Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add kevin-hs-sohn/hipocampus --skill recallgit clone --depth 1 https://github.com/kevin-hs-sohn/hipocampusWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/kevin-hs-sohn/hipocampus/recall)<a href="https://agentmods.dev/skills/kevin-hs-sohn/hipocampus/recall"><img src="https://agentmods.dev/badge/skills/kevin-hs-sohn/hipocampus/recall/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/kevin-hs-sohn/hipocampus/recall"><img src="https://agentmods.dev/badge/skills/kevin-hs-sohn/hipocampus/recall.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00034 | $0.00729 |
| Opus 5 | $0.00017 | $0.00365 |
| Sonnet 5 | $0.00007 | $0.00146 |
| Haiku 4.5 | $0.00003 | $0.00073 |
Grade A, and why
hipocampus-recall scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 12d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 68 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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).
-
Build manifest from compaction node frontmatter (NOT full content):
- Read
memory/weekly/*.mdfrontmatter only (type, period, topics) - Read
memory/monthly/*.mdfrontmatter only (type, period, topics) - Read
knowledge/*.mdfirst 3 lines only - Skip
memory/daily/(already rolled up into weekly)
- Read
-
Self-evaluate: Given the manifest and the user's query, select up to 5 most relevant files.
-
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:
projecttype + >30 days old: append warning — "이 정보는 {N}일 전 기록입니다. 현재 상태를 확인하세요."referencetype +[?]marker: append warning — "이 참조는 검증되지 않았습니다. 접근 가능 여부를 확인하세요."user/feedbacktype: no age warning (these are durable).
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 12d ago First seen · 68 lines · 34 tokens per session scan A 1b6a7491dfb8
hipocampus-recall is a skill published in the GitHub repository kevin-hs-sohn/hipocampus (206 stars, last pushed 3mo ago), licensed MIT. It adds 34 tokens to every session and 729 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
mnemo-cortex
Installs and wires Mnemo Cortex (local-first persistent memory) into OpenClaw and other MCP-capable agents. Use for cross-session recall, decision history, or multi-agent shared memory.
capture-knowledge
A guide for saving useful, reusable knowledge from conversations and documents into a structured personal knowledge base. It separates original source material from concise pages about individual concepts.
second-brain-attach
A setup process for connecting a new or reset AI agent to a local Second Brain—a personal collection of rules, identity information, and reusable skills. It installs the required skills, connects the supporting service, and checks the setup in a fresh session.
second-brain-distill
A skill for extracting reusable knowledge from selected historical AI-agent conversations and archiving the results. It can process conversations only within a clearly defined, authorized batch.
second-brain-doctor
A read-only health check for an agent’s connection to a local Second Brain. It checks the authoritative files, installed skills, supporting service, access rules, version drift, and behavior in a new session.
second-brain-learn
A learning tool that reviews the current conversation and work evidence to find durable preferences, project rules, reusable methods, and lessons. It routes each finding to the appropriate long-term owner or leaves it unsaved when it has no lasting value.