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research-lab

使用独立 Research Core 把模型比较、软件评估或其他可证伪问题转化为可恢复、可复现、证据可追溯的研究。 仅用于需要真实执行对照实验的场景:模型/方案/系统的实证对比、基线比较、模型替代评估、消融、盲评、可复现实验。 不用于简单事实查询、只读审计/盘点/调查(即使涉及"证据")、无需实验的低风险判断,或用户只要求普通文案/解释的场景。

Quality

89%

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SecuritybySnyk

Passed

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SKILL.md
Quality
Evals
Security

Quality

Content

85%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 efficient, highly actionable protocol document with a well-validated workflow: explicit pre-execution checks, structured failure outcomes, and resume semantics. Its one real defect is that the single external reference is a dangling path, so progressive disclosure cannot be relied on as written.

Suggestions

Ship the referenced examples/llm-replacement.md in the bundle, or remove the pointer if the example will not be provided — a dangling reference breaks navigation at the exact moment the reader needs it.

Trim the duplicated Decision enumeration (硬规则 4 vs 标准流程 7) to a single authoritative list.

Give one concrete invocation form for a `research_` tool (e.g., the actual command syntax for `research validate`) so the workflow's first executable step is unambiguous.

DimensionReasoningScore

Conciseness

The ~90-line body is dense and lean: no concept explanations Claude already knows, no padding, and every section (触发门禁, 硬规则, 标准流程, minimal Spec, 输出契约) earns its tokens. The only trim candidate is the Decision enumeration appearing in both 硬规则 4 and 标准流程 7, which is too minor to drop below the 'lean and efficient' anchor.

5 / 5

Actionability

Concrete tool calls with a stable `research_` prefix (research_validate, research_create, research_inspect, research_execute, research_continue, research_status, research_compare, research_evidence) in a numbered order, plus a near-complete copy-paste JSON Spec example and a literal output contract. Not a 5 because the Spec contains placeholders ("..." input, "path/to/adapter.py") and the body gives no invocation syntax for the `research_` tools, delegating that to the runtime.

4 / 5

Workflow Clarity

The 标准流程 is a clear 7-step sequence with an explicit pre-execution validation checkpoint (step 3: 用 research_inspect 检查…缺关键条件时停止执行并返回结构化缺口), resume handling (research_continue instead of duplicating runs), no-hot-polling status checking, and a structured failure vocabulary (UNSUPPORTED/INVALID/INCONCLUSIVE) plus explicit multi-device conflict handling (硬规则 7). This matches the anchor's explicit validation steps and error-recovery feedback loops.

5 / 5

Progressive Disclosure

Sections are well organized and content is appropriately inline for a ~90-line skill, but the body's only external reference — "相关示例见 examples/llm-replacement.md" — points to a file that does not exist in the bundle (no references/, scripts/, assets/, or examples/ directories). A clearly signaled yet dangling reference leaves navigation incomplete, which fits the 'structure present but organization/reference gaps' anchor better than the 'well-signaled one-level-deep references' anchor.

3 / 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.

The description is concise and strongly structured: a concrete what, an explicit use-only-when trigger list, and explicit exclusion boundaries that sharply reduce mis-trigger risk. The remaining gap is modest — a few natural user phrasings (e.g., casual comparison/benchmark wording) and the concrete spec/run/evidence mechanics are absent.

Suggestions

Add a couple of natural user phrasings as triggers, e.g. "A 和 B 哪个更好/更快/更便宜"、"跑个对比测试/benchmark", to broaden trigger-term coverage.

Mention one concrete deliverable mechanism (e.g., ResearchSpec / run / evidence digest) so the 'what' is less abstract.

Optionally note cost/latency evidence explicitly as an outcome, since those are common comparison axes users ask about.

DimensionReasoningScore

Specificity

Names the domain and several concrete actions — "模型/方案/系统的实证对比、基线比较、模型替代评估、消融、盲评、可复现实验" — with the deliverable qualities "可恢复、可复现、证据可追溯". Not a 5 because the opening action "转化为…研究" stays abstract and the description never names the concrete mechanisms (spec, run, evidence) the skill actually drives.

4 / 5

Completeness

Clearly answers both parts: the "what" ("把模型比较、软件评估或其他可证伪问题转化为可恢复、可复现、证据可追溯的研究") and an explicit "when" clause ("仅用于需要真实执行对照实验的场景" followed by concrete trigger scenarios), plus a negative trigger list ("不用于简单事实查询…"). This matches the anchor with concrete trigger phrases for both what and when.

5 / 5

Trigger Term Quality

Good natural keyword coverage: "模型比较", "软件评估", "基线比较", "消融", "盲评", "可复现实验", "模型替代评估". A 4 rather than 5 because common user phrasings like "A 和 B 哪个更好", "成本/延迟对比", or "benchmark" are missing, keeping a few natural trigger terms out of reach.

4 / 5

Distinctiveness Conflict Risk

A clear niche — real executed controlled experiments — is fenced off with explicit negative triggers ("不用于简单事实查询、只读审计/盘点/调查…无需实验的低风险判断"), which prevents overlap with read-only research, audit, or explanation skills. Minimal conflict risk.

5 / 5

Total

18

/

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

ooooooooooooooooooop/agent-tools

Path

skills/research-lab/SKILL.md

Reviewed

Table of Contents

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