Save Memory Checkpoint

Quick Summary

Goal: Save current analysis context and progress to an external file for recovery after context loss.

Thin alias. /​checkpoint is the user-facing entry point to the CHECKPOINT_CREATE protocol owned by memory-management (Part 1: File-Based External Memory). memory-management is disable-model-invocation: true (not directly user-invocable), so this command is the canonical way to create a manual checkpoint. The checkpoint file structure is defined once in .claude/​skills/​memory-management/​SKILL.md (Part 1); this skill is the command surface that invokes it.

Workflow:

1. Gather Context — task state, key findings (with file:line), files analyzed/​modified, progress, decisions, next steps, open questions
2. Write Checkpoint — save to plans/​reports/​checkpoint-{timestamp}-{slug}.md following the CHECKPOINT_CREATE structure in memory-management Part 1
3. Update Todos — reflect checkpoint creation in task tracking

Key Rules:

• Canonical protocol + file template live in memory-management Part 1 (CHECKPOINT_CREATE) — do not duplicate the structure here; this skill is the command surface only
• Save checkpoints every 30-60 minutes during complex tasks and before expected context compaction
• Always include Recovery Instructions (which file to read, which line to resume from)

Be skeptical. Apply critical thinking, sequential thinking. Every claim needs traced proof, confidence percentages (Idea should be more than 80%).

Save Memory Checkpoint

Save current analysis, findings, and progress to an external memory file to prevent context loss during long-running tasks. This is the user-invocable alias for memory-management's checkpoint-create path.

Usage

Use this command when:

• Working on complex multi-step tasks (investigation, planning, implementation)
• Before expected context compaction
• At key milestones during feature development
• After completing significant analysis phases

Checkpoint File Location

Files are saved to: plans/​reports/​checkpoint-{YYYYMMDD}-{HHMMSS}-{slug}.md (unified checkpoint grammar — the resume/​recover readers glob checkpoint-* and parse this timestamp).

Instructions

1. Determine location — stamp the filename via date +%Y%m%d-%H%M%S; path plans/​reports/​checkpoint-{YYYYMMDD}-{HHMMSS}-{slug}.md.
2. Gather + write — follow the CHECKPOINT_CREATE Protocol template in .claude/​skills/​memory-management/​SKILL.md (Part 1 — the single canonical owner of the checkpoint structure). Required sections: Task Context, Key Findings (with file:line), Files Analyzed, Progress, Important Context, Next Steps, Recovery Instructions.
3. Update todo list — add - [x] Create memory checkpoint at {timestamp}.

To recover from a checkpoint, use /​recover (CHECKPOINT_RECOVER protocol).

Best Practices

1. Save checkpoints frequently - Every 30-60 minutes during complex tasks
2. Be specific - Include file paths, line numbers, exact findings
3. Document decisions - Record why choices were made
4. Link related files - Reference other analysis documents
5. Include recovery steps - Make resumption easy

Related Commands

• /​recover - Restore workflow context from the latest checkpoint (CHECKPOINT_RECOVER)
• /​context - Load project context
• /​compact - Manually trigger context compaction
• /​watzup - Generate progress summary

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[IMPORTANT] Use TaskCreate to break ALL work into small tasks BEFORE starting — including tasks for each file read. This prevents context loss from long files. For simple tasks, AI MUST ATTENTION ask user whether to skip.

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AI Mistake Prevention — Failure modes to avoid on every task:

Check downstream references before deleting. Deleting components causes documentation and code staleness cascades. Map all referencing files before removal. Verify AI-generated content against actual code. AI hallucinates APIs, class names, and method signatures. Always grep to confirm existence before documenting or referencing. Trace full dependency chain after edits. Changing a definition misses downstream variables and consumers derived from it. Always trace the full chain. Trace ALL code paths when verifying correctness. Confirming code exists is not confirming it executes. Always trace early exits, error branches, and conditional skips — not just happy path. When debugging, ask "whose responsibility?" before fixing. Trace whether bug is in caller (wrong data) or callee (wrong handling). Fix at responsible layer — never patch symptom site. Assume existing values are intentional — ask WHY before changing. Before changing any constant, limit, flag, or pattern: read comments, check git blame, examine surrounding code. Verify ALL affected outputs, not just the first. Changes touching multiple stacks require verifying EVERY output. One green check is not all green checks. Holistic-first debugging — resist nearest-attention trap. When investigating any failure, list EVERY precondition first (config, env vars, DB names, endpoints, DI registrations, data preconditions), then verify each against evidence before forming any code-layer hypothesis. Surgical changes — apply the diff test. Bug fix: every changed line must trace directly to the bug. Don't restyle or improve adjacent code. Enhancement task: implement improvements AND announce them explicitly. Surface ambiguity before coding — don't pick silently. If request has multiple interpretations, present each with effort estimate and ask. Never assume all-records, file-based, or more complex path. Keep domain concepts out of generic/​shared/​infrastructure layers. A reusable layer (shared library, framework, infra module) must reference NO consumer-specific domain concept — tenant/​customer/​product IDs, business entities, feature rules. The leak compiles and runs, so it passes review silently while coupling the "reusable" layer to one consumer. Push domain fields/​logic down into the consumer via subclass or composition.

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Critical Thinking Mindset — Apply critical thinking, sequential thinking. Every claim needs traced proof, confidence >80% to act. Anti-hallucination: Never present guess as fact — cite sources for every claim, admit uncertainty freely, self-check output for errors, cross-reference independently, stay skeptical of own confidence — certainty without evidence root of all hallucination.

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MUST ATTENTION apply critical thinking — every claim needs traced proof, confidence >80% to act. Anti-hallucination: never present guess as fact.

<!-- /​SYNC:critical-thinking-mindset:reminder --> <!-- SYNC:ai-mistake-prevention:reminder -->

MUST ATTENTION apply AI mistake prevention — holistic-first debugging, fix at responsible layer, surface ambiguity before coding, re-read files after compaction.

<!-- /​SYNC:ai-mistake-prevention:reminder -->

Closing Reminders

• MANDATORY IMPORTANT MUST ATTENTION break work into small todo tasks using TaskCreate BEFORE starting
• MANDATORY IMPORTANT MUST ATTENTION search codebase for 3+ similar patterns before creating new code
• MANDATORY IMPORTANT MUST ATTENTION cite file:line evidence for every claim (confidence >80% to act)
• MANDATORY IMPORTANT MUST ATTENTION add a final review todo task to verify work quality

[TASK-PLANNING] Before acting, analyze task scope and systematically break it into small todo tasks and sub-tasks using TaskCreate.