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AC querit-api

Build and debug Querit.ai search API integrations - POST /v1/search for live web results, POST /v1/contents for clean page text. Use when Querit, querit.ai, api.querit.ai, or QUERIT_API_KEY appears; when writing or reviewing code that calls a web search or page-extraction API from an app, RAG pipeline, or agent and Querit is the provider. Not for running a one-off search - if the Querit MCP server is connected, call its tools instead.

ClawHub Agent Skills author: vinkybb v0.1.1 MIT-0 7 files body ≈ 1 256 tokens Open the sourceclawhub.ai↗ analyzed 2 d ago

Build and debug Querit.ai search API integrations - POST /v1/search for live web results, POST /v1/contents for clean page text. Use when Querit, querit.ai…

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

IntegrationAI and agentsSoftware developmenttype 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
98/100
safety, quality, tests
Safety 60%
100
Quality 40%
96
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: 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 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. 3 mutating operations with no state check
    • 50When it triggers. No condition that starts the skill
    • 60Tools and files. Uses tools (web, python) that frontmatter does not declare
    • 100Steps. 10 steps
    • 100Failures and branches. 2 branches, has a failure section
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1256 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
    • +3Output format is not stated: the model decides each time
    • +2Single-language instructions
    • +4Description says when NOT to use the skill
    • +3Description length 438: enough signal without eating the budget
    • +4Structure: 9 headings
    • +3Step-by-step instructions: 10 items
    • +4Has examples (4 code blocks)
    • +4Reference files are cited in the instructions (4 of 4)
    • +3All 1 scripts are documented
    • +1License stated

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

    External checks

    ClawHub: clean
    This skill is a coherent Querit.ai API integration guide with disclosed API-key and network use, but users should avoid sending sensitive queries or private URLs to the external service.
    LLM: benign (high) · VirusTotal: · 20 Aug 2026