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/ccf/agentcairn/using-agentcairn-memorynpx skills add ccf/agentcairn --skill using-agentcairn-memorygit clone --depth 1 https://github.com/ccf/agentcairnWhat 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 | $0.00046 | $0.00507 |
| Opus 5 | $0.00023 | $0.00253 |
| Sonnet 5 | $0.00009 | $0.00101 |
| Haiku 4.5 | $0.00005 | $0.00051 |
Grade A, and why
using-agentcairn-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 2d 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.
What it actually says
Using agentcairn memory
You have a persistent memory backed by agentcairn (a Markdown vault the user owns). Use it.
Require the full integration
This skill assumes AgentCairn's MCP tools are already loaded. Before following
the memory workflow, confirm that recall and remember are available.
If the tools are missing:
- Do not invent tool calls or imply that this behavior skill installed the AgentCairn runtime, MCP server, plugin, or hooks.
- Explain that the full AgentCairn integration is not loaded.
- Invoke
$agentcairn-setupif available, or direct the user to the native AgentCairn installation instructions. - Stop the memory workflow until installation is complete and the host has been restarted.
Recall before you work
Before designing, debugging, or re-deriving something non-trivial, search memory first:
- Use the
recalltool (hybrid search) with a focused query — "how did we fix the auth token refresh?", "what did we decide about the migration order?". - Expand a promising hit with
build_contextto read the full note. - Recall is cross-project: prior solutions in any repo can help. Cite notes by permalink.
- Recall automatically prefers your current project's memories while still surfacing relevant cross-project ones (marked
[from: <project>]); pass a project to target another repo, or--scope projectto limit a query to just this one.
Remember durable facts
After a decision, a non-obvious fix, a gotcha, or a stated user preference, persist it with the
remember tool — a short, self-contained fact. Good memories: "We rotate jwt-secret on deploy via
X.", "User prefers rebase-merges.", "DuckDB TIMESTAMP stores naive-UTC — bind accordingly."
Skip the trivial — the SessionEnd hook already captures the session in bulk; remember is for the
high-value things worth pinning deliberately.
The vault is plain Markdown the user can read and edit; treat it as shared, durable 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.
- 2d ago First seen · 42 lines · 46 tokens per session scan A 2e0daecacbd6
using-agentcairn-memory is a skill published in the GitHub repository ccf/agentcairn (48 stars, last pushed 5d ago), licensed Apache-2.0. It adds 46 tokens to every session and 507 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
forget
Delete specific observations from agentmemory after showing them and getting explicit confirmation. Use when the user says "forget this", "delete memory", "remove that note", or wants to scrub specific data for privacy.
handoff
Resume the most recent agent session for the current working directory, leading with any unanswered question. Use when the user says "where were we", "resume", "handoff", "pick up where I left off", or starts a session with no fresh context.
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.
remember
Save an insight, decision, or learning to agentmemory's long-term storage with searchable concept tags. Use when the user says "remember this", "save this", "note that", "don't forget", or wants to preserve knowledge for future sessions.
agentmemory-hooks
The agentmemory plugin hooks that capture observations automatically across the agent session lifecycle. Use when explaining how memory gets captured without manual saves, when debugging missing observations, or when tuning what gets recorded.