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/managed-agent)<a href="https://agentmods.dev/commands/ololand-ai/ololand-plugins/managed-agent"><img src="https://agentmods.dev/badge/commands/ololand-ai/ololand-plugins/managed-agent/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/managed-agent"><img src="https://agentmods.dev/badge/commands/ololand-ai/ololand-plugins/managed-agent.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 | $0.00114 | $0.01393 |
| Opus 5.5 | $0.00046 | $0.00557 |
| Sonnet 5.5 | $0.00023 | $0.00279 |
| Haiku 4.5 | $0.00011 | $0.00139 |
Grade A, and why
managed-agent 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 — 109 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/managed-agent (cloud deal specialists)
Runs one of OloLand's managed deal specialists — agents hosted on the Claude Platform that work a deal through OloLand's scoped, audited tool rail and return a sourced, citation-bearing answer. Use it when a question deserves a dedicated specialist pass rather than an inline answer: a full risk read, a forensic QoE battery, a pre-LOI screen, financing prep, or an IC-grade synthesis where the coordinator delegates to risk, forensic, and memo specialists in parallel.
Usage
/managed-agent <deal_id> <specialist> <question or instruction>
/managed-agent list
Arguments
deal_id(required) — the OloLand deal the specialist should work on.specialist(required) — one of thetemplate_ids returned bymcp__ololand__list_managed_agents. Typical roster:risk-chat— maps findings to the risk taxonomy with severity, likelihood, evidenceforensic-chat— Beneish, Benford, EBITDA bridge, cross-document reconciliationscreen-chat— pre-LOI go/no-go: premium analysis, accretion/dilution, antitrust HHIfinancing-chat— financing analysis, capital-provider sourcing prep, lender-package prepic-memo— IC memo drafting; can persist a DRAFT IC packageic-coordinator— multi-agent IC synthesis (delegates to risk + forensic + memo in parallel)lead-orchestrator— general sourced deal Q&A
question(required) — what the specialist should do.
Execution
This is an asynchronous managed-agent run. Do not expect an immediate answer; start it, then poll.
listmode. If the user passedlist(or asked what specialists exist), callmcp__ololand__list_managed_agentsand render each agent'stemplate_id, description, and whether it isconfiguredin this environment. Recommend the best fit for what the user is working on, then stop.- Start. Call
mcp__ololand__run_managed_agentwithdeal_id,template_id, andprompt. It returns atask_id(withtask_type: "managed_interactive_agent").{"error_code": "not_found"}— wrong deal id; confirm it withmcp__ololand__list_deals.{"error_code": "invalid_template"}— the specialist name is not callable; show the roster frommcp__ololand__list_managed_agentsand let the user pick.{"error_code": "not_configured"}— the specialist exists but is not provisioned in this environment; relay that and suggest another specialist from thelistoutput that showsconfigured: true.{"error": "Company scope required …"}— the MCP connection is not company-scoped; tell the user to reconnect / sign in, do not retry blindly.- Any other tool-level error fails closed — relay it rather than synthesizing an answer.
- Poll. Call
mcp__ololand__check_task_statuswith thetask_idevery few seconds. WhilestateisSTARTED/PROGRESS, relay the progress message. The IC coordinator's delegating turns run multi-minute — keep the user informed rather than giving up. Keep polling untilstateisSUCCESSorFAILURE. - On
FAILURE— report theerrorverbatim. Do not fabricate a specialist answer. - On
SUCCESS— theresultobject is:response— the specialist's answer, already source-cited. This is the deliverable; render it unaltered.run_id— the OloLandagent_runsaudit row.claude_platform_session_id— the hosted session for operator trace.tool_calls,input_tokens,output_tokens— run telemetry.citations— structured citation payloads backing[N]markers, when present.
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 · 109 lines · 114 tokens per session scan A fb00737b771f
managed-agent is a command published in the GitHub repository ololand-ai/ololand-plugins (0 stars, last pushed 3d ago), licensed Apache-2.0. It adds 114 tokens to every session and 1,393 once invoked, about $0.0005 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.
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