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ax-cpp-refine

Use when writing C++ code with `axllm` for reward-scored generation, iterative candidate improvement, evaluator feedback, and optimizer-backed refinement patterns.

65

Quality

78%

Does it follow best practices?

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SecuritybySnyk

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tessl review fix ./packages/cpp/skills/ax-cpp-refine/SKILL.md
SKILL.md
Quality
Evals
Security

Quality

Content

72%

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

A concise, well-structured reference skill with clear sections and signaled external materials. It is held back by a code pattern that uses undefined placeholders and by the lack of an explicitly sequenced refinement workflow with validation checkpoints.

Suggestions

Make the Core Pattern executable by showing how to construct reflection_client, options, request, and evaluator, or point to the specific example file that does.

Add an explicit sequenced workflow (e.g. build request → run optimizer → inspect evaluator feedback → iterate) with a checkpoint for confirming improvement before stopping.

Since the referenced bundle files (API.md, axir-api.json, examples/) are not present, confirm they ship with the skill or note where they live so the signaled navigation is resolvable.

DimensionReasoningScore

Conciseness

The body is lean and assumes competence: compact 'Package Facts' and 'Relevant API Surface' bullets and a minimal code pattern, with no padding explaining what C++ or libraries are.

3 / 3

Actionability

The Core Pattern gives a real C++ shape ('axllm::AxGEPA engine(...); auto result = engine.optimize(request, evaluator);') but the operands (reflection_client, options, request, evaluator) are undefined placeholders, so it is not copy-paste executable.

2 / 3

Workflow Clarity

When-to-use and Guardrails give implicit procedural direction, but the generate → evaluate → refine loop is not sequenced as explicit steps with validation checkpoints.

2 / 3

Progressive Disclosure

For a sub-50-line skill, the body is well-organized into clear sections and signals one-level-deep external materials (API.md, axir-api.json, axir-capabilities.json, examples/) by name.

3 / 3

Total

10

/

12

Passed

Description

85%

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 strong, third-person description that clearly states both capability and trigger condition with a specific niche. Its main weakness is trigger-term naturalness: several terms are technical jargon rather than phrasings a user would naturally say.

DimensionReasoningScore

Specificity

Lists multiple concrete actions — 'reward-scored generation, iterative candidate improvement, evaluator feedback, and optimizer-backed refinement patterns' — matching the anchor for several specific actions rather than vague language.

3 / 3

Completeness

Explicitly states both what ('reward-scored generation, iterative candidate improvement, evaluator feedback, and optimizer-backed refinement patterns') and when ('Use when writing C++ code with `axllm`').

3 / 3

Trigger Term Quality

'writing C++ code with `axllm`' is a natural trigger, but the remaining terms ('reward-scored generation', 'optimizer-backed refinement patterns') lean technical and miss common user phrasings like 'improve my C++ output'.

2 / 3

Distinctiveness Conflict Risk

The C++ + axllm + refinement niche is narrow and distinct, making it unlikely to trigger for unrelated skills.

3 / 3

Total

11

/

12

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.

Validation15 / 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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