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 skillmds/skillmd --skill agent-recallgit clone --depth 1 https://github.com/skillmds/skillmdWrote 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/skillmds/skillmd/agent-recall)<a href="https://agentmods.dev/skills/skillmds/skillmd/agent-recall"><img src="https://agentmods.dev/badge/skills/skillmds/skillmd/agent-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/skillmds/skillmd/agent-recall"><img src="https://agentmods.dev/badge/skills/skillmds/skillmd/agent-recall.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00157 | $0.05107 |
| Opus 5.5 | $0.00063 | $0.02043 |
| Sonnet 5 | $0.00031 | $0.01021 |
| Haiku 4.5 | $0.00016 | $0.00511 |
Grade B, and why
agent-recall scanned grade B with 1 finding 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 4d 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.
Unrestricted tool accessmediumExcessive agency
A wildcard tool grant or "run any command" leaves no least-privilege boundary at all.
- **No code execution:** The MCP server does not execute arbitrary code, run shell commands, or spawn child processes. This is a copy
100% identical to agent-recall — 1 line differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 522 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AgentRecall v3.4.30 — Usage Guide
AgentRecall is a persistent memory system. Default surface: 5 tools (two verbs + three essentials). Full surface: 18 tools via npx agent-recall-mcp --full. This guide describes how and when to use them.
Two-verb model: session_start (inhale — load context) and session_end (exhale — save and compound). Everything else is available but secondary; most agents never need more than the default 5. See Automaticity Law below.
Setup
AgentRecall requires the MCP server to be running. If tool calls fail with "unknown tool", the human needs to install it first.
Visual setup guide (all 13 clients, copy-paste prompts): open
warroom/install.htmlfrom the repo, or the GitHub raw link in a browser.
Installation (human runs once)
Claude Code:
claude mcp add --scope user agent-recall -- npx -y agent-recall-mcp
Cursor (.cursor/mcp.json):
{ "mcpServers": { "agent-recall": { "command": "npx", "args": ["-y", "agent-recall-mcp"] } } }
VS Code / GitHub Copilot (.vscode/mcp.json):
{ "servers": { "agent-recall": { "command": "npx", "args": ["-y", "agent-recall-mcp"] } } }
Windsurf (~/.codeium/windsurf/mcp_config.json):
{ "mcpServers": { "agent-recall": { "command": "npx", "args": ["-y", "agent-recall-mcp"] } } }
Codex:
codex mcp add agent-recall -- npx -y agent-recall-mcp
Hermes Agent (~/.hermes/config.yaml):
mcp_servers:
agent-recall:
command: npx
args: ["-y", "agent-recall-mcp"]
Roo Code (.roo/mcp.json):
{ "mcpServers": { "agent-recall": { "command": "npx", "args": ["-y", "agent-recall-mcp"] } } }
Any MCP-compatible agent:
command: npx
args: ["-y", "agent-recall-mcp"]
transport: stdio
Tools
AgentRecall's default surface provides 5 tools. Start the server with --full to enable the complete 18-tool surface.
What ships with it
21 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.
- .gitignore 536 B
- .nvmrc 3 B
- AGENTS.md 8.3 KB
- CHANGELOG.md 28 KB
- CONTRIBUTING.md 3.5 KB
- CONTRIBUTORS-WANTED.md 6.1 KB
- Dockerfile 385 B
- glama.json 101 B
- LICENSE 1.1 KB
- migration.sql 3.3 KB
- ORCHESTRATOR-PROTOCOL.md 19 KB
- package-lock.json 91 KB
- package.json 879 B
- README.full.md 30 KB
- README.md 17 KB
- README.zh-CN.md 18 KB
- REPORT-2026-05-30.html 31 KB
- smithery.yaml 1.1 KB
- tsconfig.base.json 363 B
- UPDATE-LOG.md 131 KB
- UPGRADE-v3.4.md 1.9 KB
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.
- 4d ago First seen · 522 lines · 157 tokens per session scan B 967b842b342f
agent-recall is a skill published in the GitHub repository skillmds/skillmd (1 stars, last pushed yesterday), licensed MIT. It adds 157 tokens to every session and 5,107 once invoked, about $0.0006 per session on Opus 5.5. A static security scan graded it B with 1 finding (unrestricted tool access). It is 100% identical to agent-recall, differing in 1 line, and is treated as a copy.
Other skills, from other repositories
agent-recall
Persistent compounding memory for AI agents. 5 default MCP tools: sessionstart, sessionend, remember, recall, check. Full surface (18 tools) available with --full flag. Two-verb model: inhale (sessionstart) and exhale (sessionend). Correction-first memory with decision trail tracking, watchfor warnings, palace rooms…
shodh-memory
Persistent memory system for AI agents. Use this skill to remember context across conversations, recall relevant information, and build long-term knowledge. Activate when you need to store decisions, learnings, errors, or context that should persist beyond the current session.
swarmvault
Use SwarmVault when the user needs a local-first knowledge vault that writes durable markdown, graph, search, dashboard, review, chat-session, context-pack, task-ledger, static AI export, retrieval, and MCP artifacts to disk from books, notes, transcripts, exports, datasets, slide decks, files, URLs, code, and…
linker
Discovers missing cross-domain connections between knowledge documents and creates/updates connection wiki articles.
tashanzhishi
A workflow for turning YouTube, Bilibili, podcast, or article content into a readable Chinese note stored under a knowledge folder called 他山之石. It separates the source author’s facts and opinions from the note writer’s interpretation.
extract-memory
Extract writing style, personality traits, and key insights from a collection of documents (Markdown, PDF, TXT, DOCX) and save them as a Claude Code memory file. Use when the user wants to capture their writing DNA, build a personal style guide, or create a reusable memory profile from their existing content.