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ai-product

Design and evaluate LLM product features, retrieval, validated outputs, latency, and cost controls.

Quality

65%

Does it follow best practices?

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Adds up to 20 points to the overall score

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SecuritybySnyk

Passed

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tessl review fix ./skills/ai-product/SKILL.md
SKILL.md
Quality
Evals
Security

Quality

Content

65%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 an exemplar of lean progressive disclosure — a minimal overview that defers cleanly to a single self-contained reference — but in doing so it provides almost no standalone actionable content; every concrete step lives in the guide. Workflow clarity is adequate for entry but depends entirely on the reference.

Suggestions

Inline a small amount of executable guidance in SKILL.md (e.g. one quick-start pattern from the guide such as the structured-output validation snippet or the circuit-breaker skeleton) so the body is not purely a pointer.

Name the guide's major sections in the Detailed Guide section (e.g. Patterns, Sharp Edges) so focused loading is navigable without opening the file.

Add one explicit validation checkpoint language in the body itself (e.g. 'Validate LLM outputs before shipping; see Sharp Edges') rather than only mandating the guide's requirements abstractly.

DimensionReasoningScore

Conciseness

The ~20-line body is lean with zero padding or explanation of concepts Claude already knows — 'Choose AI only when it improves the target user task against an evaluated baseline' plus a one-paragraph scope summary, then a pointer to the detailed guide. Every token earns its place, matching the score-5 anchor; even the mild flavor in 'cost optimization that doesn't bankrupt you' is compact, not verbose.

5 / 5

Actionability

The body's own guidance is a high-level directive plus a navigation instruction ('Read the detailed guide before executing this skill... Treat its safety, prerequisites, and validation requirements as mandatory') — no concrete steps, commands, examples, or decision procedures are inlined, matching 'minimal concrete guidance; high-level hints but missing the specific steps to execute'. It is above score 1 because it names the exact file to load and states an unambiguous precondition, but below score 3, which expects at least some concrete in-body guidance.

2 / 5

Workflow Clarity

The body defines an entry sequence (apply the baseline decision rule, read the guide before executing, load sections for focused work vs. fully for end-to-end, escalate on missing inputs per Limitations) but the actual operational workflow and validation checkpoints live entirely in the reference file — the body has no explicit checkpoints of its own, matching 'sequence present but checkpoints missing or implicit'. Not score 4: no validation step is visible in the body itself, only a mandate that the guide's requirements be treated as mandatory.

3 / 5

Progressive Disclosure

The body is a clean overview with a dedicated 'Detailed Guide' section signaling one clearly-linked reference (references/detailed-guide.md, verified to exist and self-contained at 755 lines with no nested references), plus well-organized 'When to Use' and 'Limitations' sections. This matches the score-5 anchor 'clear overview with well-signaled one-level-deep references; content appropriately split; easy navigation'.

5 / 5

Total

15

/

20

Passed

Description

50%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 has a clear 'what' scoped to the LLM product domain, but it lacks any 'when to use' trigger guidance, and its terms are somewhat abstract with missing natural synonyms (RAG, prompts). It reads as a domain inventory rather than an activation-rich description.

Suggestions

Add an explicit 'Use when...' clause with concrete triggers, e.g. 'Use when building or evaluating LLM-powered features, RAG pipelines, prompt systems, or AI product cost/latency budgets.'

Include natural synonyms and phrasings users would say — 'RAG', 'prompt engineering', 'chatbot', 'AI feature', 'evals' — instead of the abstract noun list 'retrieval, validated outputs, latency'.

Lead with concrete actions per capability area (e.g. 'Design RAG architectures with hybrid search, validate LLM outputs with function calling, set per-request cost limits') to lift specificity.

DimensionReasoningScore

Specificity

The description names the domain ('LLM product features') and two verbs ('Design and evaluate'), but the remaining items ('retrieval, validated outputs, latency, and cost controls') are abstract nouns rather than concrete actions, matching the anchor 'names domain and 1-2 concrete actions, but not comprehensive'. It falls below the score-4 anchor, whose example lists several specific executable actions with direct objects.

3 / 5

Completeness

The 'what' is stated clearly ('Design and evaluate LLM product features, retrieval, validated outputs, latency, and cost controls') but there is no 'when' clause at all — no 'Use when...' or equivalent trigger guidance. Per the judging guidelines, a missing 'Use when' clause caps completeness at 3, matching the anchor 'Has a clear what but when is missing or only weakly implied'.

3 / 5

Trigger Term Quality

Terms like 'LLM', 'retrieval', 'latency', and 'cost' are relevant, but common natural variations users would actually say are missing — 'RAG' (it says only 'retrieval'), 'prompt', 'AI feature', 'chatbot', 'evaluation/benchmarking'. This matches 'some relevant keywords but missing common variations or synonyms', not score 4's 'good keyword coverage'.

3 / 5

Distinctiveness Conflict Risk

The LLM-product niche is somewhat specific, but the broad scope (retrieval, prompts, UX, cost) overlaps with dedicated RAG, prompt-engineering, and evaluation skills that would trigger on the same requests, matching 'somewhat specific but could still overlap with similar skills'. Not score 4, since overlap risk extends beyond 'closely related skills'.

3 / 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

sickn33/agentic-awesome-skills

Path

skills/ai-product/SKILL.md

Reviewed

Table of Contents

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