/recall
Memory recall guide. Structured retrieval from hipocampus memory — ROOT.md triage, manifest-based LLM selection, qmd search fallback.
$ npx -y skills add kevin-hs-sohn/hipocampus --skill recall --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
- Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
- You can call itInvoke it directly when you want it.
- Slash command
/recall
Context preview
The summary Claude sees to decide when to auto-load this skill.
Memory recall guide. Structured retrieval from hipocampus memory — ROOT.md triage, manifest-based LLM selection, qmd search fallback.
SKILL.md
recall.SKILL.mdname: hipocampus-recall
description: "Memory recall guide. Structured retrieval from hipocampus memory — ROOT.md triage, manifest-based LLM selection, qmd search fallback."
Memory Recall Protocol
Use this when the user's question may relate to past memory. Three-step fallback: ROOT.md O(1) lookup → manifest LLM selection → qmd search.
Step 1: ROOT.md Triage (O(1) — always try first)
Check ROOT.md Topics Index for the query topic.
- **Direct match found** → read the referenced file (knowledge/, daily log date, etc.). Done.
- **Partial match / related topic found** → read referenced file, check if it answers the query. Done if yes.
- **No match at all** → proceed to Step 2.
**Decision rule:** If Topics Index contains a keyword within 1 semantic hop of the query, it's a match. "배포" matches "deployment". "CI/CD" matches "github-actions".
Step 2: Manifest-Based LLM Selection (when ROOT.md is insufficient)
Use this ONLY when ROOT.md Topics Index has no relevant match but you suspect memory may exist (e.g., the user references something that sounds familiar, or the topic is cross-domain).
1. **Build manifest** from compaction node frontmatter (NOT full content):
- Read `memory/weekly/*.md` frontmatter only (type, period, topics)
- Read `memory/monthly/*.md` frontmatter only (type, period, topics)
- Read `knowledge/*.md` first 3 lines only
- Skip `memory/daily/` (already rolled up into weekly)
2. **Self-evaluate:** Given the manifest and the user's query, select up to 5 most relevant files.
3. **Load selected files** in full and extract the answer.
**Token budget:** Manifest should be <500 tokens. If too large, use monthly nodes only.
Step 3: qmd Search (fallback)
If Step 1-2 don't find the answer and qmd is installed:
qmd query "keyword1 keyword2" # hybrid (BM25 + vector)
qmd search "keyword1 keyword2" # BM25 only
qmd vsearch "semantic query" # vector only
Use 2-4 specific keywords. Try variations if first query misses.
Freshness Warnings
When recalling memory, check the source age:
- `project` type + >30 days old: append warning — "이 정보는 {N}일 전 기록입니다. 현재 상태를 확인하세요."
- `reference` type + `[?]` marker: append warning — "이 참조는 검증되지 않았습니다. 접근 가능 여부를 확인하세요."
- `user`/`feedback` type: no age warning (these are durable).
When NOT to Use Recall
- If the answer is clearly in ROOT.md Active Context → just use it directly
- If the question is about code/files → read the code, don't search memory
- If the question is about git history → use git log, not memory
Key Principles
- ROOT.md O(1) triage is the primary path — Step 2/3 are fallbacks
- Never load full content in Step 2 manifest phase — frontmatter only
- Manifest LLM selection leverages semantic understanding (solves keyword mismatch)
- Minimize token usage: most recalls should resolve at Step 1
Read more
name: hipocampus-recall description: "Memory recall guide. Structured retrieval from hipocampus memory — ROOT.md triage, manifest-based LLM selection, qmd search fallback."
Memory Recall Protocol
Use this when the user's question may relate to past memory. Three-step fallback: ROOT.md O(1) lookup → manifest LLM selection → qmd search.
Step 1: ROOT.md Triage (O(1) — always try first)
Check ROOT.md Topics Index for the query topic.
- **Direct match found** → read the referenced file (knowledge/, daily log date, etc.). Done.
- **Partial match / related topic found** → read referenced file, check if it answers the query. Done if yes.
- **No match at all** → proceed to Step 2.
**Decision rule:** If Topics Index contains a keyword within 1 semantic hop of the query, it's a match. "배포" matches "deployment". "CI/CD" matches "github-actions".
Step 2: Manifest-Based LLM Selection (when ROOT.md is insufficient)
Use this ONLY when ROOT.md Topics Index has no relevant match but you suspect memory may exist (e.g., the user references something that sounds familiar, or the topic is cross-domain).
1. **Build manifest** from compaction node frontmatter (NOT full content):
- Read `memory/weekly/*.md` frontmatter only (type, period, topics)
- Read `memory/monthly/*.md` frontmatter only (type, period, topics)
- Read `knowledge/*.md` first 3 lines only
- Skip `memory/daily/` (already rolled up into weekly)
2. **Self-evaluate:** Given the manifest and the user's query, select up to 5 most relevant files.
3. **Load selected files** in full and extract the answer.
**Token budget:** Manifest should be <500 tokens. If too large, use monthly nodes only.
Step 3: qmd Search (fallback)
If Step 1-2 don't find the answer and qmd is installed:
qmd query "keyword1 keyword2" # hybrid (BM25 + vector) qmd search "keyword1 keyword2" # BM25 only qmd vsearch "semantic query" # vector only
Use 2-4 specific keywords. Try variations if first query misses.
Freshness Warnings
When recalling memory, check the source age:
- `project` type + >30 days old: append warning — "이 정보는 {N}일 전 기록입니다. 현재 상태를 확인하세요."
- `reference` type + `[?]` marker: append warning — "이 참조는 검증되지 않았습니다. 접근 가능 여부를 확인하세요."
- `user`/`feedback` type: no age warning (these are durable).
When NOT to Use Recall
- If the answer is clearly in ROOT.md Active Context → just use it directly
- If the question is about code/files → read the code, don't search memory
- If the question is about git history → use git log, not memory
Key Principles
- ROOT.md O(1) triage is the primary path — Step 2/3 are fallbacks
- Never load full content in Step 2 manifest phase — frontmatter only
- Manifest LLM selection leverages semantic understanding (solves keyword mismatch)
- Minimize token usage: most recalls should resolve at Step 1
Drop-in proactive memory harness for AI agents. Zero infrastructure — just files. One command to set up. Works immediately with Claude Code, OpenCode, and OpenClaw.
Repo: kevin-hs-sohn/hipocampus
Other skills on hipocampus.
- /compaction
Build 5-level compaction tree (daily/weekly/monthly/root) with smart thresholds and fixed/tentative lifecycle. Run at session start when triggers are met, or via external scheduler.
Open skill - /core
3-tier agent memory system with 5-level compaction tree. Claude Code version. Defines session start protocol, end-of-task checkpoints, and memory file management. MUST be followed every session.
Open skill - /flush
Manual memory flush: dump current session context to daily raw log via subagent. Invoke with /hipocampus:flush. Run hipocampus:compaction afterwards for tree propagation and qmd reindex.
Open skill - /search
Search memory using qmd (BM25 + optional vector) and compaction tree traversal. Use ROOT.md to decide whether to search memory or look externally. Always check memory before external lookups.
Open skill

