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llm-application-dev-prompt-optimize

You are an expert prompt engineer specializing in crafting effective prompts for LLMs through advanced techniques including constitutional AI, chain-of-thought reasoning, and model-specific optimizati

40

Quality

51%

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tessl review fix ./plugins/AI-Agents-Safe-Coding-Skills-claude/skills/llm-application-dev-prompt-optimize/SKILL.md

The canonical home for this skill is llm-application-dev-prompt-optimize in rmyndharis/antigravity-skills

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 well-sectioned stub: it stays lean but offers no actionable prompt-engineering guidance, only generic directives plus a dangling reference to a nonexistent playbook file. Claude loading this skill would gain essentially no domain-specific instructions.

Suggestions

Inline at least one concrete before/after prompt example (e.g. a weak instruction transformed into a structured prompt with role, constraints, and output format) so the skill delivers value without external files.

Replace the abstract instruction bullets with a concrete step sequence and an explicit validation checkpoint (e.g. "rewrite the prompt, then test it against the stated success criteria and iterate if it fails").

Either create `resources/implementation-playbook.md` with the promised detailed patterns and examples, or remove the dangling reference; also drop the duplicated description line and the unverified 40%/30%/50-80% statistics.

DimensionReasoningScore

Conciseness

The body is short and mostly efficient, but the opening line duplicates the frontmatter description verbatim, the Context section is unverified marketing statistics ("improve accuracy by 40%, reduce hallucinations by 30%, cut costs by 50-80%"), and the 'Do not use this skill when' bullets are vacuous ("The task is unrelated to prompt optimization"). This matches anchor 3: mostly efficient with some unnecessary content that could be tightened.

3 / 5

Actionability

The Instructions are pure high-level hints ("Clarify goals, constraints, and required inputs. Apply relevant best practices and validate outcomes.") with no concrete technique, example, or command — matching anchor 2. The single substantive pointer, `resources/implementation-playbook.md`, targets a file that does not exist in the bundle, so no concrete guidance is actually reachable.

2 / 5

Workflow Clarity

A rough implicit sequence exists (clarify → apply best practices → validate → deliver), but the steps are poorly defined generic directives with no checkpoints or error-handling, matching anchor 2 ("rough sequence present but many gaps; steps poorly defined; validation absent"). Not 3: anchor 3 requires concrete listed steps; these are abstractions, and 'validate outcomes' names no actual check.

2 / 5

Progressive Disclosure

Sections are well organized and the playbook reference is clearly signaled in a Resources section, but the referenced `resources/implementation-playbook.md` does not exist (no resources/, references/, scripts/, or assets/ directories are present), its mention is duplicated in Instructions, and it uses a nonstandard path. All substance is deferred to a dead end, matching anchor 2. Not 3: the reference isn't merely imperfectly signaled — following it fails entirely.

2 / 5

Total

9

/

20

Passed

Description

48%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.

The description identifies a clear niche but is undermined by second-person voice, buzzword-heavy capability framing, a missing 'Use when' trigger clause, and a truncated final word. It reads as a persona statement rather than a capability-plus-trigger description.

Suggestions

Rewrite in third person and lead with concrete actions, e.g. "Optimizes and rewrites prompts for LLMs using constitutional AI, chain-of-thought reasoning, and model-specific techniques."

Append an explicit trigger clause: "Use when the user asks to improve, optimize, or rewrite a prompt, or mentions prompt engineering, system prompts, or model-specific tuning."

Fix the truncation ("...model-specific optimizati") and replace vague claims like "advanced techniques" with the named techniques only.

DimensionReasoningScore

Specificity

The description names the domain and one action ("crafting effective prompts") plus technique buzzwords ("constitutional AI, chain-of-thought reasoning"), roughly matching anchor 3, but it is written in second person ("You are an expert prompt engineer"), which the judging guidelines penalize by reducing specificity by 1. The text also cuts off mid-word ("optimizati"), leaving the capability list incomplete.

2 / 5

Completeness

The 'what' is present ("crafting effective prompts for LLMs through advanced techniques") but there is no 'Use when...' clause or equivalent trigger guidance, which the guidelines cap at 3. Not 4: 'when' is entirely absent, not merely imprecise.

3 / 5

Trigger Term Quality

"prompt", "prompts", "LLM", and "chain-of-thought" are terms users naturally say, matching anchor 3, but common variations like "improve my prompt", "refine prompt", or "prompt engineering" as a trigger phrase are missing. Not 4: the everyday phrasings a user would actually type when needing this skill are largely absent.

3 / 5

Distinctiveness Conflict Risk

Prompt engineering is a fairly distinct niche with named techniques (constitutional AI, chain-of-thought, model-specific optimization), so overlap risk is minor — matching anchor 4. Not 5: the phrasing "prompts for LLMs" is broad enough that adjacent writing/API-prompting skills could compete for the same triggers.

4 / 5

Total

12

/

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

administrakt0r/AI-Agents-Safe-Coding-Skills

Path

plugins/AI-Agents-Safe-Coding-Skills-claude/skills/llm-application-dev-prompt-optimize/SKILL.md

Reviewed

Table of Contents

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