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 agentmods add agents/marlburrow/hivekeep/memorygit clone --depth 1 https://github.com/MarlBurroW/hivekeepWhat 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 | $0.00008 | $0.01140 |
| Opus 5 | $0.00004 | $0.00570 |
| Sonnet 5 | $0.00002 | $0.00228 |
| Haiku 4.5 | $0.00001 | $0.00114 |
Grade A, and why
memory 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 yesterday.
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 — 88 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Hivekeep gives every Agent persistent long-term memory, in two layers:
| Layer | What it holds | How the Agent sees it |
|---|---|---|
| Profile | What the Agent knows: current state, standing preferences, active work | Always present in its context |
| Archive | What happened: dated events, past details, one-off facts | Searched on demand with recall |
The split is what keeps memory both reliable and cheap. The profile is small and always there, so the Agent never has to get lucky with a search to know who you are and what you are working on. The archive is unbounded and costs nothing until queried, so it can keep everything else.
The profile
A short markdown document (default budget: 1500 tokens) injected into every prompt. It has conventional sections: Pinned, Active projects, Preferences & conventions, Key decisions, Open threads.
It is maintained three ways:
- Automatically, during compaction: the maintenance pass rewrites it, folding in what is new and dropping what is resolved.
- By the Agent, with
edit_profile, when you tell it something durable and it should not wait for the next compaction. - By you, in the Agent's Memory tab: edit the markdown directly, watch the token count, or regenerate the whole document from the archive.
Pinned entries
Anything under ## Pinned is copied verbatim by every automatic rewrite and never edited or dropped. Use it for instructions you want followed forever ("always write GitHub issues in English"). Both edit_profile(..., pin: true) and the editor can put entries there.
The archive
Individual memories, saved automatically during compaction or explicitly with memorize. Each carries a category, an optional subject, an importance score, and a source context describing where it came from (e.g. "While discussing weekend plans, user mentioned...").
| Category | Use case |
|---|---|
fact |
Objective information (names, dates, technical details) |
preference |
User preferences and habits |
decision |
Decisions that were made and their rationale |
knowledge |
Learned domain knowledge |
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.
- yesterday First seen · 88 lines · 8 tokens per session scan A 6336de5715cd
memory is an agent published in the GitHub repository MarlBurroW/hivekeep (51 stars, last pushed 2d ago), licensed MIT. It adds 8 tokens to every session and 1,140 once invoked, about $0.0000 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 agents, from other repositories
planner
복잡한 기능·아키텍처 변경·멀티스텝 리팩토링 구현 계획 전문. 요구사항 인터뷰 → 코드베이스 조사 → 3-6단계 plan.md 생성 + 인수 기준 포함. NEVER 구현. Use proactively when "구현 계획", "설계해줘", "어떻게 만들지", "spec 작성"처럼 코드 작성 전 계획이 필요한 시점. 발산 아이디어가 필요하면 dev-brainstormer 먼저, 아키텍처 판단은 architect 사용.
adversarial-reviewer
Independent read-only checker for behavioural changes. Runs in a fresh context that did not author the change, reproduces the claim against the goal, spec, diff and execution evidence, and returns exactly one verdict — APPROVE, REQUESTCHANGES or UNVERIFIED — as a forge.review/v1 envelope. MUST BE USED before claiming…
refactor-cleaner
데드 코드·미사용 exports·의존성 제거, 중복 통합 전문. knip/depcheck/ts-prune 감지 → Grep 참조 검증 → 안전 제거. 피처 브랜치에서만 동작. Use proactively when "데드 코드", "미사용 코드", "정리해줘", "클린업", "리팩토링" 요청 시. 빌드 에러 수정은 build-error-resolver, 새 기능은 tdd-guide 사용.
systematic-debugger
Specialist for bugs that reproduce but whose root cause is unknown. Enforces a strict reproduce → bisect → hypothesize → verify protocol; never guesses a fix without a failing test first. Use proactively when a bug reproduces but the cause is unclear — "why does this happen", "works locally but not in CI"…
defining-agents
Agent "defining-agents" from sixb-ai/sixb, covering defining agents, config, the model, instructions vs agent skills and tools.
overview
An agent is a conversational assistant you define alongside your ontology. It calls a language model, selected worker tools, sandboxed read and bash, and an authorized Sixb API.