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llm-application-dev-prompt-optimize

Evaluate and improve LLM prompts using examples, structured instructions, and measured comparisons. Use when refining prompt reliability, output quality, or model-specific behavior.

Quality

63%

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tessl review fix ./skills/llm-application-dev-prompt-optimize/SKILL.md
SKILL.md
Quality
Evals
Security

Quality

Content

32%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.

The body is a thin, generic shell: it sketches a rough workflow but provides no concrete prompt-engineering patterns, examples, or evaluation methods, and its single detailed reference points to a nonexistent file. Padding (role assignment, unsourced impact statistics, vacuous trigger sections) occupies space that actionable guidance should fill.

Suggestions

Replace circular instructions like "Apply relevant best practices and validate outcomes" with concrete techniques: e.g., before/after prompt examples, a few-shot pattern template, and a specific comparison method (run both prompt variants on N test cases and compare outputs).

Fix the broken progressive disclosure: either create resources/implementation-playbook.md with the promised "detailed patterns and examples" or inline the essential patterns directly in SKILL.md and remove the dangling reference.

Remove non-instructional padding — the "You are an expert prompt engineer" role sentence, the unsourced 40%/30%/50-80% statistics, and the "Do not use this skill when" bullets that restate the description — and add an explicit validation checkpoint (e.g., how to verify an optimized prompt actually outperforms the original before delivering it).

DimensionReasoningScore

Conciseness

The body is short overall, but contains unnecessary padding: the role-play opener "You are an expert prompt engineer specializing in... constitutional AI, chain-of-thought reasoning", the unsourced marketing statistics ("improve accuracy by 40%, reduce hallucinations by 30%, and cut costs by 50-80%"), and vacuous exclusions like "The task is unrelated to prompt optimization". Mostly efficient with sections that should be trimmed — anchor 3.

3 / 5

Actionability

The guidance is circular — "Apply relevant best practices and validate outcomes" and "Provide actionable steps and verification" restate the task rather than instruct how to do it, with no concrete prompt patterns, examples, or comparison methodology. The only concrete pointer ("open resources/implementation-playbook.md") targets a file that does not exist in the bundle, matching anchor 2's minimal high-level guidance missing specific executable steps.

2 / 5

Workflow Clarity

A rough sequence exists (clarify → apply → provide), but the core step is a placeholder ("Apply relevant best practices" is the entire task restated) and validation is named with no method or checkpoint, fitting anchor 2's rough sequence with poorly defined steps and effectively absent validation. The simple-skill exception does not apply since prompt optimization is inherently multi-step.

2 / 5

Progressive Disclosure

The body defers all detailed content to "resources/implementation-playbook.md", but that file does not exist anywhere in the bundle (no resources/ directory), so the one-level-deep reference is dangling and the disclosure chain is broken at its only link. Body space is also spent on "Use this skill when / Do not use this skill when" sections that duplicate the description's job, matching anchor 2's minimal functional structure.

2 / 5

Total

9

/

20

Passed

Description

75%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 solid description: it uses third-person voice, names the domain with several concrete techniques, and includes an explicit "Use when" clause covering three trigger conditions. Trigger coverage and the what/when pairing are good, though common synonyms ("prompt engineering", "rewrite a prompt") and more user-mention-style trigger phrases would strengthen it further.

DimensionReasoningScore

Specificity

"Evaluate and improve LLM prompts using examples, structured instructions, and measured comparisons" names the domain, two actions, and three enumerated techniques, matching anchor 4 (several specific actions with minor gaps); it exceeds anchor 3's 1-2 concrete actions but lacks anchor 5's comprehensive action coverage.

4 / 5

Completeness

Both parts are explicit: what = "Evaluate and improve LLM prompts using examples, structured instructions, and measured comparisons", when = "Use when refining prompt reliability, output quality, or model-specific behavior". The when-clause lists concrete goal-conditions but not the user-mention trigger phrases of anchor 5, and "model-specific behavior" is comparatively fuzzy, so anchor 4 is the best fit.

4 / 5

Trigger Term Quality

Natural terms like "LLM prompts", "refining", "prompt reliability", and "output quality" give good keyword coverage (anchor 4), but common variations such as "prompt engineering", "rewrite/tune a prompt", or "system prompt" are missing, so it falls short of anchor 5's comprehensive synonym coverage.

4 / 5

Distinctiveness Conflict Risk

The prompt-optimization niche is mostly distinct with specific scoped triggers ("prompt reliability", "output quality", "model-specific behavior"), matching anchor 4's minor overlap risk with closely related skills (e.g., LLM API/prompting skills); it does not reach anchor 5's sharply distinct trigger set.

4 / 5

Total

16

/

20

Passed

Validation

100%

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

Validation — 16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository

rmyndharis/antigravity-skills

Path

skills/llm-application-dev-prompt-optimize/SKILL.md

Reviewed

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