检查1Password命令行工具的安装和认证,将已保存的密码或API密钥传入命令及配置。支持桌面应用集成、服务账号和交互式登录,并先验证访问权限。
- 安装1Password命令行工具
- 将API密钥传入命令
- 在CI中使用服务账号认证
整理Knowledge中自动重连后仍未关联的记录,依据证据选择主题页面,必要时新建页面,并处理重复或已被明确替代的记录。长期有效的结论会附上记录链接后写入页面。
Review remembered Knowledge claims still unlinked after deterministic relinking. Assign subject pages, create pages for substantial missing subjects, retire exact duplicates and explicitly replaced claims, and fold durable outcomes into page State. Use for unlinked-claim cleanup or the scheduled review that follows Knowledge maintenance.
实测评分 未实测
查看安装方法此技能检查Knowledge自动重连后仍未关联的记录,按主题归组,并查看相关页面及其历史,再将记录关联到证据支持的页面。若某个重要主题尚无页面,可以创建页面并关联到上级页面。它还会识别内容完全重复的记录,以及被后续记录明确纠正、补充结论或报告最终状态的记录;撤销旧记录时会注明理由。决策、已完成的变更等长期有效的结果,可以连同支持它们的记录链接写入页面状态。进度快照保留在时间线中,不作为长期状态写入。
当未关联记录不断增加,相关页面又无法引用它们时,可以使用此技能。它也适合整理某项公关记录或决策的来龙去脉,同时保留历史。如果主题或身份无法确定,记录会保持未关联,并报告原因。作为定期检查运行时,它会再次查看剩余记录,并报告需要人工判断的项目。
设置定期检查需要使用能够访问所有者私有Knowledge的会话。写入前会先查看共享规范和发布流程。
本介绍由 AI(OpenAI Codex)根据公开的 SKILL.md 撰写。准确的规格请查看下方原文。
AI 根据 SKILL.md 的内容检查了需要注意的操作和使用前提。评分只依据实际运行的结果(实测对比)。
不计入评分。 由 AI(OpenAI Codex)于 2026年10月26日 评估。仅供参考,安装前请同时查看原文。
安装前可以查看交给 AI 代理的指令内容。
Read shared conventions and publication before writes.
knowledge_remember saves a claim without a subject page when no name in it
matches exactly one page. Scheduled maintenance then runs knowledge_relink,
which links only claims whose recorded source page is an entity page or whose
text names exactly one page. The rest stay unlinked and are invisible from the
pages they concern. This skill handles that residue with judgment. Work in
this order: link, then duplicates and replaced claims, then page prose.
Call knowledge_status for the intended scope and knowledge_ontology for the
current fingerprint. Run knowledge_relink without dryRun first so every
remembered claim it can link is linked. Then list the residue with
knowledge_relink dryRun: true, include: "all" and limit: 10, following
nextCursor: every current claim with no linked page and no source page,
whichever tool saved it. Each call reports at most 10 examples per outcome, so a
limit of 10 returns every claim it examined.
Read each claim with knowledge_record and metadataView: "full". Its
subject_hint, when present, is the saver's own description of what the claim
is about and the strongest clue to its subject. Group the claims by what they are about
(one feature, PR, decision thread, person or preference) before choosing pages.
Most claims make sense only next to their neighbours: a series of status
snapshots about one PR usually ends in an outcome claim.
For each group, find candidate pages with knowledge_grep and
knowledge_lookup, and read their State and Timeline. Ignore withdrawn pages and
fixture pages.
Link with knowledge_annotate entities, passing the complete list (it replaces
the existing one) and the claim's current revision and hash. Assertion text,
evidence and lifecycle stay unchanged.
