SKILLEMALL.ai

AD huggingface-skill

Full Hugging Face Hub skill — CLI and Python API for downloading models/datasets, uploading files, managing repos and Spaces, searching the Hub, and handling cache. Reads HF_TOKEN from environment for private repos, gated models, and write operations. Use for: model inference prep, dataset pipelines, Hub automation, and Space deployment.

ClawHub Agent Skills author: Shubha Pratim Biswas v1.0.0 MIT-0 6 files body ≈ 2 139 tokens Open the sourceclawhub.ai analyzed 3 d ago

As a process D 43/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions

IntegrationInfrastructureData and analyticstype and topics are labelled automatically from the skill text
JSON
Technical rating
A
93/100
safety, quality, tests
Safety 60%
100
Quality 40%
83
Run on models
none yet
Process rating
D
43/100
Unfinished process
Result and completion w 14
0
Inputs and preconditions w 11
0
When it triggers w 12
20
the three weakest of ten parameters · all ten

How to improve

    For the model run — optional
    • Your own cases (evals/evals.json, 4–6 real requests with expected answers): the full check would then run those instead of a model-drafted suite.
    • A spec.yaml with trigger phrases and assertions — a behaviour contract for CI; `skilltest init` writes a template.

    Guard findings · 0

    ✓ No critical or high findings

    Files scanned: 6. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    ✓ No remarks against the Agent Skills spec

    Process rating: all ten parameters 43/100

    • 0Result and completion. Does not say what the result is
    • 0Inputs and preconditions. Does not say what the process needs to start
    • 20When it triggers. No condition that starts the skill
    • 30Running it twice. 14 mutating operations with no state check
    • 40Consistency. Frontmatter name (huggingface-skill) differs from the folder (huggingface-api)
    • 50Failures and branches. 0 branches, has a failure section
    • 60Tools and files. Uses tools (bash, git, python) that frontmatter does not declare
    • 85Steps. 4 steps, 1 vague phrases
    • 100Execution cost. Instruction body is 2139 tokens
    • 100Progress reporting. Reports progress
    • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
    • low 12 top-level sections: this looks like several domains in one skill

    Everything here is measured from the skill text rather than judged by a model, so the numbers are checkable. A parameter weighs more when it is a more common reason for the process to stall.

    Quality signals

    • +5Description has no quoted example phrases that should trigger the skill
    • +4Description does not say when NOT to use the skill (false activations)
    • +3Output format is not stated: the model decides each time
    • -41 reference files, but SKILL.md never points to them: the model will not open them
    • +1No license
    • +2Single-language instructions
    • +3Description length 339: enough signal without eating the budget
    • +4Structure: 47 headings
    • +3Step-by-step instructions: 4 items
    • +4Has examples (31 code blocks)
    • +3All 1 scripts are documented

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 83.

    External checks

    ClawHub: clean
    This is a normal Hugging Face helper that clearly uses Hugging Face tokens and network services, with powerful upload and delete examples users should handle carefully.
    LLM: benign (high) · VirusTotal: · 29 May 2026