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AC socq-social-research

Research public social-platform content, accounts, keywords, and SEO search data with SocQ. Use when an agent needs keyword volume, suggestions, related terms, difficulty, intent, organic results, site rankings, or social data; or needs to discover a SocQ endpoint, estimate credits, submit asynchronous jobs, poll results, paginate normalized records, and retrieve raw files through SocQ MCP or CLI.

ClawHub Agent Skills author: SocQ v1.0.4 MIT-0 37 files body ≈ 1 280 tokens Open the sourceclawhub.ai↗ analyzed 3 d ago

Research public social-platform content, accounts, keywords, and SEO search data with SocQ.

As a process C 57/100 · Has gaps — weak spots: when it triggers, inputs and preconditions, running it twice

ProcedureYouTubeAI and agentsMarketingtype and topics are labelled automatically from the skill text
Runs in: OpenClaw 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) Goose OpenCode
JSON
Technical rating
A
94/100
safety, quality, tests
Safety 60%
98
Quality 40%
87
Run on models
none yet
Process rating
C
57/100
Has gaps
Inputs and preconditions w 11
0
Progress reporting w 2
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 · 2

    ✓ No critical or high findings

    Medium and low: 2
    • low Secrets in code secret-high-entropy-token references/platforms/google-maps.md:22
      High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
      "https://www.google.com/maps/place/Coffee%20Project%20New%20York%20%7C%20East%20Village/data=!4m7!3m6!1s0x…9fc!8m2!3d40…884!4d-7…820!16s%2Fg%2F11…qld!19sChI
      quoted
    • low Secrets in code secret-high-entropy-token references/platforms/google-maps.md:34
      High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
      "https://www.google.com/maps/place/Coffee%20Project%20New%20York%20%7C%20East%20Village/data=!4m7!3m6!1s0x…9fc!8m2!3d40…884!4d-7…820!16s%2Fg%2F11…qld!19sChI
      quoted

    Files scanned: 37. 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 57/100

    • 0Inputs and preconditions. Does not say what the process needs to start
    • 0Progress reporting. Says nothing while it works
    • 20When it triggers. No condition that starts the skill
    • 30Running it twice. 1 mutating operations with no state check
    • 50Failures and branches. 0 branches, has a failure section
    • 60Tools and files. Uses tools (bash) that frontmatter does not declare
    • 60Result and completion. Output format stated, no completion criterion
    • 100Steps. 42 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1280 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

    • +5Description has no quoted example phrases that should trigger the skill
    • +4Description does not say when NOT to use the skill (false activations)
    • +4No input/output examples
    • +1No license
    • +2Single-language instructions
    • +3Description length 400: enough signal without eating the budget
    • +4Structure: 8 headings
    • +3Step-by-step instructions: 42 items
    • +3Output format is stated explicitly
    • +4Reference files are cited in the instructions (7 of 7)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 87.

    External checks

    ClawHub: clean
    This skill is a disclosed SocQ integration for public social and SEO research, with some broad data-collection and supply-chain risks users should understand.
    LLM: benign (medium) · VirusTotal: · 3 Sept 2026