SKILLEMALL.ai

AC volcengine-sdk-generator

Generate accurate Volcengine SDK examples by locating an API through API Explorer search, fetching its swagger, and calling api/common/explorer/make-code with user-provided Params. Use when the user asks how to write Volcengine SDK code, generate SDK samples, or call a Volcengine API in Python, Go, Java, PHP, cURL, Node.js. Supports Chinese and English API names such as "角色扮演", "AssumeRole", or "STS AssumeRole". Also use for explicit SDK configuration questions about retry, timeout, AK/SK, STS, AssumeRole, temporary credentials, proxy, connection pooling, SSL, debug mode, request signing, response parsing, and error handling. If the user only needs API parameters, enum values, required fields, error codes, response schemas, pagination, or API comparisons, hand off to volcengine-api. If they need CLI-based operations, hand off to volcengine-cli.

ClawHub Agent Skills author: sdk-team v1.0.7 MIT-0 10 files body ≈ 2 085 tokens Open the sourceclawhub.ai analyzed 2 d ago

As a process C 61/100 · Has gaps — weak spots: result and completion, inputs and preconditions, running it twice

IntegrationSoftware developmenttype and topics are labelled automatically from the skill text
JSON
Technical rating
A
98/100
safety, quality, tests
Safety 60%
100
Quality 40%
94
Run on models
none yet
Process rating
C
61/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
0
Running it twice w 4
30
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: 10. 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 61/100

    • 0Result and completion. Does not say what the result is
    • 0Inputs and preconditions. Does not say what the process needs to start
    • 30Running it twice. 2 mutating operations with no state check
    • 60Tools and files. Uses tools (bash, web, python) that frontmatter does not declare
    • 70When it triggers. States when to use, but not when not to
    • 100Steps. 32 steps
    • 100Failures and branches. 11 branches, has a failure section
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 2085 tokens
    • 100Progress reporting. Reports progress
    • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing

    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

    • +4Description does not say when NOT to use the skill (false activations)
    • +3Description length 856: 120–800 characters recommended
    • +3Output format is not stated: the model decides each time
    • +2Single-language instructions
    • +5Description quotes 2 example trigger phrases
    • +4Structure: 4 headings
    • +3Step-by-step instructions: 32 items
    • +4Has examples (4 code blocks)
    • +4Reference files are cited in the instructions (5 of 5)
    • +3All 3 scripts are documented
    • +1License stated

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 94.

    External checks

    ClawHub: suspicious
    The skill mostly matches its SDK-generation purpose, but its reference guidance includes unsafe HTTP and SSL-disable examples without clear warnings.
    LLM: suspicious (high) · 31 Jul 2026