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ax-rust-agent-optimize

Use when writing Rust code with `axllm` for agent optimization, verified agent-playbook evolution, evaluators, judges, optimizer artifacts, BootstrapFewShot, and GEPA.

65

Quality

82%

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SKILL.md
Quality
Evals
Security

Quality

Content

80%Weight 40%Scale 1-5

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

A compact, well-organized overview that respects the token budget and points clearly at the package's own docs and examples. Its main gaps are the placeholder-based core pattern and the absence of an ordered workflow with a verification checkpoint (e.g., no-key check before live runs).

Suggestions

Add a short ordered workflow, e.g.: 1. check `examples/` for the exact call shape, 2. confirm capabilities in `axir-capabilities.json`, 3. write code, 4. verify with the no-key transport before real network runs.

Make the core pattern concrete by adding the key imports/types or pointing to one specific example file that implements the AxGEPA pattern.

State what 'passes the verification gate' means for playbook proposals (the check to run and what to do on failure) so the verification loop is actionable.

DimensionReasoningScore

Conciseness

The body is lean and factual — bullet-style 'Package Facts', a two-line 'Core Pattern', a terse API list, and guardrails — with no explanation of concepts Claude already knows and no padding; every token earns its place.

5 / 5

Actionability

The 'Core Pattern' snippet ('let engine = axllm::AxGEPA::new(reflection_client, options)?; let result = engine.optimize(request, evaluator)?;') gives a concrete call shape and the 'Relevant API Surface' lists exact symbols, but the snippet has undefined placeholders rather than copy-paste-ready code. That matches 'mostly executable guidance with minor gaps' rather than the fully runnable common-case coverage of a 5.

4 / 5

Workflow Clarity

There is no ordered sequence: guidance is scattered across 'When To Use' bullets and guardrails ('Start from package examples... before inventing a new call shape') with no explicit steps or validation checkpoints (e.g., verify with the no-key transport before real network runs). It sits between 'steps listed but checkpoints missing' (3) and the unambiguous single-action case (5), and the guardrail sequencing alone does not establish a clear workflow.

3 / 5

Progressive Disclosure

The body is under 50 lines with no skill-bundle reference files needed; it is cleanly organized into well-labeled sections and its file pointers ('API.md', 'axir-capabilities.json', 'examples/') point one level deep into the target `axllm` package rather than nesting skill references. Per the simple-skill guidance, well-organized sections with no need for external references score 5.

5 / 5

Total

17

/

20

Passed

Description

78%Weight 40%Scale 1-5

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

A tight, trigger-first description with strong domain-specific keywords and virtually no conflict risk. Its main weakness is that the 'what' is only implied inside the 'use when' clause and it enumerates objects/topics rather than concrete actions the skill performs.

Suggestions

Open with a brief third-person capability statement before the trigger clause, e.g. 'Guides writing and optimizing Rust agents with the `axllm` package: evaluators, judges, playbook evolution, optimizer artifacts.'

Convert the topic list into concrete actions (e.g., 'create evaluator callbacks', 'run BootstrapFewShot/GEPA optimization', 'persist optimizer artifacts') to lift specificity.

Add one or two natural user phrasings such as 'optimize an AxAgent' or 'prompt/agent optimization' to broaden trigger coverage.

DimensionReasoningScore

Specificity

The description names several concrete items — "evaluators, judges, optimizer artifacts, BootstrapFewShot, and GEPA" — with a specific action ("writing Rust code with `axllm`") but minor gaps; it lists topics/objects more than distinct actions, so it does not reach the comprehensive multi-action coverage of a 5.

4 / 5

Completeness

The 'when' is fully explicit ("Use when writing Rust code with `axllm` for agent optimization...") and a 'what' is embedded (guides writing Rust code for these optimization tasks), matching the anchor where both are present but one could be more explicit. It is not a 5 because the capabilities are never stated as a distinct what-clause, and not a 2 because the enumerated task list conveys substantive scope beyond a bare 'use when'.

4 / 5

Trigger Term Quality

Good natural keyword coverage for this domain — "Rust", "agent optimization", "evaluators", "judges", "playbook", "BootstrapFewShot", "GEPA" — but a few plausible user terms (e.g., "optimize an AxAgent", "reflection", "prompt optimization") are missing, so it falls short of the comprehensive synonym coverage of a 5.

4 / 5

Distinctiveness Conflict Risk

A clear niche with distinct triggers — Rust + `axllm` + "BootstrapFewShot" + "GEPA" — that virtually no other skill would claim; minimal conflict risk.

5 / 5

Total

17

/

20

Passed

Validation

93%

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

Validation — 15 / 16 Passed

Validation for skill structure

CriteriaDescriptionResult

frontmatter_unknown_keys

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

Warning

Total

15

/

16

Passed

Repository
ax-llm/ax
Reviewed

Table of Contents

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