mhylle/claude-skills-collection/context-saver
- community
Checking that the SKILL.md file is still in the repository.
Write the current session's in-flight work state to a human-readable handoff doc on disk (docs/context/CONTEXT-{topic}.md) so a future chat session — or another person — can pick up exactly where this one stops. Distinct from Claude Code's built-in memory system, which captures long-lived facts and preferences; this skill captures **active session context** (what's being built right now, which files are mid-edit, what's been tried, what's next). Use when the user says "save context", "checkpoint", "save progress", "preserve state", "save before I step away", "dump this so I can continue tomorrow", or before ending a long session with incomplete work. Optimizes for correctness, completeness, minimal size, and trajectory preservation.
Install
npx vskill@latest install mhylle/claude-skills-collection/context-saverSKILL.md
Context Saver
<!-- contents: generated by tests/skill_structure.py --write; edit headings, not this block -->
Contents
- Scope — how this differs from auto-memory
- When to Use
- Core Principles
- Signal Extraction Priority
- Information Density Rules
- Context File Structure
- Dependencies & Relationships
- Decisions Made
- Approaches Taken
- Succeeded
- Failed/Abandoned
- In Progress
- User Requirements
- Blockers & Open Questions
- Next Steps
- Session Notes
Reference files (open one when a step points to it):
references/context-template.md: Context File Template
<!-- /contents -->
Preserve in-flight session state to disk for seamless continuation in another session. Extracts signal from noise — captures the essential state (trajectory, decisions, active files, blockers, next steps) while discarding ephemeral details (exploratory reads, rejected paths, verbose tool output).
Scope — how this differs from auto-memory
Claude Code has a built-in memory system at ~/.claude/projects/.../memory/ that captures long-lived, typed entries (user, feedback, project, reference). That handles persistent knowledge: facts about you, validated preferences, project-state that's true across sessions.
This skill handles what memory doesn't: the snapshot of what you're actively doing right now. It's the answer to "I need to step away mid-task — capture where I am so a new session can pick up seamlessly."
- Memory: "This user prefers pytest over unittest." (always true, loads into every session)
- This skill: "Currently refactoring
src/auth/token.service.ts:45-78— tried approach A (failed, see line 60), now trying approach B, next step is to update the test at__tests__/token.service.spec.ts:120." (true right now, for this specific task)
Memory grows bigger over months; context-save docs are ephemeral — delete them once the work they describe lands.
When to Use
- User explicitly requests context saving ("save context", "checkpoint this")
- Before ending a long session with incomplete work
- When switching focus and needing to preserve state
- Before attempting risky operations that may require rollback
- When collaborating and need to hand off context to another session
Core Principles
Signal Extraction Priority
- Trajectory (highest): What is the user trying to achieve? What's the end goal?
- Decisions: What choices were made and why? What was rejected?
- State: What code is being modified? What's the current progress?
- Approaches: What has been tried? What worked/failed?
- Noise (discard): Exploratory reads, rejected paths, verbose tool output
Information Density Rules
- Include: Specific file:line references, concrete decisions, blockers, next steps
- Exclude: General exploration, routine file reads, verbose error traces
- Compress: Long code blocks → key excerpts with line references
- Preserve: User-stated requirements verbatim, even if seemingly minor
Context File Structure
Create context files in docs/context/ or project root as CONTEXT-{topic}.md:
// {file}:{start_line}-{end_line} // {Brief explanation of what this code does and why it's relevant} {Essential code excerpt - max 30 lines}
Extraction Process
Step 1: Identify Trajectory
Before extracting anything, answer:
- What transformation is the user trying to achieve?
- What would "done" look like?
- What constraints exist?
Step 2: Map Active Code
Scan recent tool calls to identify:
- Files with Write/Edit operations → primary focus
- Files with multiple Read operations → secondary context
- Grep/Glob patterns → areas of exploration
For each active file, capture:
- Specific line ranges being modified
- The "why" behind modifications
- Relationships to other files
Step 3: Extract Decisions
Review conversation for:
- Explicit choices ("let's use X instead of Y")
- Implicit preferences (user accepted one approach, rejected another)
- Constraints stated or discovered
Step 4: Capture Approaches
Document what was tried:
- Succeeded: Brief note on what worked
- Failed: Crucially, why it failed (prevents re-trying)
- In Progress: Current state and remaining work
Step 5: Preserve User Requirements
Search conversation for:
- Direct quotes of user requirements
- Specific asks even if mentioned once
- Preferences and style guidance
Step 6: Define Next Steps
Convert remaining work into actionable items:
- Each step should reference specific files/functions
- Order by logical dependency
- Include enough context to start without re-reading everything
Quality Checklist
Before finalizing context file:
- [ ] Trajectory clear? Can a new session understand the goal in 30 seconds?
- [ ] Code references specific? File:line, not just "the auth module"
- [ ] Decisions documented? Including what was rejected and why?
- [ ] User requirements preserved? Verbatim where possible?
- [ ] Next steps actionable? Each step is specific enough to start immediately?
- [ ] Noise removed? No exploratory reads, verbose outputs, tangential discussion?
- [ ] Size reasonable? Under 500 lines, ideally under 300?
File Naming Convention
Usage Examples
Saving Mid-Implementation Context
Saving After Investigation
Saving Before Risky Operation
Anti-Patterns to Avoid
- Dumping full files: Use excerpts with line references
- Including tool output: Summarize findings, don't paste logs
- Vague references: "The service file" → "src/services/auth.service.ts:45-78"
- Missing rationale: What was done without why
- Forgotten rejections: Document why alternatives were rejected
- Stale context: Always update, don't create parallel files
Security
- Security scanPassed, 100 of 100Pattern checks on the skill's files.
- Model reviewPassed
Quality evaluation
- Task evaluationNot runNo evaluation yet. Run one in Skill Studio.
Source
- SKILL.md in the repositoryCheckingskills/context-saver/SKILL.md
Popularity
- Repository stars
- 13
- 7-day trend score
- 13
Stars and forks are for the whole GitHub repository, not this skill alone. Refreshed 1 Apr 2026, 6 months ago.
Works with
Any agent vskill installs to: 56 targets in all. These 39 have a page on this site.
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- Windsurf
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- Cline
- Amp
- Roo Code
- Goose
- Continue
- Kilo Code
- Junie
- Trae
- OpenHands
- Qwen Code
- AI Agent
- Tabnine Chat
- Sourcegraph Cody
- Amazon Q
- Firebase Genkit
- Sweep
- Devin
- Phind
- Mentat
- Claude Desktop
- Aider
- GPT Pilot
- Plandex
- bolt.new
- v0
- Replit Agent
- Anse
- TypingMind
- BetterChatGPT
- LibreChat
- Warp AI
- Pieces
- Fig AI
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