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hugging-face-jobs

This skill should be used when users want to run any workload on Hugging Face Jobs infrastructure. Covers UV scripts, Docker-based jobs, hardware selection, cost estimation, authentication with tokens, secrets management, timeout configuration, and result persistence. Designed for general-purpose compute workloads including data processing, inference, experiments, batch jobs, and any Python-based tasks. Should be invoked for tasks involving cloud compute, GPU workloads, or when users mention running jobs on Hugging Face infrastructure without local setup.

61

Quality

77%

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SecuritybySnyk

Low

Low-risk findings worth noting

Fix and improve this skill with Tessl

tessl review fix ./backend/cli/skills/other/hugging-face-jobs/SKILL.md
SKILL.md
Quality
Evals
Security

Quality

Content

63%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 exceptionally actionable — executable MCP/CLI/Python examples for every operation, a critical inline-vs-path gotcha for the script parameter, and ready-to-run bundled scripts with arguments. It is undermined by severe redundancy and token bloat: token usage is documented twice (inline and in a reference file), troubleshooting appears in two sections, and takeaways/directives repeat each other, inflating the file to ~1000 lines where ~400 would do.

Suggestions

Replace the inline ~190-line 'Token Usage Guide', the 'Credential Setup' section, and the Prerequisites token subsection with a ~15-line summary that says when a write token is needed, the 'secrets': {'HF_TOKEN': '$HF_TOKEN'} pattern, and a pointer to references/token_usage.md (which already exists and duplicates this content).

Merge 'Common Failure Modes' and 'Troubleshooting' into a single section (or keep only the failure modes and point to references/troubleshooting.md), and delete 'Key Takeaways' since 'Key Directives' already covers the same rules.

Turn the scattered checks into one ordered end-to-end workflow (verify auth → choose flavor → write script with PEP 723 header → set timeout + secrets → submit → verify persistence) with explicit validate-and-retry steps, so the validation checkpoints read as a loop rather than scattered checklists.

DimensionReasoningScore

Conciseness

The ~1000-line body is heavily padded: a ~190-line inline 'Token Usage Guide' explains basics Claude already knows ('What are HF Tokens? - Authentication credentials for Hugging Face Hub'), the Prerequisites checklist duplicates the Token section, a standalone 'Credential Setup' section re-covers token setup, 'Troubleshooting' repeats 'Common Failure Modes', and 'Key Takeaways' restates 'Key Directives'. Fits 'noticeably verbose; several unnecessary explanations or padded sections' rather than the extreme single-theme padding of 1.

2 / 5

Actionability

Nearly every section gives copy-paste-ready guidance: complete hf_jobs('uv'/'run'/'scheduled', {...}) calls with flavor/timeout/secrets, CLI equivalents, Python API calls, a script-path gotcha with explicit wrong/right examples, and three bundled scripts wired up with concrete script_args. Specific examples cover the common cases, matching the 5 anchor.

5 / 5

Workflow Clarity

Key Directives sequence the workflow (submit via hf_jobs, handle auth, report job ID/URL, set timeout), and there are real checkpoints: a prerequisites checklist, a pre-submission persistence 'Verification Checklist', token-verification asserts in scripts, and a Failure Modes section with fixes. Falls short of 5 because there is no single ordered end-to-end workflow with validate-then-retry feedback loops — the checks are scattered across duplicated sections rather than a coherent loop.

4 / 5

Progressive Disclosure

The bundle is well-formed (references/hardware_guide.md, hub_saving.md, token_usage.md, troubleshooting.md and three scripts/ files all exist and are listed with descriptions), and references are clearly signaled. However, ~190 lines of token usage material are inlined in SKILL.md even though references/token_usage.md exists for exactly that purpose — 'content that should be separate is inline'. That duplication, plus repeated credential/troubleshooting coverage, places it at the 3 anchor rather than 4's 'most content appropriately placed'.

3 / 5

Total

14

/

20

Passed

Description

83%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 clearly and explicitly states both what the skill covers and when to invoke it, with a good inventory of capability areas and natural trigger terms. Its weaknesses are verbosity for a description field, topic-noun lists rather than concrete verbs, and a broad 'any workload / cloud compute' framing that raises mild conflict risk with adjacent cloud/training skills.

DimensionReasoningScore

Specificity

Enumerates concrete capability areas — 'UV scripts, Docker-based jobs, hardware selection, cost estimation, authentication with tokens, secrets management, timeout configuration, and result persistence' — plus named workload types ('data processing, inference, experiments, batch jobs'). These are topics/parameters rather than crisp verbs, and 'run any workload' is broad, so it sits below the 5 anchor's multiple concrete actions but above 3's 1-2 actions.

4 / 5

Completeness

Explicitly answers 'what' ('run any workload on Hugging Face Jobs infrastructure', covering UV scripts, Docker jobs, hardware, cost, auth, timeouts, persistence) and 'when' ('Should be invoked for tasks involving cloud compute, GPU workloads, or when users mention running jobs on Hugging Face infrastructure without local setup'). Both halves are present with concrete trigger phrases, matching the 5 anchor; 4 would require the when-clause to be less explicit.

5 / 5

Trigger Term Quality

Includes natural phrases users would say: 'run any workload', 'cloud compute', 'GPU workloads', 'batch jobs', 'running jobs on Hugging Face infrastructure'. Missing common synonyms/short forms like 'HF Jobs', 'Spaces', 'remote/managed compute', so it stops just short of comprehensive coverage.

4 / 5

Distinctiveness Conflict Risk

'Hugging Face Jobs infrastructure' carves a clear niche with distinct triggers, but 'general-purpose compute workloads', 'tasks involving cloud compute', and 'GPU workloads' broaden it into overlap with generic cloud-compute and training skills (the skill itself defers to a model-trainer skill). Minor overlap risk with closely related skills — the 4 anchor.

4 / 5

Total

17

/

20

Passed

Validation

81%

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation — 13 / 16 Passed

Validation for skill structure

CriteriaDescriptionResult

skill_md_line_count

SKILL.md is long (1063 lines); consider splitting into references/ and linking

Warning

metadata_version

'metadata.version' is missing

Warning

frontmatter_unknown_keys

Unknown frontmatter key(s) found; consider removing or moving to metadata

Warning

Total

13

/

16

Passed

Repository

synthetic-sciences/openscience

Path

backend/cli/skills/other/hugging-face-jobs/SKILL.md

Reviewed

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