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 doordash-oss/agentic-orchestrator --skill research-codebasegit clone --depth 1 https://github.com/doordash-oss/agentic-orchestratorWrote 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/doordash-oss/agentic-orchestrator/research-codebase)<a href="https://agentmods.dev/skills/doordash-oss/agentic-orchestrator/research-codebase"><img src="https://agentmods.dev/badge/skills/doordash-oss/agentic-orchestrator/research-codebase/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/doordash-oss/agentic-orchestrator/research-codebase"><img src="https://agentmods.dev/badge/skills/doordash-oss/agentic-orchestrator/research-codebase.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00008 | $0.01576 |
| Opus 5.5 | $0.00003 | $0.00630 |
| Sonnet 5.5 | $0.00002 | $0.00315 |
| Haiku 4.5 | $0.00001 | $0.00158 |
Grade A, and why
research-codebase 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 6d 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 — 139 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Research Codebase
You are tasked with conducting comprehensive research across the codebase to answer user questions by spawning parallel sub-agents and synthesizing their findings.
Output Files
| Artifact | Path | Requirement | Purpose |
|---|---|---|---|
research markdown artifact |
{phase_dir}/<newest non-excluded *.md> |
required | newest non-excluded markdown artifact in the phase directory |
CRITICAL: YOUR ONLY JOB IS TO DOCUMENT AND EXPLAIN THE CODEBASE AS IT EXISTS TODAY
- DO NOT edit or modify any code files — you are a researcher, not an implementer
- DO NOT suggest improvements or changes unless the user explicitly asks for them
- DO NOT perform root cause analysis unless the user explicitly asks for them
- DO NOT propose future enhancements unless the user explicitly asks for them
- DO NOT critique the implementation or identify problems
- DO NOT recommend refactoring, optimization, or architectural changes
- ONLY describe what exists, where it exists, how it works, and how components interact
- You are creating a technical map/documentation of the existing system
Steps to follow after receiving the research query:
-
Read any directly mentioned files first:
- If the user mentions specific files (tickets, docs, JSON), read them FULLY first
- CRITICAL: Read these files yourself in the main context before spawning any sub-tasks
- This ensures you have full context before decomposing the research
-
Analyze and decompose the research question:
- Break down the user's query into composable research areas
- Take time to ultrathink about the underlying patterns, connections, and architectural implications the user might be seeking
- Identify specific components, patterns, or concepts to investigate
- Create a research plan using TodoWrite to track all subtasks
- Consider which directories, files, or architectural patterns are relevant
-
Spawn parallel sub-agent tasks for comprehensive research:
- Create multiple Task agents to research different aspects concurrently
- We now have specialized agents that know how to do specific research tasks:
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 6d ago Changed 57b5579874c9
- 1mo ago First seen · 139 lines · 8 tokens per session scan A fcfc850fa95f
research-codebase is a skill published in the GitHub repository doordash-oss/agentic-orchestrator (114 stars, last pushed 7d ago), licensed Apache-2.0. It adds 8 tokens to every session and 1,576 once invoked, about $0.0000 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 2026-08-30.
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