SKILLEMALL.ai
GitHub ↗ RU Sign in

BC create-dataflow

Use this skill when the user wants to create a new dataflow, set up a new data import, connect a new data source to a destination, pull data from an integration into Google Sheets or BigQuery or another destination, or configure a source-to-destination data pipeline. Triggers include: 'import my HubSpot data', 'set up a Stripe export', 'create a pipeline from X to Y', 'I want to pull data from [source]', 'create a new dataflow', 'connect [source] to [destination]', 'set up a data flow for [source]'.

coupler-io/skills Agent Skills author: coupler-io 1 file body ≈ 2 776 tokens Open the sourcegithub.com↗ analyzed 7 d ago

Use this skill when the user wants to create a new dataflow, set up a new data import, connect a new data source to a destination, pull data from an…

As a process C 59/100 · Has gaps — weak spots: result and completion, inputs and preconditions, running it twice

GeneratorGoogle SheetsGoogle CloudHubSpotStripeData and analyticsInfrastructureSales and CRMtype and topics are labelled automatically from the skill text
Runs in: Claude / Claude Code ChatGPT / Codex Gemini CLI Cursor GitHub Copilot / VS Code Qwen Code Kimi Code CLI Z.ai (GLM) DeepSeek (Deep Code) Mistral Vibe LangChain (deepagents) OpenClaw Goose OpenCode
JSON
Technical rating
B
94/100
safety, quality, tests
Safety 60%
100
Quality 40%
84
Run on models
none yet
Process rating
C
59/100
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: 1. 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 59/100

    • 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. 13 mutating operations with no state check
    • 60Tools and files. Uses tools (web) that frontmatter does not declare
    • 70When it triggers. States when to use, but not when not to
    • 85Steps. 40 steps, 3 vague phrases
    • 100Failures and branches. 7 branches, has a failure section
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 2776 tokens
    • 100Progress reporting. Reports progress

    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
    • +1No license
    • +2Single-language instructions
    • +3Description length 504: enough signal without eating the budget
    • +4Structure: 19 headings
    • +3Step-by-step instructions: 40 items
    • +4Has examples (1 code blocks)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 84.