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Captures session history and CLI activity to analyze skill patterns and checkpoint project progress.

Install

mkdir -p .claude/skills/checkpointing && curl -L -o skill.zip "https://agentskills.codes/api/skills/download/12877" && unzip -o skill.zip -d .claude/skills/checkpointing && rm skill.zip

Installs to .claude/skills/checkpointing

Activation

This is the description your AI agent reads to decide when to run this skill — the better it matches your request, the more reliably it fires.

Run full session checkpointing from Codex without Claude dependencies. Collect git and CLI activity, generate checkpoint files, update session history, and analyze reusable skill patterns.
188 charsno explicit “when” trigger
Intermediate

Key capabilities

  • Collect git and CLI activity
  • Generate checkpoint files
  • Update session history in CLAUDE.md
  • Analyze reusable skill patterns
  • Produce candidate skill patterns with confidence and evidence

How it works

This skill executes a Python script to collect session activity, generate checkpoint files, update session history, and analyze skill patterns.

Inputs & outputs

You give it
Current git and CLI activity
You get back
Checkpoint files, updated CLAUDE.md, and skill pattern analysis report

When to use checkpointing

  • Creating session history snapshots
  • Analyzing development skill patterns
  • Documenting project progress
  • Maintaining session consistency

About this skill

Checkpointing (Codex Native)

Use the local checkpoint script directly and perform analysis in the same Codex session.

Usage

  • /checkpointing
  • /checkpointing --since YYYY-MM-DD

Workflow

  1. Run checkpoint collection

    • Command:
      • python .claude/skills/checkpointing/checkpoint.py
      • or python .claude/skills/checkpointing/checkpoint.py --since YYYY-MM-DD
    • If python is unavailable, use:
      • python3 .claude/skills/checkpointing/checkpoint.py
      • or python3 .claude/skills/checkpointing/checkpoint.py --since YYYY-MM-DD
  2. Verify generated artifacts

    • Confirm latest files exist:
      • .claude/checkpoints/*.md
      • .claude/checkpoints/*.analyze-prompt.md
    • Confirm CLAUDE.md session history was updated.
    • Extract the checkpoint UTC timestamp and reuse it for every derived artifact.
  3. Analyze skill patterns without Claude subagents

    • Read the generated .analyze-prompt.md in the current Codex session.
    • Produce candidate skill patterns with confidence and evidence.
    • Save analysis to:
      • .claude/docs/research/skill-patterns-{checkpoint-timestamp}.md
    • Naming convention:
      • Use the same UTC timestamp as checkpoint filename (example: 2026-02-20-225248).
      • This avoids overwriting date-only reports.
  4. Enforce output quality gate

    • Artifact consistency:
      • checkpoint, analyze-prompt, and skill-patterns files all share the same timestamp.
      • New timestamped report is created (skill-patterns-{timestamp}.md), not date-only overwrite.
    • Analysis structure (required):
      • Checkpoint Stats section exists.
      • Candidate Patterns section has at least 3 patterns.
      • Each pattern includes: Description, trigger phrases (JA/EN), Confidence (0-1), Evidence, Existing skill overlap.
    • Recommendation quality:
      • Explicitly decide: "create new skill" vs "strengthen existing skill".
      • Prioritize top 1-3 actions by impact and effort.
  5. Report to user

    • Summarize checkpoint stats and top pattern suggestions.
    • Include output-quality gate result (pass/fail and missing items if any).
    • Ask whether to convert high-confidence patterns into new skills now.

Reporting Template

Use this minimal structure in the user-facing report:

  1. Checkpoint result: branch, commits, tasks completed, generated artifact paths.
  2. Quality gate: pass/fail with concrete checks performed.
  3. Top pattern suggestions: top 3 with confidence and overlap note.
  4. Recommended next action: single clear recommendation.

Command Contract

  • /checkpointing

When not to use it

  • When Claude subagents are required for skill pattern analysis
  • When only a partial session history update is desired
  • When checkpoint files should not be generated

Limitations

  • Analysis of skill patterns is performed without Claude subagents
  • Requires a Python interpreter to run the checkpoint script
  • Output quality gate enforces specific report structure

How it compares

This workflow automates the creation of session checkpoints and skill pattern analysis directly within Codex, providing structured reports and recommendations without manual data collection.

Compared to similar skills

checkpointing side by side with the closest alternatives in the catalog.

SkillInstallsUpdatedSafetyDifficulty
checkpointing (this skill)05moNo flagsIntermediate
triage-issue16moReviewIntermediate
proof-of-work16moNo flagsIntermediate
github-triage03moNo flagsIntermediate

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