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OpenClaw 官方免费在 GitHub 公开中文介绍

knowledge-source-ingest

对聊天频道、问题列表、订阅源和邮箱等有明确范围的数据进行重复采集,保存快照清单,并比较重叠范围内的项目,识别新增、变更、删除及未解决项,供后续Knowledge流程使用。

原文查看原文说明

Incrementally ingest enumerable external sources into Knowledge with canonical source identity, retained snapshot manifests, overlap-aware diffing, and reconciliation of changed or deleted items. Includes a generic manifest tool and a read-only Discrawl adapter.

openclaw/openclaw星标 393,141(GitHub 393,141 + cccskills 0)
集成工具Discord

实测评分 未实测

查看安装方法

适用场景

  • 比较聊天导出的前后快照
  • 追踪问题列表的增删改
  • 记录快照的范围和时间
  • 准备Knowledge导入所需数据

中文介绍

功能

此技能负责为Knowledge流程准备有明确范围的外部数据,例如聊天频道、问题列表、订阅源和邮箱。它为每个来源维护稳定标识,并保存快照清单,记录覆盖范围、观察时间、项目数量等信息。多次采集之间可以进行比较,即使采集范围部分重叠,也能识别新增、变更、删除、未变化和未解决的项目。技能包含通用清单工具和只读的Discrawl适配器。它管理来源身份和覆盖范围;其他Knowledge导入流程负责把保留的内容整理成相互关联的知识。

适用场景

如果需要重复采集同一来源并了解相较上次的变化,可以使用此技能。采集范围部分重叠,或需要记录快照是完整还是部分时也适用。创建定期导入任务前,应先使用knowledge-automation。文档和对话应交给knowledge-ingest,会议则交给knowledge-meeting-ingest。

使用示例

  • “为这份频道导出文件生成清单,并与上一份快照比较。”
  • “制定计划,列出这个问题列表中的新增、变更和删除项目。”
  • “将已采集的会议数据交给Knowledge会议导入流程。”

注意事项

Discrawl适配器只读,采集数据需要经过批准的读取方式。此技能不会创建定期导入任务。不要把凭据或私密原始内容写入Knowledge页面正文。

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。

文档检查

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)。

安全性发送数据、删除文件、执行命令等是否有说明
B部分不足适配器只读且说明了凭据限制,但采集仍需批准的读取工具。
安装难易度所需工具和 API 密钥是否少且有说明
B部分不足需要Node脚本、获批读取工具和Knowledge工具,但读取工具的设置未说明。

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。

如果您认为评估有误,请申请复核

请写明哪一项有误以及原因。无需登录。我们会对照 SKILL.md 重新评估,并在此处更新结果。

此表单受 reCAPTCHA 保护,适用 Google 的隐私权政策和服务条款。

包含的文件(17)

  • SKILL.md5.4 KB
  • references/assertions.md4.6 KB
  • references/conventions.md14.3 KB
  • references/identity.md4.8 KB
  • references/meeting-entity-example.md6.8 KB
  • references/profiles.md2.8 KB
  • references/publication.md22.1 KB
  • references/publish.md3.0 KB
  • references/recovery.md2.0 KB
  • references/setup.md1.7 KB
  • references/source-manifest.md1.4 KB
  • references/sources.md3.0 KB
  • references/verify.md2.7 KB
  • references/writing.md1.9 KB
  • scripts/chat-transcript.mjs8.8 KB
  • scripts/discrawl-normalize.mjs7.4 KB
  • scripts/source-manifest.mjs14.1 KB

SKILL.md(原文)

安装前可以查看交给 AI 代理的指令内容。

Source Ingest Skill

Use this for bounded collections such as chat channels, issue streams, feeds, or mailboxes where later runs can overlap prior coverage. Read shared conventions, publication, and the manifest protocol. After acquisition, route documents and conversations through knowledge-ingest, which uses knowledge-entities for entity enrichment. Route meetings through knowledge-meeting-ingest. This skill owns source identity and coverage; the ingestion workflows turn the retained content into useful connected knowledge. For creating recurring ingestion jobs, use knowledge-automation first.

