ai-data-extractor: Free open-source extractor for AI coding assistant chat histories

by isoshimodo · Agent Tool · ★ 117

Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h

Security audit verdict: SAFE · quality 71/100

None of the 42 rules matched — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against 42 published rules built on the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →

The quality score combines nine heuristic checks of the repo and its README: completeness, clarity, specificity, examples, structure, agent readiness, step-by-step content, instruction quality and security notes. It rates how well the project explains itself to an agent, not the code.

ai-dataclaude-codecursor-aidata-extractiondata-extractorwindsurfwindsurf-ai

Quick Facts

Stars117
Forks38
LanguagePython
CategoryAgent Tool
LicenseMIT
Quality Score71/100
Last Updated2026-10-03
Created2026-10-03
Platformsclaude-code, cli, python
Est. Tokens~8k

About ai-data-extractor

AI Coding Assistant Data Extraction Extract your own local chat history from AI coding assistants into a single, normalized JSONL format - for fine-tuning, personal analytics, or just backing up years of conversations before an app's local database gets cleared.

Compatible Skills

These tools work well together with ai-data-extractor for enhanced workflows:

  • unslop — semantic(0.26)+complementary+shared_fw(anthropic)+same_lang+similar_pop+shared_platform (67%)
  • skill-generator — semantic(0.31)+complementary+rare_topics+same_lang+similar_pop+shared_platform (65%)
  • dbt-llm-agent — semantic(0.20)+complementary+shared_fw(anthropic)+same_lang+similar_pop+shared_platform (65%)

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Looking for a ai-data-extractor alternative? If you're comparing ai-data-extractor with other agent tool tools, these 6 projects are the closest alternatives on Agent Skills Hub — ranked by topic overlap, star count, and community traction.

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How this security grade is produced

Grades come from a rule-based scan built on the SlowMist agent-security taxonomy: 42 published rules, covering credential harvesting, data exfiltration, curl | sh installers and more. It is a first-layer scan, not a manual audit — we say so rather than overstate it.

The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.

Sources & who's responsible:

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