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science

Use for natural-science or engineering tasks, scientific software routing, simulation, dataset analysis, model fitting, package checks, HPC-through-shell work, validation, and evidence-backed scientific claims using DeepScientist's `artifact.science(...)` Science Evidence Graph. Includes a progressive-disclosure catalog of FermiLink skilled-scipkg package cards.

79

Quality

100%

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The risk profile of this skill

SKILL.md
Quality
Evals
Security

Quality

Content

100%

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

The SKILL.md body is a well-organized orchestration overview: lean, actionable, with a sequenced workflow containing validation checkpoints, and backed by a verified one-level-deep reference catalog. It avoids concept padding and splits detail appropriately into references.

DimensionReasoningScore

Conciseness

The body is lean for its scope (~130 lines), assumes Claude's domain competence, and avoids explaining basic concepts Claude already knows; it is tighter than the 'mostly efficient but could be tightened' anchor 2.

3 / 3

Actionability

Provides concrete, actionable guidance — specific tool calls ('bash_exec(...)', 'artifact.science(..., node_type="science.package_check", ...)'), exact reference paths, and concrete 'session_patch' fields — which satisfies the instruction-only criterion despite the absence of runnable code.

3 / 3

Workflow Clarity

The 10-step workflow is clearly sequenced with explicit validation (step 8 and the Validation checklist) and feedback loops (failed/blocked package checks recorded with status); because validation is present for the batch/HPC operations, it is not capped at 2.

3 / 3

Progressive Disclosure

The body is a clear overview with well-signaled, one-level-deep references, all of which exist on disk (package-index.min.json, domain-index.md, 169 packages/<id>.md cards, and the named playbooks), matching the clear-overview anchor rather than the 'some structure' anchor 2.

3 / 3

Total

12

/

12

Passed

Description

100%

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 specific, trigger-rich, and clearly states both what the skill does and when to use it, occupying a distinct scientific-computing niche. It uses third-person/infinitive voice throughout with no over-claims.

DimensionReasoningScore

Specificity

Lists multiple concrete actions such as 'simulation, dataset analysis, model fitting, package checks, HPC-through-shell work, validation, and evidence-backed scientific claims', matching the multi-specific-action anchor rather than the single-domain anchor 2.

3 / 3

Completeness

Opens with the explicit trigger 'Use for natural-science or engineering tasks ...' (when) and states the capability (scientific software routing, recording the Science Evidence Graph, package catalog), so it clearly answers both what and when rather than capping at 2.

3 / 3

Trigger Term Quality

Includes natural terms users would say ('natural-science or engineering tasks', 'simulation', 'dataset analysis', 'model fitting', 'HPC', 'validation'); some jargon ('FermiLink skilled-scipkg') is present but coverage of natural terms is strong, above the 'some relevant keywords' anchor 2.

3 / 3

Distinctiveness Conflict Risk

Targets a clear scientific niche tied to DeepScientist's 'artifact.science(...)' Science Evidence Graph, giving distinct triggers unlikely to fire for non-science skills; not the generic 'works with files' anchor 2.

3 / 3

Total

12

/

12

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.

Validation15 / 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
ResearAI/DeepScientist
Reviewed

Table of Contents

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