rail-grill
Skill5 starsChanged 4 months agoMITView on GitHub
Relentless interview to sharpen a plan or design when there is no codebase. Saves nothing locally.
In this repository12 other files it gives its agents.
- AGENTS.mdAGENTS.md
- CLAUDE.mdCLAUDE.md
- rail-alignSkill
- rail-buildSkill
- rail-debugSkill
- rail-passSkill
- rail-reviewSkill
- rail-setupSkill
- railSkill
- rail-sliceSkill
- rail-specSkill
- rail-tddSkill
The file
We split this skill into the rules it gives an agent
10 rules. About 0 are common, 8 are distinctive, and 2 are very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈8
Distinctive · estimate
This author's own choices. Worth a look.
2
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Distinctive: line 13
Ask one question at a time.
- Distinctive: line 14
Prefer multiple-choice when options are clear
- Distinctive: line 15
If a *fact* is findable in the environment, look it up — do not ask
- Very specific: line 17
Do not write any docs/monorail/ files (including CONTEXT.md / ADRs) — no codebase / intentionally stateless
- Distinctive: line 18
Do not implement code
- Distinctive: line 19
Every question uses the Question format below — without exception
- Distinctive: line 37
A is always the recommended option: Write the recommended answer first and label it A — never list it in a later slot
- Distinctive: line 41
No other phrasing of a question is permitted
- Distinctive: line 45
When every decision-tree branch is resolved:
- Very specific: line 49
Do not suggest /rail-setup, /rail-align, /rail-spec, or any other skill handoff in this closing message.
≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
Rail Grill
Interview the user relentlessly about every aspect of the plan until shared understanding. Walk each branch of the decision tree, resolving dependencies one by one. For each question, provide your recommended answer.
Rules
- Ask one question at a time; wait for the answer before continuing
- Prefer multiple-choice when options are clear
- If a fact is findable in the environment, look it up — do not ask
- Decisions belong to the user — put each one to them
- Do not write any
docs/monorail/files (includingCONTEXT.md/ ADRs) — no codebase / intentionally stateless - Do not implement code
- Every question uses the Question format below — without exception
Question format
A hard template. Every question must have exactly this structure:
**问题:** <question text>
**推荐:A**
- **A.** <conclusion>.<one line of reason>
- **B.** …Constraints:
- The
**问题:**line carries the question; the**推荐:A**line is separate and follows it - A is always the recommended option. Write the recommended answer first and label it A — never list it in a later slot
**推荐:A**is fixed text — the recommendation line never names a letter other than A- Options are prefixed
- **A.**,- **B.**, and so on, starting from the recommended one - Option length is unconstrained — expand as the problem warrants
- No other phrasing of a question is permitted
Completion
When every decision-tree branch is resolved:
- Output the plan — a concise synthesis of decisions (goals, scope, key choices, out-of-scope). Do not ask the user to confirm "is this the full scope?" or re-gate on shared understanding; the interview already did that work.
- Ask only whether to start implementation, or if anything still needs adjusting. Wait for that answer.
- Do not suggest
/rail-setup,/rail-align,/rail-spec, or any other skill handoff in this closing message.
Does it work?
Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.
No reviews yet
People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.
- It worked
- Partly
- It didn't
Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.
Your trail
Files you open will collect here, so you can find them again.
Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.
Now do this for your own.
Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.