Contract

  • Resolve a stable provider/source key before reading content. Display names are labels, never identity.
  • Maintain one canonical source page with source_role: canonical, provider, and source_key. Reuse it on every run.
  • For each bounded observation, preserve the generated manifest as an attachment on a source page with source_role: snapshot, exact coverage, observation time, manifest hash, count, completeness, watermark, and dependency on the canonical source page.
  • Manifests retain stable item IDs and content hashes, not source text. Preserve original/exported content separately as evidence where authorized.
  • Query prior snapshot pages for the same provider/source key before planning. Download their manifests, verify attachment hashes, and pass all overlapping snapshots to the planner. Do not rely on conversation memory.
  • Reuse unchanged source bytes and existing records. Continue previously omitted enrichment from those retained sources when requested; an unchanged manifest does not mean entity enrichment already happened. Ingest added items. Reconcile changed items against existing pages/claims. Treat deletion as observed source state, not permission to erase Knowledge; correct or withdraw only when the evidence warrants it.
  • A complete snapshot can establish absence inside its exact coverage. Partial or unknown snapshots cannot; unresolved missing IDs remain unresolved.

Procedure

  1. Inspect knowledge_status and knowledge_ontology for the intended vault scope and the source fields, then search Knowledge for the canonical source and prior snapshots by provider/source key. If an owner-private ingestion unexpectedly resolves to a conversation scope or an empty vault after a restart, stop before writing and restore the intended authorized scope; never infer that prior coverage is absent from the wrong scope.
  2. Acquire a bounded export with an approved reader. For Discrawl, run scripts/discrawl-normalize.mjs with its configured read-only archive, exact channel ID when known, and [since,before) RFC3339 bounds. The normalized payload includes stable author IDs plus archive-observed display fields. Preserve the raw JSONL, and create a separate line-stable readable transcript when human review or line citations are needed.
  3. Generate a deterministic manifest with scripts/source-manifest.mjs snapshot. Attach it before publishing the snapshot page; record the returned SHA-256.
  4. Download all overlapping prior manifests and run scripts/source-manifest.mjs plan. Inspect covered/uncovered intervals and the added/changed/deleted/unchanged/unresolved lists.
  5. Test 3–5 substantive items or discussions, then ingest through knowledge-ingest (or knowledge-meeting-ingest for meetings). For chat, group related messages with their reply context; use knowledge-entities to enrich relevant people, companies, projects and concepts from their contributions. Carry supported developments into State, dated timelines and contextual back-links, not only a channel digest. Use stable external IDs in source citations and the retained snapshot as a dependency. Reuse its originals rather than uploading one copy per discussion.
  6. Publish or update derived records, then publish the snapshot page. A snapshot is complete only when acquisition and manifest creation covered the declared interval; derived extraction may still be partial and must be reported as such.
  7. Read back the canonical page, snapshot page, attachment, derived records, and source-dependent connections. Verify hashes and exact coverage.

Discrawl example

node scripts/discrawl-normalize.mjs --config /approved/discrawl.yaml \
  --channel 1234567890 --since 2026-09-21T00:00:00-07:00 \
  --before 2026-09-28T00:00:00-07:00 --output messages.jsonl
node scripts/source-manifest.mjs snapshot --provider discord \
  --source-key discord:GUILD_ID:1234567890 \
  --coverage-start 2026-09-21T00:00:00-07:00 \
  --coverage-end 2026-09-28T00:00:00-07:00 \
  --input messages.jsonl --output manifest.jsonl.gz --summary snapshot.json
node scripts/source-manifest.mjs plan --current manifest.jsonl.gz \
  --previous prior-1.jsonl.gz --previous prior-2.jsonl.gz --output plan.json

Do not include credentials, archive paths, or raw private content in Knowledge page prose. No scheduler, connector daemon, or plugin-owned ingestion job is created; the skill and scripts support an authorized agent-run workflow.

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