From honeycomb
Searches prior context from Honeycomb memory before tasks, cites recalled decisions, and stores new facts, preferences, and gotchas with the correct type.
How this skill is triggered — by the user, by Claude, or both
Slash command
/honeycomb:honeycomb-memoryWhen to use
Before re-explaining a convention the user has already stated, before re-deciding something already decided, when the user asks "did we cover this before" or "what did we decide about X", or right after a decision/preference/gotcha is stated in the conversation.
The summary Claude sees in its skill listing — used to decide when to auto-load this skill
Honeycomb is a cross-harness memory system. It stores decisions, conventions, preferences, facts,
Honeycomb is a cross-harness memory system. It stores decisions, conventions, preferences, facts, gotchas, and references, and makes them recallable across sessions and across harnesses through a local daemon exposed here as MCP tools. This skill teaches three behaviors; each points at one of those existing tools. It never invents a new tool.
Before starting a task that plausibly has prior context, e.g. it touches a past decision, a stated
convention, or "where does X live", call hivemind_search (or memory_search) with a query
describing the task FIRST, rather than asking the user to re-explain something they may have
already told Honeycomb.
hivemind_search runs the hybrid recall (lexical + semantic, degraded-honest) over durable
memory and returns refs you can zoom into.memory_search is the direct memory-table search when you already know you want a memory, not a
broader recall.This is the token-cheap, model-driven complement to Honeycomb's always-on recall floor: it fires only when this skill decides it is relevant, not on every turn.
When a search surfaces a prior decision or convention, cite it in your work rather than silently
re-deciding it. If the hit is a summary and you need more detail, e.g. the exact wording, the
surrounding turns, use hivemind_read to zoom the ref down:
depth: 1 (the default) resolves to the Tier-2 summary.depth: 2 resolves to Tier-3 raw turns (bounded by the daemon's turn cap).Do not silently ignore a recalled decision that conflicts with what you are about to do. Surface the conflict to the user instead of overriding it unprompted.
After a decision, a stated preference, or a durable fact emerges in the conversation, e.g. the user
picks an approach, corrects your assumption, or states a fact about the system, call memory_store
so it is recallable next time. Classify it using the closed memory-type taxonomy the tool publishes
in its own schema (do not invent a type outside this set):
fact (default): a stable, verifiable truth about the system, codebase, or domain.convention: how things are done here, idioms and patterns to follow by default.preference: the user/team's stated way of working, corrections and do/don't guidance.decision: an architectural or design choice and its rationale, don't relitigate it.gotcha: a non-obvious trap, failure mode, or constraint to watch out for.reference: a pointer to an external resource (URL, dashboard, ticket, doc).Prefer the most specific type over fact when the content clearly fits convention,
preference, decision, gotcha, or reference instead.
If the Honeycomb MCP server is not registered in this session, hivemind_search, memory_search,
hivemind_read, and memory_store simply are not in the available tool list. This skill has no
effect in that case: do not attempt to work around a missing tool, and do not tell the user memory
is unavailable unless asked.
npx claudepluginhub legioncodeinc/honeycomb --plugin honeycombSearches memini memory service for prior context, decisions, or facts relevant to the current task. Use before file edits, architectural changes, or debugging recurring issues.
Searches and retrieves facts, decisions, and past context from SuperLocalMemory using multi-channel semantic retrieval with reranking. Use before storing new facts to avoid duplicates.
Searches and surfaces relevant memories from past sessions to inform current work with decisions, patterns, and learnings. Supports hybrid, vector, and text search modes with namespace filtering.