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/plangit clone --depth 1 https://github.com/ololand-ai/ololand-pluginsWhat 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 | $0.00050 | $0.00985 |
| Opus 5 | $0.00025 | $0.00492 |
| Sonnet 5 | $0.00010 | $0.00197 |
| Haiku 4.5 | $0.00005 | $0.00098 |
Grade A, and why
plan 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 2d 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 — 77 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/plan
You are running OloLand's planner agent in plan-mode. The planner reads the user's question, the active deal context, and produces a structured PlanResponse (steps, open_questions, escalation_required, reasoning, estimated tool calls) — without running any tools, doing any document search, or escalating to templates. It's a single Flash-class call; cheap relative to the executor run that follows.
Required inputs
- deal_id — the deal the conversation is about.
- session_id — an active ADK conversation session. If the user doesn't have one, call
mcp__ololand__create_conversation_session(deal_id)first and use the returned id. - message — the user's question that the planner should plan for. Quote it verbatim — don't paraphrase.
Action
Call mcp__ololand__submit_plan_for_approval with the three inputs.
Output
Render the plan in this shape:
Proposed plan for: "<message>"
Steps
1. <step.title> — <step.description>
Tools: <step.tools (if any)>
2. ...
Open questions
- <q1>
- ...
Estimated tool calls: <n>
Escalation required: <true|false>
Reasoning: <reasoning>
Then ask the user:
Approve this plan? Reply "yes" to approve the handoff, "no" to adjust, or rewrite any step. A same-chat "yes" records intent only; it does not execute the plan. Execution requires continuing this same session in OloLand's first-party app or normal session endpoint with the returned plan payload supplied as
approved_plan.
On approval, continue the same session through OloLand's first-party app or
normal SSE/message endpoint, attaching the returned plan payload in the
approved_plan field of SubmitMessageRequest. This field supplies execution
context; it is not currently a persisted or hash-validated approval identity,
so do not describe the handoff as tamper-proof or cryptographically bound. The external MCP
ask_deal_agent and talk_to_deal tools do not currently accept the normal
endpoint's approved_plan field; do not call them for the planned execution, and
do not treat quoted plan prose or a conversational "yes" as a substitute for
that binding. If the current client cannot send approved_plan through the
normal session endpoint, stop and report that governed execution is
unavailable on this rail.
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.
- 2d ago First seen · 77 lines · 50 tokens per session scan A 761260b87691
plan is a command published in the GitHub repository ololand-ai/ololand-plugins (0 stars, last pushed 6d ago), licensed Apache-2.0. It adds 50 tokens to every session and 985 once invoked, about $0.0003 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
git
Git operations with intelligent commit messages and workflow optimization.
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.