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Detect host inventory and effective CPU, memory, disk, scheduler, container, and accelerator limits when a user asks for resource-aware planning or before a clearly resource-sensitive local workload. Produces a redacted JSON snapshot and conservative planning helpers without stress tests or assuming visible host hardware is usable.

72

Quality

89%

Does it follow best practices?

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SecuritybySnyk

Passed

No findings from the security scan

SKILL.md
Quality
Evals
Security

Quality

Content

93%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 content is lean, highly actionable, and well-structured with verified one-level-deep references and clear safety contracts. The only minor gap is the absence of an explicit validate-and-retry feedback loop in the workflow narrative.

DimensionReasoningScore

Conciseness

The body is dense and operational with no padding of concepts Claude already knows; each line states non-obvious interpretation rules (e.g. quota vs. physical cores, cgroup vs. container markers) that earn their tokens, and the only long block is the required citation protocol rather than explanatory fluff.

5 / 5

Actionability

Provides copy-paste-ready, fully executable commands with exact flags across detection, planning, validation, and diagnostics (e.g. `python scripts/plan_workload.py resource-snapshot.json --workload cpu --tasks 100 --memory-per-worker-mib 2048`), covering the common cases.

5 / 5

Workflow Clarity

The detect -> interpret -> validate -> plan sequence is clear with explicit validation (`snapshot_tools.py validate`) and a partial-failure/warnings inspection section, but it stops short of an explicit validate -> fix -> re-validate feedback loop, leaving a minor validation gap relative to the top anchor.

4 / 5

Progressive Disclosure

The body is a well-organized overview with one-level-deep, clearly signaled markdown links to verified bundle files (references/resource_semantics.md, snapshot_schema.md, sources.md) and a Bundled files index, splitting detail appropriately for easy navigation.

5 / 5

Total

19

/

20

Passed

Description

85%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 specific, complete, and distinct, clearly stating both capabilities and trigger conditions in third person. Its main weakness is limited coverage of natural trigger-term synonyms a user might actually say.

Suggestions

Add common natural trigger phrases users would say aloud, e.g. "how many CPUs/cores do I have", "available memory/RAM", "GPU availability", or "can I run N workers", to improve trigger_term_quality.

Keep the explicit "Use when ..." style phrasing but broaden beyond "resource-aware planning" to include everyday phrasings like "before running a heavy local workload".

DimensionReasoningScore

Specificity

Lists multiple concrete capability domains (CPU, memory, disk, scheduler, container, accelerator limits) plus concrete artifacts ("redacted JSON snapshot", "conservative planning helpers") and explicit constraints ("without stress tests", "not assuming visible host hardware is usable"), matching the comprehensive-coverage anchor.

5 / 5

Completeness

Explicitly answers both what (detect host inventory and effective limits; produce a redacted JSON snapshot and planning helpers) and when ("when a user asks for resource-aware planning or before a clearly resource-sensitive local workload"), with concrete trigger phrasing.

5 / 5

Trigger Term Quality

Includes a couple of relevant natural phrases ("resource-aware planning", "resource-sensitive local workload") but misses common spoken variations and synonyms users would naturally say (e.g. "how many cores", "RAM", "GPU", "do I have enough resources"), fitting the anchor with some relevant keywords but missing common variations.

3 / 5

Distinctiveness Conflict Risk

Occupies a clear niche (process-scoped conservative resource detection with redaction and planning helpers) with distinct triggers specific to resource-aware planning, giving minimal overlap risk with other skills.

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.

Validation16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository
K-Dense-AI/scientific-agent-skills
Reviewed

Table of Contents

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