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compute-env-setup

Set up a compute environment on a remote provider so Claude Science jobs can run there. Covers direct SSH/conda hosts, Slurm clusters, container-via-bridge runners, and managed-API providers (Modal, GCP, RunPod). Use when standing up a new provider, porting an env to a different backend, adding a tool that needs its own software stack, or wiring weight caches. Triggers on "new compute provider", "set up env on", "port env to", "build GPU image", "weight cache", "compute_details", "conda env on the box", "apptainer on slurm".

72

Quality

89%

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SecuritybySnyk

Low

Low-risk findings worth noting

SKILL.md
Quality
Evals
Security

Quality

Content

82%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.

An expert-calibrated, highly actionable body: concrete commands for every provider shape, explicit three-level validation with feedback loops, and a grep-able diagnosis table. The main gaps are length that could be offloaded to reference files, an implicit rather than explicit step sequence, and two referenced files that are not present in the bundle.

Suggestions

Consolidate the workflow into an explicit ordered checklist (e.g., 1. compute_details read → 2. pick shape → 3. build → 4. weights → 5. validate → 6. record) near the top, so the sequence doesn't have to be inferred from section order.

Move the 14-row diagnosis table to a reference file (e.g., references/diagnosis.md) and keep only the top few universal rows inline, trimming the SKILL.md token cost.

Fix or qualify the dangling references to 'remote-compute-modal/env-setup.md' and 'remote-compute-<provider>/env-setup.md' — either ship them in the bundle or note that they live in the remote-compute skill's directory so agents don't chase a missing path.

DimensionReasoningScore

Conciseness

Assumes Claude's competence (no explanations of what conda or Slurm are) and nearly every sentence carries a non-obvious gotcha (purge windows, APPTAINER_CACHEDIR, no-egress compute nodes). But the four provider-shape paragraphs and the 14-row diagnosis table run long with multi-clause sentences that could be tightened or split out. Efficient with minor trimming opportunities — anchor 4, not 5 because not every token earns its place.

4 / 5

Actionability

Copy-paste-ready commands throughout: 'compute_details({provider, mode:"read"})', 'conda create -n <name> python=<X>', 'apptainer pull <name>.sif docker://<ref>', 'module use $HOME/modulefiles', the literal ENV_TABLE entry, 'build_env(name)', exact sed fixes, and 'OMP/MKL/OPENBLAS_NUM_THREADS=<tier.cpus>'. Fully executable guidance covering the common cases per provider shape — matches the 5 anchor.

5 / 5

Workflow Clarity

The flow (read compute_details first → recognise the shape → build → place weights → validate → record back) is coherent, and validation is explicit at three levels with when-to-run guidance ('after any env rebuild... or doc edit — and before declaring the env ready') plus symptom→fix feedback loops in the diagnosis table. But the sequence is implied by section order rather than stated as explicit steps, and there is no consolidated build checklist. Anchor 4 ('clear sequence with most checkpoints; minor gaps'), not 5.

4 / 5

Progressive Disclosure

Good structure with a well-signaled, one-level-deep, real reference ('references/envs_reference.md — the Claude Science envs as worked examples'). However 'remote-compute-modal/env-setup.md' and 'remote-compute-<provider>/env-setup.md' are referenced but absent from the bundle, and the large inline diagnosis table arguably belongs in a reference file. Anchor 4 ('good structure... minor organization gaps'), not 5 because of the dangling/unverifiable references.

4 / 5

Total

17

/

20

Passed

Description

96%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 description: concrete actions, an explicit 'Use when' clause with domain-specific trigger phrases, and comprehensive coverage of the provider shapes. The only weakness is minor trigger overlap with generic container/image-building skills.

DimensionReasoningScore

Specificity

Lists multiple concrete actions — 'Set up a compute environment', 'porting an env to a different backend', 'adding a tool that needs its own software stack', 'wiring weight caches' — and comprehensively names the four provider shapes (SSH/conda, Slurm, container-via-bridge, managed API). Matches the 'multiple specific concrete actions; comprehensive coverage' anchor; not 4 because there are no coverage gaps within its stated domain.

5 / 5

Completeness

Explicitly answers 'what' ('Set up a compute environment on a remote provider so Claude Science jobs can run there') and 'when' ('Use when standing up a new provider, porting an env to a different backend...') with concrete trigger phrases. Matches the 5 anchor; not 4 because the 'when' clause is both explicit and specific.

5 / 5

Trigger Term Quality

Eight explicit natural trigger phrases ('new compute provider', 'set up env on', 'port env to', 'build GPU image', 'weight cache', 'conda env on the box', 'apptainer on slurm') plus provider names users would say. Comprehensive synonym coverage matches the 5 anchor; not 4 because common variations are well represented, with only the internal tool name 'compute_details' being borderline jargon.

5 / 5

Distinctiveness Conflict Risk

The Claude Science compute-env niche is clear and triggers like 'apptainer on slurm' are distinct, but 'build GPU image' and container phrasing carry minor overlap risk with generic Docker/container-build skills. Matches the 'mostly distinct; minor overlap risk' anchor; not 5 for that overlap, not 3 because the domain is unmistakably specific.

4 / 5

Total

19

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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
UnicomAI/wanwu
Reviewed

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