Ejecuta cualquier Skill en Manus
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Ejecuta cualquier Skill en Manus con un clic

zeabur-rag-triage

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Actualizado13 de abril de 2026 a las 08:47

List pending knowledge base maintenance items — open reports, unverified learned chunks, low-score queries, and negative feedback. Use when checking KB health, reviewing the maintenance queue, or starting a curation session. Triggers on "review queue", "what needs attention in the KB", "pending reports", "unverified chunks", "failed queries", "KB health check". Requires admin scope.

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zeabur

zeabur/rag-service

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SKILL.md

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name	zeabur-rag-triage
description	List pending knowledge base maintenance items — open reports, unverified learned chunks, low-score queries, and negative feedback. Use when checking KB health, reviewing the maintenance queue, or starting a curation session. Triggers on "review queue", "what needs attention in the KB", "pending reports", "unverified chunks", "failed queries", "KB health check". Requires admin scope.

RAG — Triage

Show what needs attention in the knowledge base. Returns 5 categories of pending items in a single call.

Base URL: $ZEABUR_RAG_URL Auth: Authorization: Bearer $RAG_API_KEY — admin scope required

API

curl -s "$ZEABUR_RAG_URL/api/admin/triage?days=14&similarity_threshold=0.4&limit=20" \
  -H "Authorization: Bearer $RAG_API_KEY"

Param	Default	Description
`days`	`14`	Time window for signals (reports and learned have no time window)
`similarity_threshold`	`0.4`	Queries with `top_similarity` below this are flagged. Note: in hybrid mode (RRF), scores are ~0–0.02 so the default 0.4 catches all hybrid queries. Adjust to ~0.005 for meaningful hybrid-mode filtering.
`limit`	`20`	Max items per category

Response

Returns JSON with 5 arrays + counts (true totals, not truncated) + has_more (boolean per category):

open_reports — reports with status='open', no time window. Fields: id, type, chunk_id, query, detail, created_at.
unverified_learned — learned chunks with status='unverified', no time window. Fields: id, title, tags, created_at, age_days.
low_similarity_signals — queries where the top score fell below the threshold, within the time window, deduped by query text. The score is RRF in hybrid mode (~0–0.02) or cosine in semantic mode (0–1). Fields: id, query, top_similarity, top_chunk_ids, client, created_at.
negative_feedback_signals — queries that got negative user feedback, within the time window. Fields: id, query, feedback_score, feedback_comment, top_chunk_ids, created_at.
needs_frontmatter — chunks whose tags include "needs-frontmatter", meaning they were ingested from a source file that lacks proper frontmatter and got a temporary ID. Fields: id, title, tags, url, source, created_at.

If a category query fails, its array is empty and a <category>_error field appears with the error message.

After triage

To drill into a specific chunk: use zeabur-rag-inspect with the chunk ID.
To start the full curation loop: use zeabur-rag-curate.
Do not mutate anything directly from triage results. Always inspect first, then decide on action.

When all categories are empty

If every count is 0, the knowledge base has no pending maintenance. Report: "The knowledge base has no pending items — looks healthy ✅"

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Contribute new verified knowledge to the RAG knowledge base. Use after you've solved a concrete problem, debugged a non-obvious issue, or discovered undocumented behavior that would help future agents — but only after calling zeabur-rag-search first to confirm the knowledge isn't already covered. Only contribute verified facts, not guesses or workarounds. The new chunk is marked unverified until an admin reviews it.

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zeabur-rag-curate

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Interactive knowledge base curation loop — reviews pending items (reports, unverified chunks, failed queries, negative feedback) and walks through fixes one by one. Use when asked to "maintain the knowledge base", "review pending items", "run a curation round", "process reports", "clean up the KB", or any request to improve KB quality. This is the main entry point for the expand → organize → reflect cycle, and requires admin scope.

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zeabur-rag-feedback

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Send feedback on RAG search results — thumbs-up (+1) or thumbs-down (-1) to indicate whether the answer was helpful. Use after calling zeabur-rag-search when you can evaluate the quality of the results. Positive feedback reinforces good results; negative feedback flags poor matches for review. Requires write:feedback scope.

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zeabur-rag-inspect

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View a single chunk's full content plus related reports, search signals, and edit history. Use when you need to understand a chunk's context before editing, when investigating why a search result appeared, or when reviewing a reported issue. Triggers on "inspect chunk SUP-1234", "show chunk DOC-xxx", "chunk history", "why did this result appear", "look at this chunk". Requires admin scope.

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zeabur-rag-search

zeabur/rag-service

Search the RAG knowledge base via hybrid search (semantic + BM25). Use whenever answering technical questions that may be covered in your knowledge base — even if the user doesn't explicitly ask you to search. Also use when you need to verify claims or find up-to-date information.

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zeabur-rag-edit

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Edit the content of an existing chunk in the RAG knowledge base. Use when a chunk has a typo, outdated info, wrong tags, wrong visibility, or needs its title/question/answer rewritten — and you want the fix to take effect immediately. Requires admin scope. Prefer zeabur-rag-report for surfacing issues without write access.

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name	zeabur-rag-triage
description	List pending knowledge base maintenance items — open reports, unverified learned chunks, low-score queries, and negative feedback. Use when checking KB health, reviewing the maintenance queue, or starting a curation session. Triggers on "review queue", "what needs attention in the KB", "pending reports", "unverified chunks", "failed queries", "KB health check". Requires admin scope.

RAG — Triage

Show what needs attention in the knowledge base. Returns 5 categories of pending items in a single call.

Base URL: $ZEABUR_RAG_URL Auth: Authorization: Bearer $RAG_API_KEY — admin scope required

API

curl -s "$ZEABUR_RAG_URL/api/admin/triage?days=14&similarity_threshold=0.4&limit=20" \
  -H "Authorization: Bearer $RAG_API_KEY"

Param	Default	Description
`days`	`14`	Time window for signals (reports and learned have no time window)
`similarity_threshold`	`0.4`	Queries with `top_similarity` below this are flagged. Note: in hybrid mode (RRF), scores are ~0–0.02 so the default 0.4 catches all hybrid queries. Adjust to ~0.005 for meaningful hybrid-mode filtering.
`limit`	`20`	Max items per category

Response

Returns JSON with 5 arrays + counts (true totals, not truncated) + has_more (boolean per category):

open_reports — reports with status='open', no time window. Fields: id, type, chunk_id, query, detail, created_at.
unverified_learned — learned chunks with status='unverified', no time window. Fields: id, title, tags, created_at, age_days.
low_similarity_signals — queries where the top score fell below the threshold, within the time window, deduped by query text. The score is RRF in hybrid mode (~0–0.02) or cosine in semantic mode (0–1). Fields: id, query, top_similarity, top_chunk_ids, client, created_at.
negative_feedback_signals — queries that got negative user feedback, within the time window. Fields: id, query, feedback_score, feedback_comment, top_chunk_ids, created_at.
needs_frontmatter — chunks whose tags include "needs-frontmatter", meaning they were ingested from a source file that lacks proper frontmatter and got a temporary ID. Fields: id, title, tags, url, source, created_at.

If a category query fails, its array is empty and a <category>_error field appears with the error message.

After triage

To drill into a specific chunk: use zeabur-rag-inspect with the chunk ID.
To start the full curation loop: use zeabur-rag-curate.
Do not mutate anything directly from triage results. Always inspect first, then decide on action.

When all categories are empty

If every count is 0, the knowledge base has no pending maintenance. Report: "The knowledge base has no pending items — looks healthy ✅"