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 L-LesterYu/OpenClaw-hot-skills-zh --skill persona-heartbeatgit clone --depth 1 https://github.com/L-LesterYu/OpenClaw-hot-skills-zhWrote 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/l-lesteryu/openclaw-hot-skills-zh/persona-heartbeat)<a href="https://agentmods.dev/skills/l-lesteryu/openclaw-hot-skills-zh/persona-heartbeat"><img src="https://agentmods.dev/badge/skills/l-lesteryu/openclaw-hot-skills-zh/persona-heartbeat/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/l-lesteryu/openclaw-hot-skills-zh/persona-heartbeat"><img src="https://agentmods.dev/badge/skills/l-lesteryu/openclaw-hot-skills-zh/persona-heartbeat.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 | $0.00118 | $0.02991 |
| Opus 5.5 | $0.00047 | $0.01196 |
| Sonnet 5.5 | $0.00024 | $0.00598 |
| Haiku 4.5 | $0.00012 | $0.00299 |
Grade A, and why
persona-heartbeat 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 2mo 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 — 400 lines — stays where its author put it; the contents beside it link to each section on GitHub.
人格心跳
AI 人格操作系统的环境监控和健康心跳系统。本技能处理程序化的上下文健康检查、主动建议展示、会话恢复和记忆维护。
阶段 1:上下文健康检查
目的: 监控上下文窗口使用量并根据阈值触发相应操作。
步骤 1.1:确定当前上下文使用量
通过可用的系统指标检查当前上下文窗口使用百分比。
context_usage_pct = get_context_window_usage()
computed.context_level = context_usage_pct
步骤 1.2:应用阈值逻辑并呈现用户可见消息
根据上下文使用量,确定用户看到的内容以及需要采取的操作:
| 使用范围 | 用户可见行为 |
|---|---|
| < 50% | 无 — 正常运行 |
| 50-69% | 无 — 内部记录以供跟踪 |
| 70-84% | "📝 上下文 [X]% — 继续前正在保存检查点。"然后委托给 persona-checkpoint |
| 85-94% | "🟠 上下文 [X]% — 已保存紧急检查点。建议尽快启动新会话。" |
| 95%+ | "🔴 上下文 [X]% — 严重。正在保存必要信息。请启动新会话。" |
if context_usage_pct >= 95:
show_critical_warning()
save_essential_state()
computed.action_taken = "critical_checkpoint"
elif context_usage_pct >= 85:
show_emergency_warning()
delegate_to_checkpoint()
computed.action_taken = "emergency_checkpoint"
elif context_usage_pct >= 70:
show_checkpoint_notice()
delegate_to_checkpoint()
computed.action_taken = "standard_checkpoint"
else:
computed.action_taken = "none"
步骤 1.3:记录阈值跨越事件
跟踪阈值跨越时间,避免在同一会话中重复通知。
if computed.action_taken != "none":
timestamp = current_timestamp()
append_to_session_log(threshold_event, timestamp)
阶段 2:主动建议引擎
目的: 在顾问模式启用时提供有用建议,遵循严格规则以避免噪音。
激活条件: 仅当 USER.md 中顾问模式为开启状态时。
步骤 2.1:检查顾问模式状态
user_config = read_file("~/workspace/USER.md")
advisor_enabled = parse_advisor_mode(user_config)
computed.advisor_active = advisor_enabled
步骤 2.2:评估建议条件
仅在满足所有条件时才展示建议:
- 发现了关于用户目标的重要新上下文
- 发现了未注意到的模式或机会
- 存在时间敏感的机会
- 当前未进行复杂任务
- 未超过每会话最多 1 条建议的限制
- 上一条建议未被忽略/拒绝
if not advisor_enabled:
return # 完全跳过建议引擎
suggestion_contexts = [
"new_goal_context",
"unnoticed_pattern",
"time_sensitive_opportunity"
]
blockers = [
"complex_task_active",
"session_quota_exceeded",
"previous_ignored"
]
if any_suggestion_context() and not any_blocker():
computed.suggestion_eligible = true
else:
computed.suggestion_eligible = false
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.
- 2mo ago First seen · 400 lines · 118 tokens per session scan A d29d22d97370
persona-heartbeat is a skill published in the GitHub repository L-LesterYu/OpenClaw-hot-skills-zh (57 stars, last pushed 7mo ago), licensed MIT. It adds 118 tokens to every session and 2,991 once invoked, about $0.0005 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 2026-08-30.
persona-heartbeat compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| persona-heartbeatL-LesterYu/OpenClaw-hot-skills-zh | Skill | 57 | 7mo ago | MIT | A | 118 | Claude Code |
| knowledge-ingestopenclaw/openclaw | Skill | 394k | today | MIT | A | 80 | Claude Code, Codex |
| mem0-oss-to-platformmem0ai/mem0 | Skill | 69k | yesterday | Apache-2.0 | A | 217 | Claude Code, Codex |
| forgetasgeirtj/system_prompts_leaks | Skill | 71k | today | CC0-1.0 | A | 0 | Claude Code, Codex |
| Cortexdanielmiessler/LifeOS | Skill | 20k | 2d ago | MIT | A | 184 | Claude Code |
| memoryopensquilla/opensquilla | Skill | 7.1k | yesterday | Apache-2.0 | A | 44 | Claude Code, Codex |
| ha-data-storesshiwenwen/hope-agent | Skill | 1.9k | today | MIT | A | 115 | Claude Code, Codex |