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.
git 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/remember)<a href="https://agentmods.dev/commands/ololand-ai/ololand-plugins/remember"><img src="https://agentmods.dev/badge/commands/ololand-ai/ololand-plugins/remember/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/commands/ololand-ai/ololand-plugins/remember"><img src="https://agentmods.dev/badge/commands/ololand-ai/ololand-plugins/remember.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 | $0.00054 | $0.00706 |
| Opus 5.5 | $0.00022 | $0.00282 |
| Sonnet 5.5 | $0.00011 | $0.00141 |
| Haiku 4.5 | $0.00005 | $0.00071 |
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 1mo 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 — 57 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/remember
You are persisting a confirmed (key, value) fact about an OloLand deal into deal_session_memory. This is the externalized-working-memory primitive — facts written here survive across conversation sessions about the same deal.
Required inputs
Ask the user for these if any are missing:
- deal_id — the OloLand deal this fact belongs to.
- key — a short, stable kebab-case identifier (≤ 200 chars). Examples:
ebitda_fy24_confirmedsponsor_nameic_datewc_normalization_conventiondd_milestone_status
- value — the fact itself. Any JSON value: a number, string, list, or small object.
Optional:
- note — short provenance note (≤ 1000 chars). Where the fact came from, what confirmed it, who confirmed it. Strongly recommended for audit trail.
Action
Call mcp__ololand__remember_deal_fact with the collected fields. The tool is idempotent on key — re-remembering the same key replaces the value and updates the note.
After success, confirm to the user with the returned status (created or updated) and quote back the key and value. If the user supplied a note, quote that too so they see the audit trail being written.
When to use
- A user confirms a number that the AI extracted ("yes, $34.2M FY24 EBITDA is right").
- A partner gives a fact in a meeting that should persist ("Jim from MidOcean is leading").
- A modeling convention is agreed ("we're normalizing WC against the 3-year average, not the trailing 12").
- A milestone advances ("LOI signed 2026-05-20").
- A deal-specific assumption is locked ("4.0% terminal growth — discussed with sponsor").
When NOT to use
- For working-state of an in-progress task (use TodoWrite-style state inside the conversation instead).
- For risks, materialized risks, or claim corrections — those have dedicated capture surfaces (
record_materialized_risks,submit_risk_correction,submit_agent_claim_correction). - For deal outcomes — use
record_deal_outcome. - For information that is derivable from the deal corpus by running a tool — recompute don't memorize. Reserve
/rememberfor analyst confirmations the corpus can't yield.
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.
- 1mo ago First seen · 57 lines · 54 tokens per session scan A 393887a75fcd
remember is a command published in the GitHub repository ololand-ai/ololand-plugins (0 stars, last pushed 3d ago), licensed Apache-2.0. It adds 54 tokens to every session and 706 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 2026-08-31.
Other commands, from other repositories
daily-review
Run a daily reflection on recent trades and behavioral patterns.
evolve
Run the Evolution Engine to discover and validate trading strategies.
performance
Generate a strategy performance report with key metrics.
recall
Recall similar past trades using outcome-weighted memory.
record-trade
Record a completed trade into all memory layers with full context.
model-revenue
Produce a Revenue Model — current revenue map by stream, target revenue map, concentration risk flags, margin analysis, unit economics, growth-curve shape per stream, compounding-vs-linear assessment. Composes with Genius Profile Freedom Path buckets. Not financial advice.