select-llm-model

Recommend which LLM model to use for a task. Use when asked "which model / best LLM for X", "pick a model for this task", or for a model shortlist ranked by live leaderboard evidence (coding, reasoning, agentic, tool-use, price, speed). Live-fetches public leaderboards and ranks models with per-axis rationale and citations.

Quality

88%

Does it follow best practices?

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Securityby

Low

Low-risk findings worth noting

Quality

Content

88%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-sequenced, highly actionable workflow with explicit validation and fail-fast checkpoints, and its script references are real and one-level-deep. It leans slightly verbose in the fail-fast prohibitions and inlines detail that could live in separate reference files.

Suggestions

Consolidate the repeated fail-fast/curl-workaround prohibitions into a single concise rule to trim token cost.

Move the 'Known Gaps' catalog and per-source notes into a separate reference file referenced one level deep from SKILL.md to improve progressive disclosure.

Dimension	Reasoning	Score
Conciseness	Largely efficient and assumes competence, but the fail-fast / curl-workaround prohibitions repeat the same guidance with multiple enumerated alternatives that could be trimmed, fitting anchor 4 rather than 5.	4 / 5
Actionability	Provides copy-paste-ready `curl ... \| deno run ... --stdin` commands with full flags, output row schemas, and subcommand specs covering the common cases, matching anchor 5.	5 / 5
Workflow Clarity	Phases 0-4 are clearly sequenced with explicit validation checkpoints (fail-fast tooling check, gap recording, STOP-when-all-fail) and feedback loops for error recovery, matching anchor 5.	5 / 5
Progressive Disclosure	Structure is good and the referenced scripts (benchmarks.ts, openrouter.ts) exist and are one-level-deep, but substantial detail (source notes, known gaps, the full command catalog) is inlined in SKILL.md rather than split into separate reference files, fitting anchor 4.	4 / 5
	Total	18 / 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.

The description is strong: it pairs a clear statement of what the skill does with explicit, natural trigger phrases, and occupies a distinctive niche. Minor specificity and synonym coverage gaps keep it from being maximal on those two axes.

Dimension	Reasoning	Score
Specificity	Lists several concrete actions ('ranks models with per-axis rationale and citations', 'Live-fetches public leaderboards') but stops short of the comprehensive coverage of anchor 5.	4 / 5
Completeness	Explicitly answers both what (recommend/rank models from live leaderboards with per-axis rationale) and when ('Use when asked...') with concrete trigger phrases, matching anchor 5.	5 / 5
Trigger Term Quality	Includes natural user phrasings ('which model / best LLM for X', 'pick a model for this task') plus axis keywords (coding, reasoning, agentic, tool-use, price, speed), but a few common synonyms are missing.	4 / 5
Distinctiveness Conflict Risk	Live-leaderboard LLM recommender is a clear niche with distinct, specific triggers and minimal overlap with other skills.	5 / 5
	Total	18 / 20 Passed

Validation

93%

Warnings & errors only

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

Criteria	Description	Result
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
Commit: b4ba256

Reviewed: ~22 hours ago

Table of Contents

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