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select-llm-model

Recommend which LLM to use for a task. Use when asked which model or best LLM for X, to pick a model, or for a shortlist ranked on live leaderboard evidence (coding, reasoning, agentic, tool-use, price, speed). Fetches leaderboards and cites them.

68

Quality

86%

Does it follow best practices?

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SecuritybySnyk

Low

Low-risk findings worth noting

SKILL.md
Quality
Evals
Security

Quality

Content

81%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 strong, highly executable multi-phase workflow with explicit fail-fast validation and stop conditions. Its weaknesses are repetition of the no-fabrication rules across sections, an inline dated incident note, and a stale reference to non-existent test fixture files.

Suggestions

Consolidate the fail-fast / never-fabricate / don't-dismiss-fetched-rows rules into one canonical section (Operating principles) and reference it from Phases 0, 2, and 4 instead of restating it.

Move the "(2026-09-06: one run set the workdir...)" incident note into a short 'Known failure modes' or notes subsection so time-sensitive detail doesn't pad the operational steps.

Fix the Scope section's reference to scripts/parse-*_test.ts (no such files exist in the bundle) and consider moving the source catalog / known-gaps list into a references/ file to shorten SKILL.md.

DimensionReasoningScore

Conciseness

The body is mostly operational, but the fail-fast/never-fabricate rule is restated in Operating principles, Phase 0, Phase 2, and again in Phase 4 ("'these look like fixtures'... are not findings"), and the inline dated incident "(2026-09-06: one run set the workdir...)" is time-sensitive detail outside any notes/deprecated section. Several passages could be tightened or deduplicated.

3 / 5

Actionability

Fully executable guidance throughout: copy-paste "curl -fsSL -H \"x-api-key: $AA_API_KEY\" https://artificialanalysis.ai/api/v2/data/llms/models | deno run -A scripts/benchmarks.ts scores --category coding --top 10 --stdin" commands with exact URLs, flags, and output row schemas, plus a category→benchmark mapping table covering the common cases.

5 / 5

Workflow Clarity

Phases 0–4 are clearly sequenced with explicit validation checkpoints and feedback loops: non-zero exit → record as Gap, missing deno/AA_API_KEY → Gap, "If EVERY source's fetch fails this way, STOP — do not produce a ranking", plus an ordered five-part report checklist.

5 / 5

Progressive Disclosure

Well-sectioned, one-level structure whose referenced scripts (benchmarks.ts, openrouter.ts) exist in the bundle, but everything — source catalog, known-gaps list, category table — is inlined in a ~230-line SKILL.md with no reference docs, and the Scope section cites "parser fixtures inlined in scripts/parse-*_test.ts" which do not exist in the bundle.

4 / 5

Total

17

/

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 description: third-person, concrete, with explicit 'Use when' triggers and specific natural keywords. It clearly communicates both capability and activation conditions with minimal conflict risk.

DimensionReasoningScore

Specificity

"Recommend which LLM to use for a task", "shortlist ranked on live leaderboard evidence (coding, reasoning, agentic, tool-use, price, speed)", "Fetches leaderboards and cites them" — several concrete actions with minor gaps (no mention of timestamp/gap disclosure). Not 3: more than 1–2 actions; not 5: coverage of the capability set is incomplete.

4 / 5

Completeness

Explicitly answers what ("Recommend which LLM to use for a task... Fetches leaderboards and cites them") and when ("Use when asked which model or best LLM for X, to pick a model, or for a shortlist ranked on live leaderboard evidence") with concrete trigger phrases — a clean match for the top anchor.

5 / 5

Trigger Term Quality

Natural user phrases like "which model", "best LLM for X", "to pick a model", "shortlist", "leaderboard" are present. Not 5: misses common variations such as "recommend a model" or "model comparison"; not 3: keyword coverage is good rather than partial.

4 / 5

Distinctiveness Conflict Risk

A clear niche — live-leaderboard-evidence model recommendation — with distinct, specific triggers unlikely to fire for unrelated skills. Not 4: the trigger phrases are scoped tightly enough that overlap risk is minimal.

5 / 5

Total

18

/

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

referenced_paths_exist

Referenced path issues: 1 missing

Warning

Total

15

/

16

Passed

Repository

korchasa/flowai-plugins

Path

plugins/flowai-beta/skills/select-llm-model/SKILL.md

Reviewed

Table of Contents

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