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/playbook-recall)<a href="https://agentmods.dev/commands/ololand-ai/ololand-plugins/playbook-recall"><img src="https://agentmods.dev/badge/commands/ololand-ai/ololand-plugins/playbook-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/commands/ololand-ai/ololand-plugins/playbook-recall"><img src="https://agentmods.dev/badge/commands/ololand-ai/ololand-plugins/playbook-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 | $0.00037 | $0.00731 |
| Opus 5 | $0.00018 | $0.00365 |
| Sonnet 5 | $0.00007 | $0.00146 |
| Haiku 4.5 | $0.00004 | $0.00073 |
Grade A, and why
playbook-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 8d 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 — 51 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Playbook Recall
Surfaces your firm's institutional memory on deals like this one. For each similar past deal, returns the playbook moves that worked, the moves that didn't, the risks that were correctly flagged, and (most usefully) the risks that were missed and materialized post-close.
Usage
/playbook-recall <deal_id>
Arguments
deal_id(required) — The current deal to recall playbooks for. Similarity is computed against industry (35%), size (25%), deal type (20%), and margin profile (20%).
Execution
-
Call
find_similar_dealsfrom the MCP server with thedeal_id. Returns up to 8 most similar past deals. -
If the response is
status: "no_usable_corpus"— stop here. Tell the user explicitly that institutional memory cannot support this deal yet (strict deal-type / sector-family / size-ratio filters couldn't form a usable cohort). Do NOT fabricate a cohort from looser matching. -
For each similar deal in a usable cohort, walk the
outcome/learning_insightsblocks thatfind_similar_dealsreturns directly:- The risk categories that were flagged during DD vs. the risks that materialized post-close
- The accuracy patterns (where projections were systematically optimistic / pessimistic)
- The valuation ranges that closed vs. the underwritten range
-
For deeper context on specific past deals, use
get_deal,get_deal_risks, andget_evidence_linkson the historical deal IDs returned in step 1. -
Synthesize into a structured playbook recall:
- What worked — moves that recurred across multiple similar deals with positive outcomes
- What didn't — moves attempted but with poor outcomes; treat as anti-patterns
- What was missed — risks that weren't flagged during DD but materialized post-close. This is the most valuable section: it surfaces the systematic blind spots in your firm's prior reads of this deal type.
- Calibration — for each metric the current deal is presenting (revenue growth, EBITDA margin, leverage), the historical accuracy of similar deals' projections vs. realizations.
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.
- 8d ago First seen · 51 lines · 37 tokens per session scan A c65beaa39a50
playbook-recall is a command published in the GitHub repository ololand-ai/ololand-plugins (0 stars, last pushed 4d ago), licensed Apache-2.0. It adds 37 tokens to every session and 731 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.
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.
lians-recall
Recall current (non-stale) facts from Lians memory, optionally as-of a past date.