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 skills add latestaiagents/agent-skills --skill agent-checkpointinggit clone --depth 1 https://github.com/latestaiagents/agent-skillsWrote 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/latestaiagents/agent-skills/agent-checkpointing)<a href="https://agentmods.dev/skills/latestaiagents/agent-skills/agent-checkpointing"><img src="https://agentmods.dev/badge/skills/latestaiagents/agent-skills/agent-checkpointing/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/latestaiagents/agent-skills/agent-checkpointing"><img src="https://agentmods.dev/badge/skills/latestaiagents/agent-skills/agent-checkpointing.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00067 | $0.02585 |
| Opus 5.5 | $0.00027 | $0.01034 |
| Sonnet 5.5 | $0.00013 | $0.00517 |
| Haiku 4.5 | $0.00007 | $0.00259 |
Grade A, and why
agent-checkpointing 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 1mo 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 — 407 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Agent Checkpointing
Save, restore, replay, and debug agent execution with checkpoints.
Why Checkpointing?
- Recovery: Resume from failure without losing progress
- Debugging: Replay exact execution path
- Branching: Try different paths from same checkpoint
- Audit: Complete history of agent decisions
- Testing: Reproduce specific scenarios
Checkpoint Architecture
┌─────────────────────────────────────────────────────────────┐
│ Checkpoint Timeline │
│ │
│ CP-1 CP-2 CP-3 CP-4 │
│ │ │ │ │ │
│ ▼ ▼ ▼ ▼ │
│ ┌───┐ ┌───┐ ┌───┐ ┌───┐ │
│ │ S │──────▶│ S │──────▶│ S │──────▶│ S │ │
│ └───┘ └───┘ └───┘ └───┘ │
│ State State State State │
│ + + + + │
│ Metadata Metadata Metadata Metadata │
│ │ │
│ │ Rewind here │
│ ▼ │
│ ┌───┐ │
│ │ S │──────▶ New Branch │
│ └───┘ │
│ │
└─────────────────────────────────────────────────────────────┘
Basic Checkpointing
Setup
from langgraph.graph import StateGraph, END
from langgraph.checkpoint.sqlite import SqliteSaver
from langgraph.checkpoint.postgres import PostgresSaver
# Development: SQLite
checkpointer = SqliteSaver.from_conn_string("./checkpoints.db")
# Production: PostgreSQL
checkpointer = PostgresSaver.from_conn_string(
"postgresql://user:pass@host/db"
)
# Compile graph with checkpointer
app = workflow.compile(checkpointer=checkpointer)
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.
- 1mo ago First seen · 407 lines · 67 tokens per session scan A e48476ef2e9f
agent-checkpointing is a skill published in the GitHub repository latestaiagents/agent-skills (6 stars, last pushed 6mo ago), licensed MIT. It adds 67 tokens to every session and 2,585 once invoked, about $0.0003 per session on Opus 5.5. 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 2026-08-31.
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