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AC claude-chat-export-analyze

当用户要导出并分析与 Claude.ai 网页版的全部聊天历史(用于回顾、检索、复盘、生成报告、做时间线、找具体某次对话)时,加载本 skill。典型触发:用户提到 "claude 聊天记录"、"claude conversations.json"、"导出 claude 对话"、"分析我和 claude 的对话"、"翻翻以前和 claude 聊的"、"哪次 claude 跟我说过 X" 等。注意:本 skill 只覆盖 Claude.ai 网页版/claude.ai 的官方导出,不包括 Claude Code 本地 JSONL(那是不同的产物,需要另一套解析)。

ClawHub Agent Skills author: 少卿 v0.1.0 MIT-0 2 files body ≈ 1 124 tokens Open the sourceclawhub.ai↗ analyzed 4 d ago

当用户要导出并分析与 Claude.ai 网页版的全部聊天历史(用于回顾、检索、复盘、生成报告、做时间线、找具体某次对话)时,加载本 skill。典型触发:用户提到 "claude 聊天记录"、"claude conversations.json"、"导出 claude 对话"、"分析我和 claude…

As a process C 51/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions

AnalyzerAI and agentstype 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
91/100
safety, quality, tests
Safety 60%
100
Quality 40%
77
Run on models
none yet
Process rating
C
51/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
0
Failures and branches w 10
0
the three weakest of ten parameters · all ten

How to improve

  1. Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
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: 2. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning description-no-when description does not say WHEN to use the skill (no "use when")

Process rating: all ten parameters 51/100

  • 0Result and completion. Does not say what the result is
  • 0Inputs and preconditions. Does not say what the process needs to start
  • 0Failures and branches. Linear process with no failure handling
  • 0Progress reporting. Says nothing while it works
  • 20When it triggers. No condition that starts the skill
  • 30Running it twice. 4 mutating operations with no state check
  • 100Tools and files. No external tools needed
  • 100Steps. 26 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 1124 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

  • +4Description does not say when NOT to use the skill (false activations)
  • +3Output format is not stated: the model decides each time
  • +1No license
  • +2Single-language instructions
  • +5Description quotes 6 example trigger phrases
  • +3Description length 284: enough signal without eating the budget
  • +4Structure: 16 headings
  • +3Step-by-step instructions: 26 items
  • +4Has examples (8 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 77.

External checks

ClawHub: clean
The artifact appears to be a coherent set of ClawHub and Convex operational skills with disclosed, user-directed commands and no evidence of hidden or malicious behavior.
LLM: benign (medium) · VirusTotal: · 26 Jun 2026