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pska-digest
Execute PSKA digest jobs with claim-first knowledge extraction, grounded digest notes, and reviewable memory/action/relationship candidates.
用 Codex 或 Claude 帮你安装 复制这段 Prompt,粘贴到 Codex、Claude 或其他助手里,让它检查 Skill 页面并帮你完成安装。
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Execute PSKA digest jobs with claim-first knowledge extraction, grounded digest notes, and reviewable memory/action/relationship candidates.
用 Codex 或 Claude 帮你安装 复制这段 Prompt,粘贴到 Codex、Claude 或其他助手里,让它检查 Skill 页面并帮你完成安装。
基于 SOC 职业分类
Answer questions using PSKA MCP retrieval tools with citations, gaps, and no unsupported claims.
Execute PSKA extraction jobs by using PSKA MCP tools, preserving ACLs, source refs, review boundaries, and JSON-compatible candidate outputs.
Help review PSKA candidates without applying changes unless PSKA review tools and approvals permit it.
Guide for using GraphRAG knowledge graph tools to search and query entities and relationships. Use this SKILL for knowledge retrieval tasks like "search for X", "find entities about Y", "what is X". This SKILL is NOT for programming, implementation, or code writing tasks. GraphRAG知识图谱工具使用指南,用于知识检索和实体查询。适用于"搜索"、"查询"、"查找"等任务,不适用于编程实现任务。
Automated code review and quality analysis
Advanced file operations and navigation
| name | pska_digest |
| description | Execute PSKA digest jobs with claim-first knowledge extraction, grounded digest notes, and reviewable memory/action/relationship candidates. |
| tags | ["pska","digest","knowledge-claims","memory","summary","review"] |
| version | 1.1.0 |
| mcp_servers | ["pska"] |
| recommended_tools | ["pska_pska_job_context","pska_pska_write_candidates"] |
Use this skill when FastReAct is asked to run a PSKA digest pass.
Rules:
pska_pska_job_context at most once when a job_id is provided and context needs verification.knowledge_claims that state what the source says. Then write digest notes and other candidates only when useful.pska_pska_write_candidates when useful candidates exist.pska_pska_write_candidates at most once. If there are multiple candidate categories, merge all knowledge claims, digest notes, entities, memory candidates, review items, hyperedges, summaries, and action candidates into the same payload.schema_version: "pska.candidates.v1", job_id, source_refs, confidence, and producer: "fastreact".passage_window_id, then chunk_id, then document_id, then source_item_id.digest_notes, hyperedges, or relationship suggestions unless the same payload also includes at least one knowledge_claim.Budget invariant: after one pska_pska_write_candidates call in a batch, do
not call it again. Add any additional grounded candidate to the first payload,
or omit it and report the gap in the final summary.
Valid pska_pska_write_candidates payload shape:
{
"schema_version": "pska.candidates.v1",
"owner_user_id": "user_primary",
"job_id": "job_id_from_context",
"request_id": "stable_batch_request_id",
"source_refs": [{"source_item_id": "src_id", "document_id": "optional", "passage_window_id": "optional", "chunk_id": "optional"}],
"producer": "fastreact",
"knowledge_claims": [
{
"claim_type": "fact|definition|decision|constraint|action|risk|conflict|preference|relationship",
"statement": "中文优先的自然语言 claim,说明文档说了什么。",
"subject": "optional subject",
"predicate": "optional predicate",
"object": "optional object",
"qualifiers": {"scope": "optional"},
"evidence_text": "Minimal exact evidence snippet from the source.",
"confidence": 0.85,
"source_refs": [{"source_item_id": "src_id", "document_id": "doc_id", "passage_window_id": "pw_id"}]
}
],
"digest_notes": [
{
"title": "Readable digest title",
"synopsis": "Human-readable summary of what matters.",
"key_points": [{"summary": "Important point.", "source_refs": [{"source_item_id": "src_id"}]}],
"actions": [],
"open_questions": [],
"risks": [],
"memory_suggestions": [],
"relationship_suggestions": [],
"confidence": 0.8,
"source_refs": [{"source_item_id": "src_id", "document_id": "doc_id", "passage_window_id": "pw_id"}]
}
],
"entities": [
{"entity_type": "project|person|concept|source|service|event", "label": "Entity label", "confidence": 0.8}
],
"memory_candidates": [
{"kind": "agent_memory", "layer": "semantic", "text": "Grounded memory text.", "confidence": 0.8}
],
"review_items": [
{"review_type": "profile_update|conflict|quality", "title": "Review title", "proposal": {"note": "Grounded proposal"}}
],
"hyperedges": [
{
"relation_type": "depends_on|mentions|related_to",
"evidence_text": "Short source-grounded evidence.",
"confidence": 0.75,
"members": [
{"entity_type": "project", "label": "PSKA", "role": "subject"},
{"entity_type": "service", "label": "FastReAct", "role": "object"}
]
}
]
}
For simple single-fact digests, prefer one readable knowledge_claim and one
small digest_note. Only add memory_candidates, entities, or hyperedges
when the source clearly contains durable information, durable entities, or a
relationship worth review/retrieval. Do not use name, content, source, or
memory_id fields for agent memories; use text.
When no candidate should be written, return an empty JSON object with gaps explaining why. Do not invent candidates to satisfy the shape.