SKILLEMALL.ai

BC chatgpt-memory-extraction

Extract structured personal memories from ChatGPT export data (conversations JSON). Produces organized timeline, people profiles, and thematic records by deeply reading every conversation. Use when: (1) user exported or wants to export ChatGPT data, (2) user wants to organize/analyze/search their chat history, (3) user wants to build a personal memory archive or diary from conversations, (4) user asks to extract people/events/emotions/knowledge/timeline from ChatGPT, (5) user mentions conversations.json or ChatGPT data export, (6) user wants to migrate memories from ChatGPT to another system, (7) user wants a summary or review of their ChatGPT usage over time. Triggers on: 'organize my ChatGPT history', 'extract memories from ChatGPT', 'analyze my ChatGPT export', '整理ChatGPT对话', '导出ChatGPT数据', 'build memory from chats', 'what did I talk about with ChatGPT', 'review my ChatGPT conversations', 'make a timeline from my chats', 'ChatGPTのデータを整理'. NOT for: other AI chat exports (Claude/Gemini), real-time logging, or automated summarization without human review.

ClawHub Agent Skills author: cyresearch v1.0.1 MIT-0 8 files body ≈ 332 tokens Open the sourceclawhub.ai analyzed 3 d ago

As a process C 61/100 · Has gaps — weak spots: inputs and preconditions, failures and branches, running it twice

AnalyzerAI and agentsInfrastructureWriting and documentstype and topics are labelled automatically from the skill text
JSON
Technical rating
B
86/100
safety, quality, tests
Safety 60%
100
Quality 40%
66
Run on models
none yet
Process rating
C
61/100
Has gaps
Inputs and preconditions w 11
0
Failures and branches w 10
0
Progress reporting w 2
0
the three weakest of ten parameters · all ten

How to improve

  1. Shorten the description to 1024 characters.
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: 8. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • error description-long description is 1071 chars, limit 1024

Process rating: all ten parameters 61/100

  • 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
  • 30Running it twice. 1 mutating operations with no state check
  • 60Tools and files. Uses tools (bash) that frontmatter does not declare
  • 60Result and completion. Output format stated, no completion criterion
  • 100Steps. 9 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 332 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

  • +5Description has no quoted example phrases that should trigger the skill
  • +3Description length 1071: 120–800 characters recommended
  • +4No input/output examples
  • +1No license
  • +2Single-language instructions
  • +4Description says when NOT to use the skill
  • +4Structure: 4 headings
  • +3Step-by-step instructions: 9 items
  • +3Output format is stated explicitly
  • +4Reference files are cited in the instructions (2 of 2)
  • +3All 1 scripts are documented

Quality base 70; lint remarks subtract, signals add up to 100. Result: 66.

External checks

ClawHub: clean
This skill transparently turns a user-provided ChatGPT export into a local searchable memory archive, but the archive can contain very sensitive personal and third-party information.
LLM: benign (high) · VirusTotal: · 29 May 2026