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b17z

checkpoint

by b17z

Stop losing context. Checkpoint your AI research and recall it later. A Plugin and CLI for context preservation: semantic checkpoints, knowledge recall, session handoffs

1🍴 1📅 Jan 24, 2026

SKILL.md


name: checkpoint description: > Auto-save research progress using sage_autosave_check MCP tool. INVOKE WHEN: user asks to research, compare, analyze, or investigate topics; after completing web searches; when synthesizing conclusions ("therefore", "in summary"). INVOKE FOR: checkpoint, save this, remember this, save my research. DO NOT INVOKE: for simple Q&A, code editing, or file operations unrelated to research.

Checkpoint Skill

You have the ability to create semantic checkpoints that preserve research state across context windows. Use this proactively when you detect state transitions.

When to Checkpoint

Checkpoint when you detect:

SignalExample Phrases
Conclusion reached"So the answer is...", "This means...", "Therefore..."
Hypothesis validated"This confirms...", "This rules out..."
Branch point"We could either X or Y", "Two approaches..."
Constraint discovered"Wait, that changes things...", "I didn't realize..."
Topic transitionShift in focus, new entity/concept
User validation"That makes sense", "Let's go with that", "Agreed"
Explicit request"checkpoint", "save this", "remember this"

Checkpoint Format

When checkpointing, create a structured block:

id: [timestamp]_[short-description]
trigger: [manual | synthesis | branch_point | constraint | transition]

core_question: |
  What decision or action is this research driving toward?

thesis: |
  Current synthesized position (1-2 sentences)
confidence: [0.0-1.0]

open_questions:
  - What's still unknown?
  - What needs more research?

sources:
  - id: [identifier]
    type: [person | document | api | observation]
    take: [Decision-relevant summary, 1-2 sentences]
    relation: [supports | contradicts | nuances]

tensions:
  - between: [source1, source2]
    nature: What they disagree on
    resolution: [unresolved | resolved | moot]

unique_contributions:
  - type: [discovery | experiment | synthesis | internal_knowledge]
    content: What WE found that isn't in external sources

action:
  goal: What's being done with this research
  type: [decision | output | learning | exploration]

Storage

Save checkpoints to ~/.sage/checkpoints/ (global Sage directory).

Filename format: YYYY-MM-DDTHH-MM-SS_short-description.yaml

Compression Principles

  1. Compress for decisions, not completeness - "Would this change the decision?"
  2. Preserve tensions - Disagreements between credible sources are high-value
  3. Elevate unique contributions - Your discoveries are differentiated value
  4. Drop re-derivable content - Keep conclusions, not the reasoning chain

Restoration

When continuing from a checkpoint, inject it as context:

# Research Context (Restored from Checkpoint)

## Core Question
[core_question]

## Current Thesis (confidence: X%)
[thesis]

## Open Questions
[open_questions as bullets]

## Key Sources
[sources with relation indicators: [+] supports, [-] contradicts, [~] nuances]

## Tensions
[unresolved disagreements]

## Unique Discoveries
[unique_contributions]

Autosave Triggers

Think of checkpointing like a game's autosave system. Call sage_autosave_check at these moments:

Trigger EventWhenGame Analogy
research_startUser asks research questionEntering boss room
web_search_completeAfter processing web search resultsPicked up item
synthesisYou say "So...", "Therefore...", "In summary..."Quest complete
topic_shiftUser pivots to new topicSwitching levels
user_validatedUser confirms your finding ("yes", "agreed", "that's right")Checkpoint reached
constraint_discoveredNew info changes approachPlot twist
branch_pointMultiple viable paths identifiedFork in road

Call pattern:

sage_autosave_check(
  trigger_event="synthesis",
  core_question="What we're researching",
  current_thesis="Where we are now",
  confidence=0.75
)

The tool decides whether to save. If it saves, briefly confirm: "📍 Autosaved: [thesis]"

Behavior

  • Proactive: Call autosave checks at trigger moments. Don't wait to be asked.
  • Lightweight: Brief notification ("📍 Autosaved"), don't disrupt flow.
  • Cumulative: Each checkpoint builds on previous, creating a research trail.

Score

Total Score

60/100

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