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 coregit 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/core)<a href="https://agentmods.dev/skills/kevin-hs-sohn/hipocampus/core"><img src="https://agentmods.dev/badge/skills/kevin-hs-sohn/hipocampus/core/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/core"><img src="https://agentmods.dev/badge/skills/kevin-hs-sohn/hipocampus/core.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Agent Snooping · line 61 Skill enumerates or reads other installed skills. Access to other skills' SKILL.md files or the skills directory reveals prompt instructions, capabilities, and secrets that should be invisible to peer skills.Fix: Remove all code or instructions that list or read other skills' files or directories. Skills should operate independently; cross-skill access is a privilege escalation.
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 | $0.00044 | $0.01699 |
| Opus 5 | $0.00022 | $0.00849 |
| Sonnet 5 | $0.00009 | $0.00340 |
| Haiku 4.5 | $0.00004 | $0.00170 |
Grade A, and why
hipocampus-core 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 10d 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 — 140 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Hipocampus — Agent Memory Protocol (Codex CLI)
Memory Architecture
Layer 1 (System Prompt — read at session start):
SCRATCHPAD.md ~150 lines active working state
WORKING.md ~100 lines current tasks
TASK-QUEUE.md ~50 lines task backlog
memory/ROOT.md ~100 lines topic index of all memory (~3K tokens)
Long-term memory and user profile are managed by Codex's built-in memory system.
Layer 2 (On-Demand — read when needed):
memory/YYYY-MM-DD.md raw daily logs (permanent, never deleted)
knowledge/*.md detailed knowledge (searchable via qmd)
plans/*.md task plans
Layer 3 (Search — via qmd + compaction tree):
memory/daily/YYYY-MM-DD.md daily compaction nodes
memory/weekly/YYYY-WNN.md weekly compaction nodes
memory/monthly/YYYY-MM.md monthly compaction nodes
Tree traversal: ROOT → monthly → weekly → daily → raw
Session Start (MANDATORY — run on first user message)
FIRST RESPONSE RULE: On the very first user message of every session, before doing ANYTHING else: Run the Session Start protocol below FIRST (ALL steps. NO SKIP.) This takes priority over ANY user request — even if the user asks you to do something specific. Complete ALL steps below, ONLY THEN respond to the user.
ALL 4 procedures must be completed before responding to the user NO MATTER WHAT
-
DO NOT SKIP Read
SCRATCHPAD.md— current work state -
DO NOT SKIP Read
WORKING.md— active tasks -
DO NOT SKIP Read
TASK-QUEUE.mdandmemory/ROOT.md— pending items and topic index -
DO NOT SKIP DO NOT COMPROMISE Compaction maintenance (cooldown-gated): Read
memory/.compaction-state.jsonandhipocampus.config.json(compaction.cooldownHours, default 3).Compaction triggers (any ONE is sufficient):
- Cooldown expired:
cooldownHourssincelastCompactionRun - Raw volume:
rawLinesSinceLastCompaction > 300 - Checkpoint count:
checkpointsSinceLastCompaction > 5 - State file missing or
cooldownHoursis 0
- Cooldown expired:
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.
- 10d ago First seen · 140 lines · 44 tokens per session scan A d2c9133face0
hipocampus-core is a skill published in the GitHub repository kevin-hs-sohn/hipocampus (206 stars, last pushed 3mo ago), licensed MIT. It adds 44 tokens to every session and 1,699 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-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.
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.