checkpointing
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.zipInstalls 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.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
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
-
Run checkpoint collection
- Command:
python .claude/skills/checkpointing/checkpoint.py- or
python .claude/skills/checkpointing/checkpoint.py --since YYYY-MM-DD
- If
pythonis unavailable, use:python3 .claude/skills/checkpointing/checkpoint.py- or
python3 .claude/skills/checkpointing/checkpoint.py --since YYYY-MM-DD
- Command:
-
Verify generated artifacts
- Confirm latest files exist:
.claude/checkpoints/*.md.claude/checkpoints/*.analyze-prompt.md
- Confirm
CLAUDE.mdsession history was updated. - Extract the checkpoint UTC timestamp and reuse it for every derived artifact.
- Confirm latest files exist:
-
Analyze skill patterns without Claude subagents
- Read the generated
.analyze-prompt.mdin 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.
- Use the same UTC timestamp as checkpoint filename (example:
- Read the generated
-
Enforce output quality gate
- Artifact consistency:
checkpoint,analyze-prompt, andskill-patternsfiles all share the same timestamp.- New timestamped report is created (
skill-patterns-{timestamp}.md), not date-only overwrite.
- Analysis structure (required):
Checkpoint Statssection exists.Candidate Patternssection 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.
- Artifact consistency:
-
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:
Checkpoint result: branch, commits, tasks completed, generated artifact paths.Quality gate: pass/fail with concrete checks performed.Top pattern suggestions: top 3 with confidence and overlap note.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.
| Skill | Installs | Updated | Safety | Difficulty |
|---|---|---|---|---|
| checkpointing (this skill) | 0 | 5mo | No flags | Intermediate |
| triage-issue | 1 | 6mo | Review | Intermediate |
| proof-of-work | 1 | 6mo | No flags | Intermediate |
| github-triage | 0 | 3mo | No flags | Intermediate |
Try saying
Example prompts that trigger this skill in your AI assistant.
You might also like
triage-issue
mysticaltech
Use when triaging a GitHub issue - analyzes issue, checks for duplicates, categorizes, and drafts response
proof-of-work
MadAppGang
Proof artifact generation patterns for task validation. Covers screenshots, test results, deployments, and confidence scoring.
github-triage
OutlineDriven
Triage GitHub issues through a configurable label-based state machine. Use when user wants to triage incoming issues, prepare issues for an autonomous agent, or move an issue between workflow states. Repo inferred from `git remote`; all GitHub calls go through `gh`.
label-issue
chagong
Classify and label GitHub issues based on repository-specific labeling instructions. Use when (1) auto-labeling new issues, (2) classifying issue types (bug, feature, etc.), (3) adding priority or area labels, (4) applying consistent labeling rules. Triggers on requests like "label issue", "classify
resolve-conflicts
antinomyhq
Use this skill immediately when the user mentions merge conflicts that need to be resolved. Do not attempt to resolve conflicts directly - invoke this skill first. This skill specializes in providing a structured framework for merging imports, tests, lock files (regeneration), configuration files, and handling deleted-but-modified files with backup and analysis.
git-advanced-workflows
wshobson
Master advanced Git workflows including rebasing, cherry-picking, bisect, worktrees, and reflog to maintain clean history and recover from any situation. Use when managing complex Git histories, collaborating on feature branches, or troubleshooting repository issues.