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AA chat-visualizer-ymind

Turn AI chat transcripts into interactive D3.js thinking maps with reasoning nodes, thinking shifts, and action items. Invoke this skill when the user shares a public share link from any AI chatbot or assistant (ChatGPT, Gemini, Claude, DeepSeek, Doubao, etc.), or pastes conversation text directly. Also applies when the user wants to visualize, analyze, or extract insights from a chat, even without explicit mention of a graph (e.g. "help me understand this chat", "what was decided here", "summarize the key takeaways").

ClawHub Agent Skills author: Stella Yu v1.0.5 MIT-0 13 files · 1 script body ≈ 1 648 tokens Open the sourceclawhub.ai↗ analyzed 2 d ago

As a process A 82/100 · Runs to the end — no weak spots found

AnalyzerInfrastructureAI and agentsWriting and documentstype 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
97/100
safety, quality, tests
Safety 60%
100
Quality 40%
93
Run on models
none yet
Process rating
A
82/100
Runs to the end
Tools and files w 18
60
Result and completion w 14
60
Inputs and preconditions w 11
70
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: 13. 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 82/100

    • 60Tools and files. Uses tools (web, git, python, node) that frontmatter does not declare
    • 60Result and completion. Output format stated, no completion criterion
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 85Steps. 22 steps, 1 vague phrases
    • 100When it triggers. States when to use and when not to
    • 100Failures and branches. 7 branches, has a failure section
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1648 tokens
    • 100Running it twice. Mutating operations check current state
    • 100Progress reporting. Reports progress
    • low The response is described with custom markup (12 tags): a typed call is more reliable

    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

    • +4Description does not say when NOT to use the skill (false activations)
    • -31 of 5 scripts are never mentioned in SKILL.md
    • +1No license
    • +2Single-language instructions
    • +5Description quotes 3 example trigger phrases
    • +3Description length 524: enough signal without eating the budget
    • +4Structure: 12 headings
    • +3Step-by-step instructions: 22 items
    • +3Output format is stated explicitly
    • +4Has examples (8 code blocks)
    • +4Reference files are cited in the instructions (1 of 1)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 93.

    External checks

    ClawHub: suspicious
    This chat visualization skill appears functional, but it saves full chats locally and includes browser automation meant to work around provider blocking, so it needs user review before installation.
    LLM: suspicious (high) · VirusTotal: · 29 May 2026