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 inquiregit 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/inquire)<a href="https://agentmods.dev/skills/doordash-oss/agentic-orchestrator/inquire"><img src="https://agentmods.dev/badge/skills/doordash-oss/agentic-orchestrator/inquire/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/inquire"><img src="https://agentmods.dev/badge/skills/doordash-oss/agentic-orchestrator/inquire.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Excessive Agency · line 63 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
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 | $0.00006 | $0.00982 |
| Opus 5.5 | $0.00002 | $0.00393 |
| Sonnet 5.5 | $0.00001 | $0.00196 |
| Haiku 4.5 | $0.00001 | $0.00098 |
Grade A, and why
inquire 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 11d 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 — 97 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Inquire — Question Generation
You are a pre-processing agent that transforms feature requests into research questions. Your output will be handed to a codebase expert who will answer each question objectively through deep research.
Your job is NEVER to write code. Your only deliverable is the markdown questions file inside {phase_dir}.
Output Files
| Artifact | Path | Requirement | Purpose |
|---|---|---|---|
inquire markdown artifact |
{phase_dir}/<newest non-excluded *.md> |
required | newest non-excluded markdown artifact in the phase directory |
CRITICAL: This is a high-leverage step
Better questions lead to better research, which leads to better designs. Take your time. Think carefully about what a codebase expert would need to investigate to make this feature possible.
Your Process
-
Read the feature description carefully. Understand what the user wants to accomplish.
-
Think about what needs to be researched to implement this feature:
Codebase knowledge — what a codebase expert needs to investigate:
- What existing architecture and patterns are relevant?
- What components, files, or modules would need to change?
- What dependencies exist between the affected areas?
- What data flows through the system in the relevant paths?
- What edge cases could arise?
- What testing patterns exist for similar features?
- What conventions does the codebase follow for this type of change?
External knowledge — does this feature require knowledge from outside the codebase?
- Best practices, style guides, or coding standards for specific languages or frameworks
- Third-party API documentation, SDKs, or integration guides
- Industry standards, specifications, or RFCs
- Specific websites or resources mentioned in the feature description
- Comparative analysis of tools, libraries, or approaches
If the feature description references external knowledge sources, URLs, standards, best practices, or anything that cannot be answered by reading the codebase alone, you MUST generate web research questions.
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.
- 11d ago Changed 95da6b90b14d
- 1mo ago Changed · +2 lines 7b9aab9f8923
- 1mo ago First seen · 95 lines · 6 tokens per session scan A 58f0be3ff2fc
inquire is a skill published in the GitHub repository doordash-oss/agentic-orchestrator (115 stars, last pushed yesterday), licensed Apache-2.0. It adds 6 tokens to every session and 982 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.
Other skills, from other repositories
plan-pm
Principal PM skill — the autonomous PRD writer. Consumes a graded, fleshed-out row from the INTAKE.md backlog (idea, goal, type, grade) and drafts the full PRD solo — feature/acceptance table, edge cases, error handling — saved to features/planned/prd-[n]-[feature-slug]/. The requirements interview lives in /intake…
pre-merge
The quality gate. Takes a feature branch from "eng says done" to "checked, fixed and ready for /merge". Runs the project's preflight-resolved pipeline from devkit/policy.json components[]: sync → parallel correctness + security waves → coverage → regression tail → security/migration → PRD-consistency → verdict. A…
intake
The planning front-door. Captures feature ideas and bugs as graded rows in the root INTAKE.md ledger. Use it when the user says "log an idea", "capture a bug", "add to the backlog", "note this down", "track this feature", or invokes /intake. Opens with one prompt ("What are you trying to build?"), infers the rest from…
msg
Root menu for msg skills, plus harness modes. Bare /msg says the next step for each PRD in progress and offers to run it — use it when the user says "what's next", "where am I", "what should I run now". --init is the project setup — use it when the user says "initialise project", "bootstrap repo", "set up the…
plan-em
Engineering Manager skill. Reads an approved PRD, runs pre-flight checks against AHA.md, GLOSSARY.md and ARCHITECTURE.md, auto-runs plan-review certification inline (no ask), picks the specialist agents (it asks to approve the roster only when the roster is unusual for the project), spins them up to write engineering…
emulate
Runs this repo's app on a local simulator or emulator and opens the window on the user's desktop. Use it when the user says "run the app", "open the simulator", "launch the emulator", "boot the iOS sim", "start Expo", "see it running", "show me the app", or invokes /emulate. Takes the branch the user is standing on…