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ax-cpp-agent-optimize

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

67

Quality

84%

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SKILL.md
Quality
Evals
Security

Quality

Content

76%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 tight, well-organized reference-style skill body with excellent token efficiency and mostly actionable API guidance. Its main gap is the absence of an explicit step-by-step optimization workflow with validation checkpoints — the content tells you what exists and what to respect, but not in what order to do things or how to verify each stage.

Suggestions

Add a short numbered workflow (e.g., 1. pick an example as a template, 2. construct AxGEPA with an evaluator callback, 3. run optimize() within an explicit budget, 4. verify playbook proposals pass the verification gate, 5. persist optimizer artifacts) so the optimize→evaluate→persist sequence has explicit checkpoints instead of being implied across sections.

Make the core pattern snippet closer to copy-paste ready by showing how `reflection_client`, `options`, `request`, and `evaluator` are constructed, or by naming the specific example file in `examples/` that contains the full working version.

Move the "Relevant API Surface" symbol list into a reference file (or the already-mentioned API.md) and keep only the 2–3 most-used symbols inline, tightening progressive disclosure to a clean overview-plus-pointers shape.

DimensionReasoningScore

Conciseness

The body is lean and fact-dense — bullets like "Real network support: yes" and "Treat AxIR as the source of generated package truth" assume Claude's competence with zero padding or explanation of known concepts. Every section earns its tokens, matching the anchor-5 example of lean efficiency.

5 / 5

Actionability

A concrete call-shape snippet ("axllm::AxGEPA engine(reflection_client, options); auto result = engine.optimize(request, evaluator);") plus an explicit API symbol list and the guardrail "Start from package examples for exact native syntax" give mostly executable guidance per anchor 4. Not 5 because the snippet contains unresolved placeholders (reflection_client, request, evaluator) and no includes or construction steps, so it is not copy-paste ready.

4 / 5

Workflow Clarity

The body organizes facts and guardrails rather than a sequenced workflow; the "When To Use" tasks (optimize, mine weaknesses, persist artifacts, bound runs) are listed but any validate-then-proceed sequence is only implicit (e.g., "keep only playbook proposals that pass the verification gate"). This matches anchor 3 — sequence/checkpoints present but implicit — and since optimization runs are batch-style operations lacking explicit validation steps, workflow clarity is capped at 3 by the rubric guideline.

3 / 5

Progressive Disclosure

Good structure for a short skill: clearly labeled one-level pointers ("Package API docs: `API.md` and `axir-api.json`", "Runnable examples: `examples/`") and well-organized sections match anchor 4. Not 5 because no in-skill reference files exist (the pointers target external package docs), and the inline "Relevant API Surface" symbol list is material that could live in a separate reference file — minor organization gaps.

4 / 5

Total

16

/

20

Passed

Description

87%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 strong, concise description with an explicit "Use when..." trigger clause, concrete package-specific keywords, and a clearly bounded niche. Its only weakness is that the capability list leans on topic nouns (evaluators, judges, artifacts) rather than stated actions, which slightly blurs the 'what does this do' picture.

DimensionReasoningScore

Specificity

The description lists several specific capability items — "agent optimization, verified agent-playbook evolution, evaluators, judges, optimizer artifacts, BootstrapFewShot, and GEPA" — matching the anchor for several specific actions with minor gaps. It falls short of 5 because the items are topic nouns rather than explicitly stated actions, and below 4 would understate the breadth of concrete capabilities named.

4 / 5

Completeness

It explicitly answers both questions: the "Use when writing C++ code with `axllm` for agent optimization..." clause is a concrete, explicit when-trigger, and the enumerated topics (playbook evolution, evaluators, judges, optimizer artifacts) state the what. The when clause is as specific as the anchor-5 exemplar, so it does not fit the anchor-4 case where 'when could be more explicit'.

5 / 5

Trigger Term Quality

"C++ code", "axllm", "agent optimization", "BootstrapFewShot", and "GEPA" are natural terms a user needing this skill would say, giving good keyword coverage per the anchor-4 example. Not 5 because there are no synonyms or common phrasings (e.g., "few-shot optimization", "AxAgent", "playbook mining") that would make coverage comprehensive.

4 / 5

Distinctiveness Conflict Risk

The C++/`axllm`/BootstrapFewShot/GEPA niche is highly specific with distinct trigger terms, so the risk of firing for the wrong skill is minimal — a clear match for anchor 5. Anchor 4 ('minor overlap risk') would understate how uniquely identifying these package-specific terms are.

5 / 5

Total

18

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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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