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 commands/ololand-ai/ololand-plugins/recallgit clone --depth 1 https://github.com/ololand-ai/ololand-pluginsWrote 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/commands/ololand-ai/ololand-plugins/recall)<a href="https://agentmods.dev/commands/ololand-ai/ololand-plugins/recall"><img src="https://agentmods.dev/badge/commands/ololand-ai/ololand-plugins/recall.svg" alt="Measured on agentmods" 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 | $0.00048 | $0.00579 |
| Opus 5 | $0.00024 | $0.00290 |
| Sonnet 5 | $0.00010 | $0.00116 |
| Haiku 4.5 | $0.00005 | $0.00058 |
Grade A, and why
recall 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 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.
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 — 56 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/recall
You are reading the persistent fact store for an OloLand deal. Every fact returned was confirmed by an analyst at some point and survives across conversation sessions.
Required inputs
- deal_id — the OloLand deal whose facts to recall.
Optional:
- keys — list of specific keys to filter on (e.g.
["ebitda_fy24_confirmed", "sponsor_name"]). Omit to recall every fact stored for the deal.
Action
Call mcp__ololand__recall_deal_facts with the deal_id and (optionally) keys. Render the response in this format:
Confirmed facts for <deal_id>:
<key>: <value>
Provenance: <note if any>
Last updated: <updated_at>
If the result is empty:
No confirmed facts for <deal_id>. Use /remember to start building the deal's persistent record.
When to use
- At the start of any new session about a deal. Run
/recallbefore doing anything else so you have the analyst-confirmed context loaded before the conversation drifts. - Before composing an IC memo or CIM section. Cross-reference what the corpus says with what analysts have explicitly confirmed; conflicts are signal.
- When a previous answer feels wrong. A recalled fact may explain why — e.g. the model is quoting unaudited management figures when the analyst confirmed an adjusted number lives at key
ebitda_fy24_confirmed.
When NOT to use
- For information that's pulled from the deal corpus on demand (use
mcp__ololand__search_deal_documentsormcp__ololand__search_extracted_knowledgefor that). - For deal-level metadata (use
mcp__ololand__get_dealinstead). - As an audit trail for AI claim corrections (use the corrections endpoint —
mcp__ololand__submit_agent_claim_correction).
Pairing rule
/recall and /remember are companions. The institutional-memory pattern:
- Open a session — start with
/recall <deal_id>. - Do work; surface a fact that the corpus cannot give you.
- Confirm with the user.
/rememberit.- Future sessions start the loop again with
/recall.
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 · 56 lines · 48 tokens per session scan A 365f0b2e5dd4
recall is a command published in the GitHub repository ololand-ai/ololand-plugins (0 stars, last pushed 7d ago), licensed Apache-2.0. It adds 48 tokens to every session and 579 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 2026-08-31.
Other commands, from other repositories
minutes-ideas
Surface recent voice memos and ideas captured from any device. Use when the user asks "what ideas did I have?", "what were my recent memos?", "what did I record while walking?", or wants to recall a captured thought.
learn
Force claude-smart to extract learnings from this session now.
session-end
I'll summarize this coding session and update the memory system with our accomplishments.
memory-store
Store an insight, decision, or pattern to memory.
cc-memory
Configure persistent memory that survives across sessions using a layered approach: split rule files for always-loaded context, auto-memory for organic learning, and optional MCP-backed long-term memory for large codebases.
analyze-context
USE WHEN you want to analyze project context before starting work on a task. Calls context + recall, then synthesizes goals, decisions, gotchas, and relevant memories into a pre-task brief.