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AC apify-marketing-agency-database

Build a marketing agency database from Clutch.co with the Clutch.co Agency API Actor (johnvc/clutch-agency-api). Point it at any Clutch directory or category or location URL and get one JSON row per listed agency: name, profile_url, website, rating, review_count, verification, min_project_size, hourly_rate, employees, location, and service_lines. Pagination and cross-page de-duplication are handled for you, so one company is never billed twice. Use when someone wants a marketing agency database, a list of digital marketing agencies, a B2B agency directory as structured data, top agencies by service or city, or to export agencies from Clutch into a spreadsheet, CRM, or vector store. Billed per listing delivered with no start fee, and MCP-ready for Claude and other AI agents.

apify/awesome-skills Agent Skills author: apify 3 files body ≈ 2 188 tokens Open the sourcegithub.com↗ analyzed 3 d ago

Build a marketing agency database from Clutch.co with the Clutch.co Agency API Actor (johnvc/clutch-agency-api). Point it at any Clutch directory or category…

As a process C 60/100 · Has gaps — weak spots: result and completion, running it twice, progress reporting

GeneratorAI and agentstype 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
97/100
safety, quality, tests
Safety 60%
100
Quality 40%
92
Run on models
none yet
Process rating
C
60/100
Has gaps
Result and completion w 14
0
Progress reporting w 2
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: 3. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    • note frontmatter-key unknown frontmatter key "author_url"

    Process rating: all ten parameters 60/100

    • 0Result and completion. Does not say what the result is
    • 0Progress reporting. Says nothing while it works
    • 30Running it twice. 2 mutating operations with no state check
    • 50When it triggers. No condition that starts the skill
    • 50Failures and branches. 0 branches, has a failure section
    • 60Tools and files. Uses tools (web) that frontmatter does not declare
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 100Steps. 38 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 2188 tokens
    • 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
    • +3Output format is not stated: the model decides each time
    • +2Single-language instructions
    • +4Description says when NOT to use the skill
    • +3Description length 784: enough signal without eating the budget
    • +4Structure: 13 headings
    • +3Step-by-step instructions: 38 items
    • +4Has examples (4 code blocks)
    • +4Reference files are cited in the instructions (2 of 2)
    • +1License stated

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 92.