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 skills add ololand-ai/ololand-plugins --skill deal-session-memorygit 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/skills/ololand-ai/ololand-plugins/deal-session-memory)<a href="https://agentmods.dev/skills/ololand-ai/ololand-plugins/deal-session-memory"><img src="https://agentmods.dev/badge/skills/ololand-ai/ololand-plugins/deal-session-memory/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/skills/ololand-ai/ololand-plugins/deal-session-memory"><img src="https://agentmods.dev/badge/skills/ololand-ai/ololand-plugins/deal-session-memory.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 | $0.00071 | $0.01023 |
| Opus 5.5 | $0.00028 | $0.00409 |
| Sonnet 5.5 | $0.00014 | $0.00205 |
| Haiku 4.5 | $0.00007 | $0.00102 |
Grade A, and why
deal-session-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 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 — 67 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Deal Session Memory
deal_session_memory is the table that turns OloLand from a stateless reasoning engine into a persistent deal record. Every fact persisted via remember_deal_fact survives across conversation sessions, analyst handoffs, and even LLM-provider changes.
Why it exists
The marketing claim "OloLand captures the institution, not the session" is load-bearing only if there is somewhere durable to capture to. Conversation history is per-session and stale within hours. Document ingestion is per-corpus and doesn't capture analyst judgment. Risk corrections and outcomes have dedicated tables but cover their own narrow domains.
deal_session_memory is the catch-all for analyst-confirmed facts. It is the place where you write:
- "FY24 EBITDA is $34.2M, confirmed against the CPA-audited statements" —
ebitda_fy24_confirmed: 34200000. - "Sponsor's lead partner is Jim from MidOcean" —
sponsor_lead_partner: "Jim Smith (MidOcean)". - "We're normalizing WC against the 3-year average, not the trailing 12, after partner discussion 2026-05-12" —
wc_normalization_convention: "3yr_average"with a note. - "LOI signed 2026-05-20" —
loi_signed_date: "2026-05-20".
Why it is not the conversation log
Conversations are noisy — they contain hypotheses, partial answers, dead-end branches. deal_session_memory is the curated subset: only facts an analyst has confirmed. The discipline matters; otherwise the recall surface fills with noise and stops being useful.
Storage shape
| Field | Constraint |
|---|---|
deal_id |
Foreign key to deals — facts are deal-scoped, not user-scoped. |
company_id |
Inherited from the deal. Multi-tenant isolation. |
key |
≤ 200 chars. Short, stable, kebab-case. |
value |
Any JSON value. Scalar, list, or small object. |
note |
≤ 1000 chars. Optional provenance. |
updated_at |
Set on every write. |
Re-writing the same (deal_id, key) is idempotent — value and note are replaced.
Institutional pattern
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 · 67 lines · 71 tokens per session scan A 477271b1e8f9
deal-session-memory is a skill published in the GitHub repository ololand-ai/ololand-plugins (0 stars, last pushed 3d ago), licensed Apache-2.0. It adds 71 tokens to every session and 1,023 once invoked, about $0.0003 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 2026-09-03.
Other skills, from other repositories
tushare-data
A Chinese-language workflow for researching financial and market data through Tushare, a data service covering Chinese stocks and related economic information. It turns natural-language questions into steps for retrieving, cleaning, comparing, filtering, exporting, and briefly analysing data.
trade-memory
Decision audit trail for AI trading agents. Records every trading decision with full context (conditions, filters, indicators, risk state) in a SHA-256 hash chain, so a later edit is detectable, and exports it as JSON or JSONL. Works alongside Binance Spot, Futures, and Web3 skills: they execute trades, TradeMemory…
tradememory-bridge
Bridge between Binance trading events and TradeMemory Protocol. Automatically journals trades, recalls similar past setups, detects behavioral biases, and provides outcome-weighted recall for AI trading agents. Use this skill after executing Binance spot trades to build persistent memory.
evolution-engine
Domain knowledge for the Evolution Engine — LLM-powered autonomous strategy discovery from raw OHLCV data. Covers the generate-backtest-select-evolve loop, vectorized backtesting, out-of-sample validation, and strategy graduation. Use when discovering trading patterns, running backtests, evolving strategies, or…
risk-management
Risk management domain knowledge for trading agents — affective state monitoring, position sizing, drawdown management, tilt detection, and behavioral guardrails. Use when checking risk before trades, managing drawdowns, detecting behavioral drift, or enforcing discipline. Triggers on "risk", "drawdown", "tilt"…
trading-memory
Domain knowledge for AI trading memory — Outcome-Weighted Memory (OWM) architecture, 5 memory types, recall scoring, and behavioral analysis. Use when recording trades, recalling similar contexts, analyzing performance, or checking behavioral drift. Triggers on "record trade", "remember trade", "recall", "similar…