Claude Code 세션 히스토리를 분석하고 인사이트를 추출합니다.
Works with
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionhistory-insightExecute the skills CLI command in your project's root directory to begin installation:
Fetches history-insight from ai-native-camp/camp-2 and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate history-insight. Access via /history-insight in your agent's command palette.
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your environment. Always review source, verify the publisher, and test in isolation before production.
Submit your Claude Code skill and start earning
Create detailed user stories, acceptance criteria, and feature specs
Example
Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios
Reduce spec writing time by 50%, ensure comprehensive coverage
Research competitors, compare features, identify gaps
Example
Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities
Complete competitive research in 2 hours instead of 2 days
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
0
total installs
0
this week
14
GitHub stars
0
upvotes
Run in your terminal
0
installs
0
this week
14
stars
Claude Code 세션 히스토리를 분석하고 인사이트를 추출합니다.
~/.claude/projects/<encoded-cwd>/*.jsonl
Path Encoding: /Users/foo/project → -Users-foo-project
상세 파일 포맷:
${baseDir}/references/session-file-format.md
스코프 결정:
명시된 경우 (AskUserQuestion 생략 가능):
current_projectall_sessions명시되지 않은 경우 - AskUserQuestion 호출:
question: "세션 검색 범위를 선택하세요"
options:
- "현재 프로젝트만" → ~/.claude/projects/<encoded-cwd>/*.jsonl
- "모든 Claude Code 세션" → ~/.claude/projects/**/*.jsonl
# Current project only
find ~/.claude/projects/<encoded-cwd> -name "*.jsonl" -type f
# All sessions (모든 프로젝트)
find ~/.claude/projects -name "*.jsonl" -type f
날짜 필터링: 파일의 mtime(수정시간) 확인 후 필터. OS별 stat 옵션 다름:
stat -f "%Sm" -t "%Y-%m-%d" <file>stat -c "%y" <file>Session files found?
├─ No → Error: "No sessions found"
└─ Yes → How many files?
├─ 1-3 files → Direct Read + parse
└─ 4+ files → Batch Extract Pipeline
직접 Read로 JSONL 파싱. 파일이 크면(≥5000 tokens) extract-session.sh 사용:
${baseDir}/scripts/extract-session.sh <session.jsonl>
/tmp/cc-cache/<analysis-name>/)sessions.txt)user_messages.txt)clean_messages.txt)clean_messages.txt가 너무 커서 Read 실패 시:
파일 분할:
split -l 2000 clean_messages.txt /tmp/cc-cache/<name>/batch_
병렬 Task(opus) 호출:
Task(subagent_type="general-purpose", model="opus", run_in_background=true)
prompt: "batch_XX 파일을 읽고 주제/패턴 요약해줘"
결과 병합: Task(opus)로 종합
## Session Capture Complete
- **Sessions:** N files processed
- **Messages:** X total, Y after filter
### Extracted Insights
[분석 결과]
| Scenario | Response |
|---|---|
| No session files found | "No session files found for this project." |
| File too large | Auto-preprocess with extract-session.sh |
| jq not installed | "Error: jq is required. Install with: brew install jq" |
| Task failed | "Warning: Could not process [file]. Skipping." |
| 0 relevant sessions | "No sessions matched your criteria." |
~ prefix 사용)${baseDir}/scripts/extract-session.sh - JSONL 압축 (thinking, tool_use 제거)${baseDir}/references/session-file-format.md - JSONL 구조 및 파싱Make data-driven prioritization decisions faster
Draft PRDs, status updates, and stakeholder presentations
Example
Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement
Save 3-5 hours/week on communication overhead
Prerequisites
Time Estimate
30-60 minutes to see productivity improvements
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ Use when
Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.
✗ Avoid when
Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.
mattpocock/skills
parcadei/continuous-claude-v3
cursor/plugins
ailabs-393/ai-labs-claude-skills
pproenca/dot-skills
mattpocock/skills
history-insight reduced setup friction for our internal harness; good balance of opinion and flexibility.
history-insight is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
Solid pick for teams standardizing on skills: history-insight is focused, and the summary matches what you get after install.
I recommend history-insight for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
history-insight reduced setup friction for our internal harness; good balance of opinion and flexibility.
Useful defaults in history-insight — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
We added history-insight from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
Keeps context tight: history-insight is the kind of skill you can hand to a new teammate without a long onboarding doc.
history-insight fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
history-insight is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
showing 1-10 of 36