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 skills/rmarquesa/agentmemory-offline/remembernpx skills add rmarquesa/agentmemory-offline --skill remembergit clone --depth 1 https://github.com/rmarquesa/agentmemory-offlineWhat 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 | $0.00052 | $0.00502 |
| Opus 5 | $0.00026 | $0.00251 |
| Sonnet 5 | $0.00010 | $0.00100 |
| Haiku 4.5 | $0.00005 | $0.00050 |
Grade A, and why
remember 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.
This is a copy
95% identical to remember — 7 lines 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.
What it actually says
The user wants to save this to long-term memory: $ARGUMENTS
Quick start
memory_save {
"content": "We rotate JWT refresh tokens on every use; the old token is revoked server-side in auth/refresh.ts.",
"concepts": "jwt-refresh-rotation, token-revocation, auth-flow",
"files": "src/auth/refresh.ts"
}
Expected output:
Saved memory abc12345 with 3 concepts: jwt-refresh-rotation, token-revocation, auth-flow.
Why
A memory is only as useful as the terms that retrieve it. Tag with specific
concepts so a future recall finds it, and preserve the user's own phrasing.
Workflow
- Pull the core insight, decision, or fact out of
$ARGUMENTS. - Extract 2-5 lowercased concept phrases. Prefer specific over generic
(
jwt-refresh-rotationbeatsauth). - Extract referenced file paths (absolute or repo-relative). Empty if none.
- Call
memory_savewithcontent,concepts(comma-separated string), andfiles(comma-separated string). - Confirm the save and echo the concepts so the user knows the retrieval terms.
Anti-patterns
WRONG: concepts: "stuff, code, notes" (generic tags nothing can find later).
RIGHT: concepts: "jwt-refresh-rotation, token-revocation" (specific, retrievable).
Checklist
- Content preserves the user's phrasing, not a paraphrase.
- Concepts are specific, lowercased, 2-5 items.
- File paths are real references, not guesses.
- Confirmation echoes the exact concepts tagged.
See also
recall: retrieve what you save here (the pair to this skill).forget: remove a memory you saved by mistake.
Troubleshooting
See ../_shared/TROUBLESHOOTING.md if memory_save is not available.
What ships with it
1 file 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.
- yesterday First seen · 62 lines · 52 tokens per session scan A f68701ff5cdf
remember is a skill published in the GitHub repository rmarquesa/agentmemory-offline (0 stars, last pushed 2d ago), licensed Apache-2.0. It adds 52 tokens to every session and 502 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. It is 95% identical to remember, differing in 7 lines, and is treated as a copy.
Other skills, from other repositories
internal-rag
Mandatory persistent project memory for substantial coding tasks. Use at task start, recovery, milestones, failures, before risky operations, before compaction, and before finishing. v1.0.1 adds type filtering, type-priority scoring, query expansion, grouped context output, and promote workflow.
recall
Search agentmemory for past observations, sessions, and learnings about a topic using hybrid BM25 plus vector plus graph search. Use when the user says "recall", "what did we do about", "did we ever", "have we seen", or needs context from past sessions.
agentmemory-mcp-tools
Map of every agentmemory MCP tool, what each does, and its parameters. Use when choosing which memory tool to call, when a tool name or argument is unclear, or when answering what agentmemory can do via MCP.
lesson
Save a correction or hard-won rule as a confidence-weighted lesson that resurfaces before similar work. Use when the user corrects your approach, says "learn this", "always" or "never do X", or you notice yourself repeating a past mistake.
memory-discipline
The session loop that makes agentmemory pay off, recall before starting work, save at decision points, learn from corrections. Use when starting a nontrivial task, after settling a decision or debugging a gotcha, or whenever deciding if something belongs in memory.
commit-history
List recent git commits linked to agent sessions, optionally filtered by branch or repo. Use when the user asks "show agent commits", "what has the agent shipped", "list linked commits", or wants commits with their session context.