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AB kimi-use

Drive the Kimi desktop app (Kimi.app) through computer-use to query its built-in data plugins — 天眼查 company records, 同花顺 iFinD financials, 财新数据, 标普全球市场财智, 恒生聚源, SEC, IMF, 世界银行公开数据, 学术数据库, 法律数据库 and more — through the user's own logged-in Kimi session, with no separate API keys. Use whenever the user says "用 Kimi 查" / "操作 Kimi 客户端" / "Kimi 插件", asks to fetch company shareholders, financial statements, market data, or academic/legal records via Kimi, or needs a data source that has no standalone API but exists as a Kimi plugin. Covers both Claude Code (computer-use MCP) and Codex (computer plugin) driving, query-prompt patterns that force source-labeled honest answers, and the cross-checking discipline that screen-transcribed data requires. Not for kimi.com browser automation (that is kimi-webbridge) or for direct credentialed iFinD API access.

daymade/claude-code-skills Agent Skills author: daymade 4 files body ≈ 1 187 tokens Open the sourcegithub.com↗ analyzed 2 h ago

Drive the Kimi desktop app (Kimi.app) through computer-use to query its built-in data plugins — 天眼查 company records, 同花顺 iFinD financials, 财新数据, 标普全球市场财智…

As a process B 65/100 · Nearly there — 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: 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
96/100
safety, quality, tests
Safety 60%
100
Quality 40%
90
Run on models
none yet
Process rating
B
65/100
Nearly there
Result and completion w 14
0
Inputs and preconditions w 11
0
Progress reporting w 2
0
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: 4. 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 65/100

    • 0Result and completion. Does not say what the result is
    • 0Inputs and preconditions. Does not say what the process needs to start
    • 0Progress reporting. Says nothing while it works
    • 30Running it twice. 1 mutating operations with no state check
    • 50Failures and branches. 0 branches, has a failure section
    • 100Tools and files. No external tools needed
    • 100Steps. 22 steps
    • 100When it triggers. States when to use and when not to
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1187 tokens

    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

    • +3Description length 854: 120–800 characters recommended
    • +3Output format is not stated: the model decides each time
    • +4No input/output examples
    • +1No license
    • +2Single-language instructions
    • +5Description quotes 3 example trigger phrases
    • +4Description says when NOT to use the skill
    • +4Structure: 7 headings
    • +3Step-by-step instructions: 22 items
    • +4Reference files are cited in the instructions (3 of 3)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 90.