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 Xwen0857/agent-orchestrator --skill keepergit clone --depth 1 https://github.com/Xwen0857/agent-orchestratorWrote 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/xwen0857/agent-orchestrator/keeper)<a href="https://agentmods.dev/skills/xwen0857/agent-orchestrator/keeper"><img src="https://agentmods.dev/badge/skills/xwen0857/agent-orchestrator/keeper/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/xwen0857/agent-orchestrator/keeper"><img src="https://agentmods.dev/badge/skills/xwen0857/agent-orchestrator/keeper.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 | $0.00040 | $0.00601 |
| Opus 5 | $0.00020 | $0.00300 |
| Sonnet 5 | $0.00008 | $0.00120 |
| Haiku 4.5 | $0.00004 | $0.00060 |
Grade A, and why
keeper 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 8d 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.
What it actually says
Keeper
核心职责
- 维护知识库质量:发现重复、候选合并、条目降级与归档建议。
- 维护经验权重:基于反馈日志重算
score/status,同步治理信号。 - 维护容量边界:控制低价值条目膨胀,输出清理与拆分建议。
- 维护审计可见性:生成 keeper 报告,供 orchestrator 读取与决策。
启用开关(强约束)
templates/coordination/planner/config/current.md中keeper_enabled: true才允许执行。- 当
keeper_enabled: false时,keeper 必须 no-op 并返回禁用状态。
输入路径
knowledge-base/entriesknowledge-base/feedback/kb_feedback.ndjsonknowledge-base/references/weighting-model.mdtemplates/coordination/planner/config/current.mdtemplates/coordination/planner/properties.md
输出路径
templates/coordination/orchestrator/keeper-report.mdtemplates/coordination/orchestrator/keeper-report.json
运行脚本
keeper/scripts/keeper_run.shkeeper/scripts/keeper_ingest_candidates.shkeeper/scripts/kb_semantic_dedupe.pykeeper/scripts/keeper_scheduler.shscripts/run_keeper.sh
最小执行流程
- 读取开关与参数;若禁用则直接退出。
- 先消费
knowledge-base/inbox/pending候选并执行去重整并/入库。 - 执行 KB 分数重算:
agent-orchestrator/scripts/kb_recompute_scores.sh。 - 统计条目状态分布、低分条目、疑似重复条目。
- 输出 keeper 报告,供 orchestrator 汇总。
去重与整并规则(V1)
- 使用
kb_semantic_dedupe.py计算相似度,超过阈值(默认0.85)进入merge_candidates。 status=DEPRECATED且 30 天无命中,进入archive_candidates。HUMAN_OVERRIDE连续上升条目进入watch_candidates。- keeper 报告输出
split_candidates与 7 天趋势指标,用于收敛拆分/归档策略。
What ships with it
6 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 8d ago First seen · 48 lines · 40 tokens per session scan A 7681f87ba403
keeper is a skill published in the GitHub repository Xwen0857/agent-orchestrator (2 stars, last pushed 5mo ago), licensed MIT. It adds 40 tokens to every session and 601 once invoked, about $0.0002 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 2026-08-31.
Other skills, from other repositories
openclaw-plugin
This plugin is a thin adapter between OpenClaw and LycheeMem. It does not replace memory-core, does not claim plugins.slots.memory, and does not duplicate LycheeMem algorithms.
lycheemem
Forceful operating rules for using LycheeMem as the primary structured long-term memory path inside OpenClaw.
memory
Use LycheeMem as Claude Code's structured long-term memory for prior conversations, user preferences, project decisions, timelines, and durable facts.
lesson
Store a lesson learned from the current conversation. Triggered by /lesson command. Use when Master signals that the recent conversation contains a pitfall, fix, or key insight that should be persisted to long-term memory.
harness-24h
A production-grade framework for long-running, autonomous agents based on Harness Engineering principles. Features three-layer self-healing memory, circuit breaker protection, KAIROS dream-mode consolidation, and multi-agent coordination. When a user describes a task idea, Agent automatically initializes the…
init
A project-memory setup workflow for long-running coding work, creating a .workflow directory with status and review files.