persona-checkpoint

persona-checkpoint is a skill for Claude Code from L-LesterYu/OpenClaw-hot-skills-zh. It costs 177 tokens per session (2,016 once invoked), scanned A, original, MIT.

A system for saving an AI conversation's progress at checkpoints. A checkpoint records enough context, decisions, and next steps to continue after a long session or interruption.

In plain words
What is it for?
Use it to preserve work before major changes, track decisions and action items, and resume an interrupted task.
Why use it?
It reduces the risk of losing important context when a conversation becomes large or ends unexpectedly.

Skill for Claude Code

Written for Claude Code: allowed-tools in frontmatter.

Part of the hiivmind-openclaw-os plugin — 6 skills, 1 command shipped together

Good fit Use it to preserve work before major changes, track decisions and action items, and resume an interrupted task.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/l-lesteryu/openclaw-hot-skills-zh/persona-checkpoint
Install

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.

Any agent
npx skills add L-LesterYu/OpenClaw-hot-skills-zh --skill persona-checkpoint
Clone the repo
git clone --depth 1 https://github.com/L-LesterYu/OpenClaw-hot-skills-zh

Made for: Claude Code.

Or install hiivmind-openclaw-os, the plugin that ships this one along with the rest of its 6 skills, 1 command.

Wrote 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.

agentmods badge for persona-checkpoint

README.md
[![agentmods](https://agentmods.dev/badge/skills/l-lesteryu/openclaw-hot-skills-zh/persona-checkpoint/github.svg)](https://agentmods.dev/skills/l-lesteryu/openclaw-hot-skills-zh/persona-checkpoint)
Your own site
<a href="https://agentmods.dev/skills/l-lesteryu/openclaw-hot-skills-zh/persona-checkpoint"><img src="https://agentmods.dev/badge/skills/l-lesteryu/openclaw-hot-skills-zh/persona-checkpoint/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.

agentmods 80×15 button for persona-checkpoint

Your own site · 80×15
<a href="https://agentmods.dev/skills/l-lesteryu/openclaw-hot-skills-zh/persona-checkpoint"><img src="https://agentmods.dev/badge/skills/l-lesteryu/openclaw-hot-skills-zh/persona-checkpoint.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 177 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,016 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00177 $0.02016
Opus 5 $0.00088 $0.01008
Sonnet 5 $0.00035 $0.00403
Haiku 4.5 $0.00018 $0.00202

Measured 12d ago against content hash 59145389b770, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

persona-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 12d 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.

skills/ai-persona-os-zh/skills/persona-checkpoint/SKILL.md · 209 lines

How it starts

The opening of the file, as written. The whole thing — 209 lines — stays where its author put it; the contents beside it link to each section on GitHub.

人格检查点 — Never-Forget 协议

本技能实现 AI 人格操作系统的上下文保护和检查点功能,防止上下文丢失,实现 95% 的会话恢复准确率。

阶段 1:评估上下文窗口使用量

步骤 1.1: 检查当前上下文窗口使用百分比

根据对话长度、工具调用和复杂性计算或估算当前上下文窗口利用率。

步骤 1.2: 确定严重程度阈值

使用量 状态 所需操作
< 50% 🟢 正常 随时记录决策(轻量级)
50-69% 🟡 警觉 增加检查点频率(每约 5 次交互)
70-84% 🟠 活跃 停止 — 立即写入完整检查点
85-94% 🔴 紧急 紧急转储 — 仅保留必需信息(任务 + 恢复点)
95%+ ⚫ 严重 生存模式 — 仅保留恢复所需的最低限度信息

步骤 1.3: 应用用户可见性规则

  • < 70%:静默操作,不通知用户
  • 70-84%:通知用户:"上下文已达到 XX% — 正在写入检查点"
  • 85-94%:警告用户:"⚠️ 上下文 XX% 处需要紧急检查点"
  • 95%+:严重警告:"🚨 上下文已达到临界限制 — 生存模式检查点"

阶段 2:确定检查点操作

步骤 2.1: 根据阈值选择检查点类型

if context < 50%:
    checkpoint_type = "lightweight"  # 仅内联决策
elif 50% <= context < 70%:
    checkpoint_type = "vigilant"     # 每约 5 次交互,轻量格式
elif 70% <= context < 85%:
    checkpoint_type = "full"         # 包含推理的完整检查点
elif 85% <= context < 95%:
    checkpoint_type = "emergency"    # 仅任务 + 恢复点
else:  # 95%+
    checkpoint_type = "survival"     # 绝对最低限度

步骤 2.2: 检查强制触发条件

如果出现以下情况,覆盖基于阈值的逻辑:

  • 用户明确说"checkpoint"/"检查点"(强制完整检查点)
  • 重大决策之前(架构变更、破坏性操作)
  • 自然会话暂停时(任务完成、上下文切换)
  • 有风险的操作之前(数据删除、重构、迁移)
  • 正常操作中每约 10 次交互(主动)

阶段 3:写入检查点

步骤 3.1: 创建检查点文件路径

使用 Bash 创建目录并确定文件路径:

mkdir -p ~/workspace/memory
echo "~/workspace/memory/$(date +%Y-%m-%d).md"

步骤 3.2: 根据类型格式化检查点内容

轻量级(< 50%):

## 检查点 [HH:MM] — 上下文:XX%

**决策:** [做出了什么决策]

警觉型(50-69%):

## 检查点 [HH:MM] — 上下文:XX%

**当前任务:** [当前工作]
**继续从:** [下一步]

完整检查点(70-84%):

## 检查点 [HH:MM] — 上下文:XX%

**当前任务:** [我们正在做什么]

**关键决策:**
- [决策 1 及推理]
- [决策 2 及推理]

**待办事项:**
- [ ] [任务 1](负责人:[用户/助手])
- [ ] [任务 2](负责人:[用户/助手])

**当前状态:** [进展摘要]

**继续从:** [精确的下一步及上下文]

紧急型(85-94%):

## ⚠️ 紧急检查点 [HH:MM] — 上下文:XX%

**任务:** [一句话描述]
**继续:** [精确的下一步操作]

生存型(95%+):

## 🚨 生存检查点 [HH:MM] — 上下文:XX%

**继续:** [最低可行下一步]

步骤 3.3: 将检查点写入文件

使用 Bash 将检查点追加到当天的文件:

cat >> ~/workspace/memory/$(date +%Y-%m-%d).md << 'EOF'
[来自步骤 3.2 的格式化检查点内容]
EOF

Read the full file on GitHub · 209 lines

Changes

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.

  1. 12d ago First seen · 209 lines · 177 tokens per session scan A 59145389b770

Subscribe to this mod's changes

persona-checkpoint is a skill published in the GitHub repository L-LesterYu/OpenClaw-hot-skills-zh (54 stars, last pushed 5mo ago), licensed MIT. It adds 177 tokens to every session and 2,016 once invoked, about $0.0009 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 2026-08-30.

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