Linking every claim to the nearest existing page can bury a real subject in a broad page's Timeline. Create a page when:
gh pr view).Do not create a page for a single mention or a one-off fix that a Timeline entry
covers. Use knowledge_save_page with the ontology type, aliases, and the
identity evidence as source. Person pages use identity.status: partial unless
the identity is fully established. Write a short State that cites the claims and
a dated Timeline. Then link the claims to the new page, keeping their existing
subjects as secondaries, and link the new page from its parent page.
knowledge_annotate addEvidence, then knowledge_withdraw the
other with a reason that names the survivor's ID.knowledge_correct here: it publishes a new copy of the
replacement, which creates another duplicate.Linking makes the claims reachable from their pages; it does not change what a page says. Edit a page only when the claims change its durable state: a decision, a preference, a landed or released change, a new capability, a resolved open question, or page text that later claims show to be wrong.
gh pr view for merge state) and say when you checked. A merge is not a
deployment, and a deployment is not proof that a given Gateway runs the code.[[claim-id|label]] links to the claims.Use knowledge_edit_body for section edits. Compute UTF-16 offsets from a fresh
full read, and reread the revision and hash before every edit: other sessions
edit busy pages concurrently. Prefer inserting text over replacing it. Replace a
whole State section only when all of it is stale, and keep the claim links it
already had.
Run knowledge_verify with expectedEntities for each linked claim and
expectedLinks for each new or edited page. Enumerate the residue again: new
remembered claims may have arrived while you worked. Handle them the same way
or report them.
Report the number of claims examined and linked, pages created, claims withdrawn (with the surviving or replacing claim for each), pages edited, and claims left unlinked with the reason for each. When running unattended, never ask for a decision: leave uncertain identities, uncertain duplicates and pages you would only create on a guess untouched, and list them in the report for a person.
Knowledge maintenance queues this review after its nightly relink pass when
private remembered claims are still unlinked (maintenance.reviewJobId). The
run is unattended:
knowledge_status. If the scope is not private or writes are
disabled, reply with AUTOMATION_FAILED on its own first line and the reason:
the worker has lost the owner's access and needs to be set up again.NO_REPLY. Otherwise reply with the step 6 report.The review needs owner-private access, which only a session descended from the owner's direct conversation has. Set it up from that conversation:
Spawn a kept worker: sessions_spawn with runtime: "subagent",
cleanup: "keep" and context: "isolated", whose task is to call
knowledge_status and report the scope. Continue only if it reports
private with writes enabled. Keep the returned session key.
Create the automation bound to that worker. It needs a schedule, so give it a one-shot date years away; maintenance runs it on demand.
openclaw automations add --agent <agent> --name knowledge-unlinked-claims-review \
--session "session:<worker-session-key>" --at <date-years-ahead> --keep-after-run \
--model <provider/model> --no-deliver --timeout-seconds 3600 \
--message "Follow the knowledge-unlinked-claims skill as the scheduled review."
The job records the inference account it was created with. Choose a model that uses that account (a provider with an auth profile); otherwise every run fails before the model is called.
Set plugins.entries.knowledge.config.maintenance.reviewJobId to the job ID.
Prove it: openclaw automations run <job-id> --wait must finish with the
worker reporting the private scope, and maintenance_status reports the
review outcome after the next pass.
Disable the automation to pause the review. Resetting or removing the owner conversation the worker came from revokes its access; set the review up again.
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检查1Password命令行工具的安装和认证,将已保存的密码或API密钥传入命令及配置。支持桌面应用集成、服务账号和交互式登录,并先验证访问权限。
将提交给OpenClaw的自然语言请求转交给Claude Code等外部编程智能体,通过ACP或acpx启动、继续会话,并保留对话上下文。
为OpenClaw添加模型服务商,并通过实际回复验证连接。凭据以环境变量或文件引用的形式配置,同时验证设置并报告测试结果。
将AI代理的工作日志整理为可附在GitHub拉取请求或议题中的记录,筛选相关内容、移除敏感信息,并支持预览及经单独授权后的发布。
通过代理管理macOS上的Apple Notes笔记,支持创建、查看、搜索、编辑和删除,在文件夹之间移动笔记,并将选定笔记导出为HTML或Markdown。
通过Mac查看、添加和编辑Apple Reminders中带截止时间的待办事项,管理列表并完成或删除任务,整理在iPhone或iPad上查看的个人任务。