Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/flonat/flonat-research/checkpointnpx skills add flonat/flonat-research --skill checkpointgit clone --depth 1 https://github.com/flonat/flonat-researchWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/flonat/flonat-research/checkpoint)<a href="https://agentmods.dev/skills/flonat/flonat-research/checkpoint"><img src="https://agentmods.dev/badge/skills/flonat/flonat-research/checkpoint.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00051 | $0.00942 |
| Opus 5 | $0.00026 | $0.00471 |
| Sonnet 5 | $0.00010 | $0.00188 |
| Haiku 4.5 | $0.00005 | $0.00094 |
Grade A, and why
checkpoint scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 103 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Checkpoint — Save Session State
Snapshot the current session's progress to a YAML file so it can be restored after context compaction or in a new session. This is NOT a session log (narrative) or context save (profile/focus) — it's a machine-readable state dump for continuity.
When to Use
- Before a session is likely to hit context compression (many tool calls, long outputs)
- At phase boundaries in multi-phase work
- When the user says "save state", "checkpoint", "save progress"
- Proactively when you sense context is getting large — don't wait to be asked
When This Skill Is Invoked
Step 1: Gather State
Collect the following from the current conversation context:
- Project — which project directory are we working in?
- Task — what was the user's original request this session?
- Files modified — list every file you've created or edited this session (use
git diff --name-onlyif in a git repo, plus any new untracked files) - Decisions made — key choices and their rationale (e.g., "chose LaTeX over Markdown because...", "used sonnet for cron jobs to reduce cost")
- Current step — where in the plan are we? What just completed? What's next?
- Blocked items — anything that couldn't be completed and why
- Open questions — unresolved ambiguities that need user input
Step 2: Write Checkpoint
Write to .planning/checkpoint.yaml in the active project directory. If no project directory is obvious, write to the Task Management root.
# Auto-generated by checkpoint — do not edit manually
created: "YYYY-MM-DDTHH:MM:SS"
session_id: "<from $CLAUDE_SESSION_ID if available>"
project: "<project directory basename>"
task: |
<one-paragraph description of what the user asked for>
files_modified:
- path: "relative/path/to/file.md"
action: "created|edited|deleted"
summary: "what was changed and why"
decisions:
- decision: "what was decided"
rationale: "why"
progress:
completed:
- "step 1 description"
- "step 2 description"
current: "what step we're on now"
next:
- "next step 1"
- "next step 2"
blocked:
- item: "what's blocked"
reason: "why"
open_questions:
- "question that needs user input"
context_files:
- "list of files that should be re-read on restore"
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 2d ago First seen · 103 lines · 51 tokens per session scan A 49eca79120f3
checkpoint is a skill published in the GitHub repository flonat/flonat-research (132 stars, last pushed 11d ago), licensed MIT. It adds 51 tokens to every session and 942 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
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Use when the user asks about Claude Code best practices, how to write agents, how to use hooks, how to manage context, or how to work effectively with Claude Code's native systems.
review-plan
Review a proposed plan with a single-pass structured critique and a clear verdict. Use when the user asks to review a plan, stress-test a plan, critique a workflow, identify risks, or improve an execution sequence.