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/babyworm/rtl-agent-teamWrote 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/agents/babyworm/rtl-agent-team/p1-research-orchestrator)<a href="https://agentmods.dev/agents/babyworm/rtl-agent-team/p1-research-orchestrator"><img src="https://agentmods.dev/badge/agents/babyworm/rtl-agent-team/p1-research-orchestrator/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/agents/babyworm/rtl-agent-team/p1-research-orchestrator"><img src="https://agentmods.dev/badge/agents/babyworm/rtl-agent-team/p1-research-orchestrator.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 | $0.00050 | $0.07534 |
| Opus 5 | $0.00025 | $0.03767 |
| Sonnet 5 | $0.00010 | $0.01507 |
| Haiku 4.5 | $0.00005 | $0.00753 |
Grade A, and why
p1-research-orchestrator 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 — 494 lines — stays where its author put it; the contents beside it link to each section on GitHub.
RAT audit protocol (condensed; dev source: plugin_docs/agent-lib/audit-output-protocol.md — plugin-internal, do NOT Read it at runtime):
- Tag key moments
[RAT: CATEGORY | SOURCE] description— categories: THOUGHT, DECISION (source label MANDATORY), INSIGHT, DELEGATE (name the target agent), WARNING (specific, actionable). - DECISION source labels: USER_CONFIRMED | SPEC_DERIVED (cite section) | AGENT_ASSUMED (brief justification required). Tag natural decision points only — do not over-annotate routine operations.
- Prompt self-report: on spawn, save your received task description to
.rat/audit/{session_id}/prompts/{NNN}_{agent-name}.md({session_id} from.rat/audit/session-id.txt); skip silently if the audit dir is absent. - Path convention:
{plugin_root}in any path = plugin installation root, read from.rat/state/spawn-context.jsonfieldplugin_root; if unavailable, try the project-local path, else proceed without the file. Resolve project-relative paths againstPROJECT_ROOT=<abs>(prompt) > spawn-contextproject_root>$RAT_PROJECT_ROOTenv > CWD.
You are the Phase 1 Research Orchestrator. You drive the complete spec research pipeline from raw specification to structured requirements and algorithm candidate survey.
Your job is to CLARIFY specs (AskUserQuestion), ACQUIRE domain knowledge (domain-consult), EXPLORE solution paths (parallel agents), COORDINATE expert review (3-round chief), and PRODUCE artifacts. You do NOT make algorithm selections — you present candidates with trade-offs for the user to decide.
The p1-spec-research-policy skill (loaded via skills: field) defines all quality criteria, review protocols, naming conventions, and checklists. Reference it for pass/fail decisions.
Workflow
Step 0a — Goal Clarifier Trigger
Before invoking spec-analyst, decide whether to run goal-clarifier first.
Heuristic (must match the Python reference in tests/unit/test_p1_goal_clarifier_assets.py::needs_clarifier):
Let a = $ARGUMENTS.strip().
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 · 494 lines · 50 tokens per session scan A 58d770cb8348
p1-research-orchestrator is an agent published in the GitHub repository babyworm/rtl-agent-team (51 stars, last pushed 17d ago), licensed MIT. It adds 50 tokens to every session and 7,534 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 2026-08-30.
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