forgetful-explore
GitHub用于在平面搜索不足时探索知识图谱，支持跨项目调查、实体中心查询及决策关联追踪。通过语义入口、扩展记忆、发现实体、遍历关系及收集实体关联记忆五个阶段，将碎片化信息综合为带有路径引用的结构化全景视图。
Trigger Scenarios
Install
npx skills add ScottRBK/forgetful --skill forgetful-explore -g -y
SKILL.md
Frontmatter
{
"name": "forgetful-explore",
"tags": [
"knowledge-graph",
"traversal",
"entities",
"investigation"
],
"license": "MIT",
"description": "Explore the Forgetful knowledge graph when flat search isn't enough — cross-project investigations, \"what do we know about X\", entity-centred questions, tracing how decisions connect. Use when recall returns fragments that reference entities or trail across domains. Walks memories, entities, and relationships into one synthesized picture.",
"allowed-tools": [
"mcp__forgetful__discover_forgetful_tools",
"mcp__forgetful__how_to_use_forgetful_tool",
"mcp__forgetful__execute_forgetful_tool",
"Bash(forgetful:*)"
]
}
Exploring the knowledge graph
A flat query answers "what matches"; exploration answers "how it connects". The output is a synthesized picture that states the path taken — never a raw dump of hops.
Invoking operations
Operations are named by registry name (search_entities, get_entity_memories, ...).
Invoke via whichever surface this agent has:
- MCP:
execute_forgetful_tool(tool_name="search_entities", arguments={...}) - CLI:
forgetful call search_entities --args '{"query": "..."}' --json
Get any operation's schema at runtime: how_to_use_forgetful_tool (MCP) or
forgetful tools info <operation> (CLI) — schemas are deliberately not repeated here.
Choose a depth first
- Shallow (phases 1–2): confirm or refresh something specific.
- Medium (phases 1–4): understand a topic and its immediate neighbourhood.
- Deep (all phases): cross-project investigation or "everything we know about X".
Track visited memory and entity IDs throughout — a revisited ID means that branch is done.
Phase 1 — Semantic entry
Broad query_memory on the topic (with query_context stating the investigation's intent).
The goal is entry points, so favour breadth over precision here — raise k above its
default of 3 (max 20) rather than settle for a narrow first pass.
Done when: a handful of strong entry memories are identified.
Phase 2 — Expand the strongest hits
get_memory on each entry point for full content and linked memory IDs; follow the links
that bear on the question.
Done when: each entry point's local cluster is understood.
Phase 3 — Discover the entities
Collect entities referenced by the expanded memories, and search_entities for the topic's
obvious actors (systems, people, components) that memories may not name directly.
Done when: the cast of relevant entities is listed.
Phase 4 — Walk the relationships
get_entity_relationships on each relevant entity; follow the relationship types that
answer the question (depends_on for impact, part_of for structure, owns for responsibility).
Done when: the connections between entities are mapped, cycles skipped via the visited set.
Phase 5 — Harvest entity-linked memories
get_entity_memories on the entities that emerged as central — this surfaces knowledge
attached to the thing that topic-based queries miss. It returns linked memory IDs paired
with titles, not full content: scan the titles for relevance, then get_memory per chosen
ID for the text itself.
Done when: central entities have had their attached knowledge collected.
Synthesize
Report the picture, not the walk: what is known, how it connects, where knowledge is thin. Cite memory and entity IDs so the user can jump in, and state the traversal path in one line (e.g. "query → 3 memories → AuthService entity → 2 dependent components → 5 linked memories").
Done when: the user gets a connected answer with cited IDs and visible gaps.
Version History
- 633cc1f Current 2026-07-25 08:21
