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get-available-resources

Detects 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.

Quality

94%

Does it follow best practices?

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SecuritybySnyk

Passed

No findings from the security scan

SKILL.md
Quality
Evals
Security

Quality

Content

92%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 tightly written, highly actionable skill body: concrete commands with flags for every tool, explicit validation and partial-failure handling, and a clean overview-to-references split. The only slack is minor trimmable detail in the privacy mechanics and citation sections.

Suggestions

Trim the explicit-file privacy mechanics (mode 0600, symlink/hard-link rejection details) to one line and point to the script's own --help or references for the full contract.

Shorten the citation section to the canonical reference plus the 'fetch before writing' instruction, moving the DOI/version nuances into a reference file.

DimensionReasoningScore

Conciseness

The body is lean and assumes competence — no explanations of concepts Claude already knows — using terse field enumerations ('cpu.host.logical', 'cpu.cgroup_v2.quota_cores') and direct commands. A few sections could be trimmed, such as the detailed file-permission mechanics ('uses mode 0600 on POSIX, rejects symlinks and path traversal') and the multi-paragraph citation instructions.

4 / 5

Actionability

Every workflow ships a copy-paste-ready command with concrete flags ('python scripts/detect_resources.py --output resource-snapshot.json', the full planner invocation with '--tasks 100 --memory-per-worker-mib 2048', and 'snapshot_tools.py validate/diff'), covering the common cases. Pseudocode is absent and outputs/statuses are named precisely.

5 / 5

Workflow Clarity

The sequence is explicit — safety contract, quick start, required interpretation, plan a workload, validate/diff — with real validation checkpoints ('python scripts/snapshot_tools.py validate resource-snapshot.json', planner statuses 'insufficient_memory... do not launch that plan' and 'review_required') and an error-recovery section ('One failed probe must not erase successful observations'). Not 4: feedback loops for inspecting warnings, provenance, and null fields are explicitly spelled out.

5 / 5

Progressive Disclosure

The body is a well-signaled overview ('See references/resource_semantics.md for the detailed platform rules') pointing to real one-level-deep reference files (resource_semantics.md, snapshot_schema.md, sources.md all exist), and the 'Bundled files' section lists each script with its role. The single sibling cross-link in resource_semantics.md points to sources.md, which is also directly linked from SKILL.md, so no content is buried.

5 / 5

Total

19

/

20

Passed

Description

92%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 third-person description that clearly states what the skill does, what it produces, and when to use it, with concrete trigger phrasing and near-zero conflict risk. The only weakness is slightly formal trigger language that misses the casual phrasings users often type.

Suggestions

Add one or two natural user phrasings to the trigger clause, e.g. 'or when the user asks how many cores or how much memory is available'.

DimensionReasoningScore

Specificity

'Detects host inventory and effective CPU, memory, disk, scheduler, container, and accelerator limits' and 'Produces a redacted JSON snapshot and conservative planning helpers' list multiple concrete actions with comprehensive coverage across all six resource domains, plus explicit exclusions ('without stress tests or assuming visible host hardware is usable'). Not 4: there are no gaps in domain coverage.

5 / 5

Completeness

Both questions are answered explicitly: the 'what' ('Detects... limits', 'Produces a redacted JSON snapshot and conservative planning helpers') and the 'when' ('when a user asks for resource-aware planning or before a clearly resource-sensitive local workload'). Not 4: the when-clause is explicit and concrete rather than weakly implied.

5 / 5

Trigger Term Quality

'resource-aware planning', 'resource-sensitive local workload', and the natural domain keywords 'CPU, memory, disk, scheduler, container, accelerator limits' give good trigger coverage. Not 5: common user phrasings like 'how many cores are available', 'check available memory', or 'system resources' are missing; not 3: several natural, relevant terms are present.

4 / 5

Distinctiveness Conflict Risk

The resource-detection/allocation-limit niche is distinct — no other common skill triggers on host inventory, cgroup limits, or scheduler allocation probes. Not 4: even closely related system-inspection skills would not be wrongly selected by these triggers.

5 / 5

Total

19

/

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

K-Dense-AI/scientific-agent-skills

Path

skills/get-available-resources/SKILL.md

Reviewed

Table of Contents

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