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/talk-to-deal)<a href="https://agentmods.dev/commands/ololand-ai/ololand-plugins/talk-to-deal"><img src="https://agentmods.dev/badge/commands/ololand-ai/ololand-plugins/talk-to-deal/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/talk-to-deal"><img src="https://agentmods.dev/badge/commands/ololand-ai/ololand-plugins/talk-to-deal.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 | $0.00025 | $0.00809 |
| Opus 5 | $0.00013 | $0.00404 |
| Sonnet 5 | $0.00005 | $0.00162 |
| Haiku 4.5 | $0.00003 | $0.00081 |
Grade A, and why
talk-to-deal 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 10d 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 — 49 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Talk to Your Deal
Have a natural conversation about a deal. Returns voice-optimized responses suitable for hands-free review, Claude Dispatch, or quick deal check-ins.
Usage
/talk-to-deal <deal_id> <question>
Arguments
deal_id(required) — The deal to ask about.question(required) — Natural language question about the deal.
Execution
- Classify the request before choosing a rail:
- For a simple lookup-style question covered by
/plan's no-plan exceptions, calltalk_to_dealfrom the MCP server with the deal_id and question. - Before every other non-trivial deal question, follow
/plan: create or reuse the conversation session, submit the user's verbatim question withsubmit_plan_for_approval, render the proposed steps, and stop for the user's approval. Never invoke either MCP message tool for that planned execution. After approval, direct the user to continue the same session in the OloLand app or normal SSE/message endpoint with the returned plan payload supplied asapproved_planonSubmitMessageRequest. That field supplies execution context; it is not a persisted or hash-validated approval identity. The MCPtalk_to_dealandask_deal_agenttools cannot carry it, and quoted plan text is not a substitute. If the client cannot use the normal session endpoint, report governed execution as unavailable on this rail. - Do not fall back to an MCP message call after planning, even if
required_sections=["answer", "evidence", "assumptions", "open_questions"]would otherwise be supported. Completion headings and a read-only boundary do not replace the normal endpoint'sapproved_planexecution context.
- For a simple lookup-style question covered by
- For the lookup-only
talk_to_dealpath, relay any returnedboundary_gate,completion_contract, andgrader_passedfields. A response is not a completed or verified answer merely because text was returned. If any applicable field is absent, unavailable, or failed, say so plainly and do not replace it with an inferred pass. - Every deal fact, figure, date, risk, or recommendation must retain its returned inline source citation. Voice style may round a cited number (for example, "about $160 million [3]"), but must not remove its citation or convert missing evidence into an estimate. State unavailable evidence and the resulting limitation instead.
- Do not state a buyer's or acquirer's identity, strategy, mandate, interest, expected synergies, or likely action unless it is supported by a tenant-authorized returned source citation. A caller-supplied premise may be discussed only when labeled as a hypothetical, not as a fact about the buyer.
- The response uses voice-mode directives:
- Concise (2-3 sentences per point)
- Rounded numbers ("about 160 million" not "$164,501,234")
- Narrative style, no markdown tables
- Clear recommendation or next question at the end
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.
- 10d ago First seen · 49 lines · 25 tokens per session scan A bc634f9d0fee
talk-to-deal is a command published in the GitHub repository ololand-ai/ololand-plugins (0 stars, last pushed 6d ago), licensed Apache-2.0. It adds 25 tokens to every session and 809 once invoked, about $0.0001 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
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.
implement
Execute the implementation plan by processing and executing all tasks defined in tasks.md.