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AB where-to-start

First-move advice for a broad growth or marketing ask with no job named: looks at what GooseWorks already knows and at the site, asks a few short questions in one message, names the one thing in the way, and sends the marketer to the right next job. Use when the marketer says "help me grow my brand", "I need to grow my business", "how do I get more customers / sales", "help me with marketing", "where do I start", "sales are flat", "what should I focus on this month", or "I'm back, what's next?". Recommends only; never runs the work. Not for a named job like "make me ads" or "grow my Instagram" (go straight to it); not for a campaign with a launch, budget or date — use plan-campaign; not for why this week's numbers moved — use analyze-performance.

gooseworks-ai/goose-skills Agent Skills author: gooseworks-ai 8 files body ≈ 2 950 tokens Open the sourcegithub.com↗ analyzed 3 d ago

First-move advice for a broad growth or marketing ask with no job named: looks at what GooseWorks already knows and at the site, asks a few short questions in…

As a process B 79/100 · Nearly there — weak spots: running it twice, progress reporting

AnalyzerMarketingtype 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
A
98/100
safety, quality, tests
Safety 60%
100
Quality 40%
96
Run on models
none yet
Process rating
B
79/100
Nearly there
Progress reporting w 2
0
Running it twice w 4
30
When it triggers w 12
50
the three weakest of ten parameters · all ten

The same skill appears in 1 more place: goose-skills

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: 7. 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 79/100

    • 0Progress reporting. Says nothing while it works
    • 30Running it twice. 6 mutating operations with no state check
    • 50When it triggers. No condition that starts the skill
    • 60Tools and files. Uses tools (web) that frontmatter does not declare
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 100Steps. 25 steps
    • 100Result and completion. Output format and completion criterion are stated
    • 100Failures and branches. 1 branches, has a failure section
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 2950 tokens
    • 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

    • +4No input/output examples
    • +1No license
    • +2Single-language instructions
    • +5Description quotes 10 example trigger phrases
    • +4Description says when NOT to use the skill
    • +3Description length 756: enough signal without eating the budget
    • +4Structure: 14 headings
    • +3Step-by-step instructions: 25 items
    • +3Output format is stated explicitly
    • +4Reference files are cited in the instructions (5 of 5)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 96.