural user terms, but 'ReasoningBank intelligence', 'quantum-resistant', and 'multi-agent coordination' are jargon, and common variations like 'git', 'commits', 'merge conflicts', and 'branches' are absent. Not 3 due to missing common natural terms; not 1 because some relevant keywords are present.

2 / 3

Distinctiveness Conflict Risk

The AI-agent VCS niche is somewhat specific, but 'version control' overlaps generic git skills and there are no explicit distinct triggers to separate it. Not 3 because it could still overlap with similar VCS skills; not 1 because the multi-agent/ReasoningBank angle gives it a recognizable niche.

2 / 3

Total

8

/

12

Passed

Validation

87%

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation14 / 16 Passed

Validation for skill structure

CriteriaDescriptionResult

skill_md_line_count

SKILL.md is long (646 lines); consider splitting into references/ and linking

Warning

frontmatter_unknown_keys

Unknown frontmatter key(s) found; consider removing or moving to metadata

Warning

Total

14

/

16

Passed

Repository
ruvnet/claude-flow
Reviewed

Table of Contents

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