microsoft/agentic-journeys is a collection of guided projects that shows how AI coding agents and skills can plan, build, and deploy applications on Azure. It is for developers building Azure applications and teams evaluating agent-assisted development workflows, including APIs, databases, frontends, and AI features. The catalogue entries supply skills, agents, and instructions used in these journeys.
Borrowing it
Nothing to install: this file belongs to microsoft/agentic-journeys. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/microsoft/agentic-journeys/main/.github/skills/grill-plan/SKILL.mdgit clone --depth 1 https://github.com/microsoft/agentic-journeysWrote 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/microsoft/agentic-journeys/grill-plan)<a href="https://agentmods.dev/skills/microsoft/agentic-journeys/grill-plan"><img src="https://agentmods.dev/badge/skills/microsoft/agentic-journeys/grill-plan/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/microsoft/agentic-journeys/grill-plan"><img src="https://agentmods.dev/badge/skills/microsoft/agentic-journeys/grill-plan.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 | $0.00121 | $0.00654 |
| Opus 5.5 | $0.00048 | $0.00262 |
| Sonnet 5.5 | $0.00024 | $0.00131 |
| Haiku 4.5 | $0.00012 | $0.00065 |
Grade A, and why
grill-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 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 — 35 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Grill the Plan
Find the decisions a plan leaves open before an agent fills them in silently. The output is a short list of decisions and the tests that will prove them.
Process
- Read first. Read the issue and every plan document and section it links to. Do not ask questions the plan already answers.
- Mandatory questions. If a linked plan has a
Decision Pointssection, ask about every item in it. Each item lists a default. Offer that default as the first, recommended option. - Your own questions. Then ask up to three more questions about gaps you found yourself. Good targets are boundary values, failure paths, what happens to related data, and how each acceptance criterion will be tested. Skip questions whose answer would not change the code or the tests.
- One at a time. Ask each question with the ask-user tool. Include two to four options, put the recommended default first, and add one sentence on why the choice matters. If the user says "use the defaults", accept every remaining default and stop asking.
- Summarize. When the questions are done, print:
- A
Decisionstable with the columns Question, Decision, and Default or changed. - A
Test listwith one row per behavior (Behavior, Plan section, Proposed test name). The tdd-builder agent writes these tests in the red phase. - Anything still unresolved.
- A
- Record it. When the user asks, post the summary as a comment on the issue with
gh issue comment <number> --body-file <file>. Write the body to a temporary file first so the command works in PowerShell, Command Prompt, bash, and zsh. Delete the temporary file afterward.
Non-interactive sessions
If the ask-user tool isn't available, as in copilot -p or a cloud agent session, don't pretend an interview happened. Apply the recommended default for every question, mark each plan Decision Point row in the Decisions table as Default (not asked) and each question you raised yourself as Agent recommendation (not asked), and start the summary with one sentence saying that the interview ran non-interactively.
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 · 35 lines · 121 tokens per session scan A 0cfa04bbc5f4
grill-plan is a skill published in the GitHub repository microsoft/agentic-journeys (6 stars, last pushed 12d ago), licensed MIT. It adds 121 tokens to every session and 654 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 2026-09-28.
grill-plan compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| grill-planmicrosoft/agentic-journeys | Skill | 6 | 12d ago | MIT | A | 121 | Claude Code, Codex |
| imagegenopenai/codex | Skill | 133k | yesterday | Apache-2.0 | A | 113 | Codex |
| playwright-climicrosoft/playwright | Skill | 99k | yesterday | Apache-2.0 | A | 19 | Claude Code |
| local-ai-agentsmicrosoft/ai-agents-for-beginners | Skill | 79k | 4d ago | MIT | A | 200 | Claude Code, Codex |
| systematic-debuggingobra/superpowers | Skill | 313k | 3d ago | MIT | A | 21 | Claude Code |
| next-cache-components-adoptionvercel/next.js | Skill | 143k | today | MIT | A | 95 | Claude Code |
| next-partial-prefetching-adoptionvercel/next.js | Skill | 143k | today | MIT | A | 103 | Claude Code |