continuity-ledger▌
parcadei/continuous-claude-v3 · updated Apr 8, 2026
MDX-style export adds YAML metadata + attribution linking explainx.ai and this canonical listing URL.
Note: This skill is now an alias for /create_handoff. Both output the same YAML format.
Continuity Ledger
Note: This skill is now an alias for
/create_handoff. Both output the same YAML format.
Create a YAML handoff document for state preservation across /clear. This is the same as /create_handoff.
Process
1. Filepath & Metadata
First, determine the session name from existing handoffs:
ls -td thoughts/shared/handoffs/*/ 2>/dev/null | head -1 | xargs basename
This returns the most recently modified handoff folder name (e.g., open-source-release). Use this as the handoff folder name.
If no handoffs exist, use general as the folder name.
Create your file under: thoughts/shared/handoffs/{session-name}/YYYY-MM-DD_HH-MM_description.yaml, where:
{session-name}is from existing handoffs (e.g.,open-source-release) orgeneralif none existYYYY-MM-DDis today's dateHH-MMis the current time in 24-hour format (no seconds needed)descriptionis a brief kebab-case description
Examples:
thoughts/shared/handoffs/open-source-release/2026-01-08_16-30_memory-system-fix.yamlthoughts/shared/handoffs/general/2026-01-08_16-30_bug-investigation.yaml
2. Write YAML handoff (~400 tokens)
CRITICAL: Use EXACTLY this YAML format. Do NOT deviate or use alternative field names.
The goal: and now: fields are shown in the statusline - they MUST be named exactly this.
---
session: {session-name from ledger}
date: YYYY-MM-DD
status: complete|partial|blocked
outcome: SUCCEEDED|PARTIAL_PLUS|PARTIAL_MINUS|FAILED
---
goal: {What this session accomplished - shown in statusline}
now: {What next session should do first - shown in statusline}
test: {Command to verify this work, e.g., pytest tests/test_foo.py}
done_this_session:
- task: {First completed task}
files: [{file1.py}, {file2.py}]
- task: {Second completed task}
files: [{file3.py}]
blockers: [{any blocking issues}]
questions: [{unresolved questions for next session}]
decisions:
- {decision_name}: {rationale}
findings:
- {key_finding}: {details}
worked: [{approaches that worked}]
failed: [{approaches that failed and why}]
next:
- {First next step}
- {Second next step}
files:
created: [{new files}]
modified: [{changed files}]
Field guide:
goal:+now:- REQUIRED, shown in statuslinedone_this_session:- What was accomplished with file referencesdecisions:- Important choices and rationalefindings:- Key learningsworked:/failed:- What to repeat vs avoidnext:- Action items for next session
DO NOT use alternative field names like session_goal, objective, focus, current, etc.
The statusline parser looks for EXACTLY goal: and now: - nothing else works.
3. Mark Session Outcome (REQUIRED)
IMPORTANT: Before responding to the user, you MUST ask about the session outcome.
Use the AskUserQuestion tool with these exact options:
Question: "How did this session go?"
Options:
- SUCCEEDED: Task completed successfully
- PARTIAL_PLUS: Mostly done, minor issues remain
- PARTIAL_MINUS: Some progress, major issues remain
- FAILED: Task abandoned or blocked
After the user responds, mark the outcome:
# Mark the most recent handoff (works with PostgreSQL or SQLite)
PROJECT_ROOT=$(git rev-parse --show-toplevel 2>/dev/null || echo "${CLAUDE_PROJECT_DIR:-.}")
cd "$PROJECT_ROOT/opc" && uv run python scripts/core/artifact_mark.py --latest --outcome <USER_CHOICE>
4. Confirm completion
After marking the outcome, respond to the user:
Handoff created! Outcome marked as [OUTCOME].
Resume in a new session with:
/resume_handoff path/to/handoff.yaml
When to Use
- Before running
/clear - Context usage approaching 70%+
- Multi-day implementations
- Complex refactors you pick up/put down
- Any session expected to hit 85%+ context
When NOT to Use
- Quick tasks (< 30 min)
- Simple bug fixes
- Single-file changes
Why Clear Instead of Compact?
Each compaction is lossy compression—after several compactions, you're working with degraded context. Clearing + loading the handoff gives you fresh context with full signal.
