Pause graph execution for human review, approval, or validation, then resume with their input.
Works with
Requires three components: a checkpointer (InMemorySaver or PostgresSaver), a thread ID in config, and JSON-serializable interrupt payloads
interrupt(value) pauses and surfaces data; Command(resume=value) resumes and returns that value to the paused node
All code before interrupt() re-executes on resume, so side effects must be idempotent (use upsert, not insert)
Supports approval workfl
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AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionlanggraph-human-in-the-loopExecute the skills CLI command in your project's root directory to begin installation:
Fetches langgraph-human-in-the-loop from langchain-ai/langchain-skills 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 langgraph-human-in-the-loop. Access via /langgraph-human-in-the-loop 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
525
GitHub stars
0
upvotes
Run in your terminal
0
installs
0
this week
525
stars
Make 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
langgraph-human-in-the-loop fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
langgraph-human-in-the-loop has been reliable in day-to-day use. Documentation quality is above average for community skills.
I recommend langgraph-human-in-the-loop for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
Solid pick for teams standardizing on skills: langgraph-human-in-the-loop is focused, and the summary matches what you get after install.
I recommend langgraph-human-in-the-loop for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
Registry listing for langgraph-human-in-the-loop matched our evaluation — installs cleanly and behaves as described in the markdown.
langgraph-human-in-the-loop reduced setup friction for our internal harness; good balance of opinion and flexibility.
langgraph-human-in-the-loop fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
We added langgraph-human-in-the-loop from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
Registry listing for langgraph-human-in-the-loop matched our evaluation — installs cleanly and behaves as described in the markdown.
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