agentmemory is a persistent-memory system that lets AI coding agents retain and search information across sessions. It connects with several coding agents and MCP clients, storing knowledge with confidence scoring, lifecycle handling, knowledge graphs, and hybrid search. The catalogue contains agentmemory integrations such as skills, hooks, commands, a plugin, and an MCP component.
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 rohitg00/agentmemory --skill handoffgit clone --depth 1 https://github.com/rohitg00/agentmemoryWrote 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/rohitg00/agentmemory/handoff)<a href="https://agentmods.dev/skills/rohitg00/agentmemory/handoff"><img src="https://agentmods.dev/badge/skills/rohitg00/agentmemory/handoff/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/rohitg00/agentmemory/handoff"><img src="https://agentmods.dev/badge/skills/rohitg00/agentmemory/handoff.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk pass
- 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 | $0.00055 | $0.00737 |
| Opus 5.5 | $0.00022 | $0.00295 |
| Sonnet 5.5 | $0.00011 | $0.00147 |
| Haiku 4.5 | $0.00006 | $0.00074 |
Grade A, and why
handoff 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 7d 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 — 76 lines — stays where its author put it; the contents beside it link to each section on GitHub.
The user wants to resume work. Optional cwd override: $ARGUMENTS
Quick start
memory_sessions { "project": "<current-project-id>", "limit": 20 }
Pick the most recent session whose cwd matches this project, then:
memory_recall { "query": "<session top concepts>", "project": "<current-project-id>", "limit": 10 }.
When an agent is requested, pass the same agentId to both calls.
Expected output:
Resuming 7f3a9c2 "Auth refresh rework".
Open question: should logout revoke all device tokens or just the current one?
Next step: decide revoke scope, then update auth/logout.ts.
Why
Match the session by directory boundary, not raw prefix, so a sibling repo never gets mistaken for this one. Never invent observations for an empty session.
Workflow
- Resolve the project path: if
$ARGUMENTSis given, normalize it to absolute (path.resolve(process.cwd(), $ARGUMENTS)); else use the cwd. - Call
memory_sessionswith the exact project identifier when known, andagentIdwhen requested. Otherwise follownextCursoruntil the matching directory is found or pagination ends. Preserve filters between pages. Pick the most recent session whose normalizedcwdmatches by directory boundary: equality, ORcwd.startsWith(projectPath + sep), ORprojectPath.startsWith(cwd + sep). Prefercompletedoverabandoned. No match: report that this project has no matching session and stop. Do not substitute a different project's session. - If the session ended on an unanswered user-facing question, surface it FIRST.
Look in
summaryor recentconversationobservations whosenarrativeends in?. - Summarize: title/summary, key files, key decisions or errors, using
memory_recallon the top concepts, limit 10, preserving the chosen project and agent scope. Check each result's session or project provenance before including it; a query alone does not enforce scope. - End with one concrete "next step?" pointer.
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 7d ago Changed · +5 lines e4ec7862811a
- 17d ago Changed · +2 lines bf59c5bb44e8
- 1mo ago First seen · 69 lines · 55 tokens per session scan A 1a5695744091
handoff is a skill published in the GitHub repository rohitg00/agentmemory (29,718 stars, last pushed today), licensed Apache-2.0. It adds 55 tokens to every session and 737 once invoked, about $0.0002 per session on Opus 5.5. 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
hindsight-local
Store user preferences, learnings from tasks, and procedure outcomes. Use to remember what works and recall context before new tasks. (user).
memory-retain
Save what was learned to Hindsight long-term memory. Use when a task finishes, when the user states a fact, decision or preference, and when you discover something a later run or another Bot would need.
last30Days
Resolve "last30Days" to a concrete ISO date range relative to your run time — a rolling 30-day window ending today. Returns inclusive civil dates plus exact UTC instants so you have temporal context without computing dates by hand. Read-only: no writes, no network. Use before a "last 30 days" / trailing-month task…
thisWeek
Resolve "thisWeek" to a concrete ISO date range relative to your run time — this week so far (Monday → today). Returns inclusive civil dates plus exact UTC instants so you have temporal context without computing dates by hand. Read-only: no writes, no network. Use before a week-to-date task (this week's activity…
kb-research
Structured approach to finding and synthesizing information from the user's knowledge base.
sl_capture
How to capture new reusable patterns into ktx's semantic layer - when a measure, segment, or join belongs in the catalog and how to write it generically so it stays small and useful over time. Loaded by the post-turn memory-agent only. The research agent does not write to the SL.