How to use continuity-ledger on Cursor
AI-first code editor with Composer
Prerequisites
Before installing skills in Cursor, ensure your development environment meets these requirements:
- ›Cursor installed and configured on your development machine
- ›Node.js version 16.0+ with npm package manager (verify with
node --version) - ›Active project directory or workspace where you want to add continuity-ledger
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
The skills CLI fetches continuity-ledger from GitHub repository parcadei/continuous-claude-v3 and configures it for Cursor.
Select Cursor when prompted
The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:
Verify installation
Confirm successful installation by checking the skill directory location:
Reload or restart Cursor to activate continuity-ledger. Access the skill through slash commands (e.g., /continuity-ledger) or your agent's skill management interface.
Security & Verification Notice
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 development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.
List & Monetize Your Skill
Submit your Claude Code skill and start earning
Use Cases▌
Task Automation & Efficiency
Automate repetitive workflows and reduce manual effort
Example
Generate reports, summarize documents, draft communications
Save 3-5 hours per week on routine tasks
Knowledge Enhancement
Learn new skills, understand complex topics, get expert guidance
Example
Explain concepts, provide examples, suggest learning resources
Accelerate learning and skill development by 2x
Quality Improvement
Enhance output quality through reviews, suggestions, and refinements
Example
Review drafts, suggest improvements, catch errors
Improve work quality by 30-40% with less effort
Implementation Guide▌
Prerequisites
- ›Claude Desktop or compatible AI client with skill support
- ›Clear understanding of task or problem to solve
- ›Willingness to iterate and refine outputs
Time Estimate
15-45 minutes depending on use case complexity
Installation Steps
- 1.Install skill using provided installation command
- 2.Test with simple use case relevant to your work
- 3.Evaluate output quality and relevance
- 4.Iterate on prompts to improve results
- 5.Integrate into regular workflow if valuable
Common Pitfalls
- ⚠Expecting perfect results without iteration
- ⚠Not providing enough context in prompts
- ⚠Using skill for tasks outside its intended scope
- ⚠Accepting outputs without review and validation
Best Practices▌
✓ Do
- +Start with clear, specific prompts
- +Provide relevant context and constraints
- +Review and refine all outputs before using
- +Iterate to improve output quality
- +Document successful prompt patterns
✗ Don't
- −Don't use without understanding skill limitations
- −Don't skip validation of outputs
- −Don't share sensitive information in prompts
- −Don't expect skill to replace human judgment
💡 Pro Tips
- ★Be specific about desired format and style
- ★Ask for multiple options to choose from
- ★Request explanations to understand reasoning
- ★Combine AI efficiency with human expertise
When to Use This▌
✓ Use When
Use when skill capabilities match your task, clear ROI on time saved, and you can validate outputs. Best for repetitive tasks, learning, and quality improvement.
✗ Avoid When
Avoid when task requires deep expertise you can't validate, involves sensitive decisions, or when learning process is more valuable than speed of completion.
Learning Path▌
- 1Familiarize yourself with skill capabilities and limitations
- 2Start with low-risk, non-critical tasks
- 3Progress to more complex and valuable use cases
- 4Build expertise through regular use and experimentation
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
Ratings
4.6★★★★★31 reviews- ★★★★★Valentina White· Dec 24, 2024
Keeps context tight: continuity-ledger is the kind of skill you can hand to a new teammate without a long onboarding doc.
- ★★★★★Ganesh Mohane· Dec 4, 2024
We added continuity-ledger from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- ★★★★★Lucas Khanna· Nov 15, 2024
continuity-ledger is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- ★★★★★Lucas Desai· Oct 6, 2024
continuity-ledger fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- ★★★★★Rahul Santra· Sep 25, 2024
continuity-ledger fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- ★★★★★Hiroshi Desai· Sep 13, 2024
continuity-ledger has been reliable in day-to-day use. Documentation quality is above average for community skills.
- ★★★★★Li Harris· Sep 1, 2024
Useful defaults in continuity-ledger — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- ★★★★★Layla Chen· Aug 28, 2024
continuity-ledger fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- ★★★★★Chinedu Malhotra· Aug 20, 2024
continuity-ledger has been reliable in day-to-day use. Documentation quality is above average for community skills.
- ★★★★★Pratham Ware· Aug 16, 2024
continuity-ledger is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
showing 1-10 of 31