Skip to content
cccskills
FreePublished on GitHub

grill-me

Test a plan before implementation through structured, challenging questions. Work through decisions in dependency order, receive recommendations with each question, and finish with a clear account of agreed choices, open issues, and scope.

OriginalShow the original description

Adversarial plan interview before implementation.

See how to install

Good for

  • Clarifying an idea before implementation
  • Testing authentication or payment plans
  • Examining failure scenarios and rollback options
  • Agreeing on scope and decisions before coding

Files included (1)

  • SKILL.md4.6 KB

SKILL.md (original)

Review the instructions the agent will receive before you install.

Grill Me

Stress-tests a plan through structured adversarial questioning before any code is written. Models the plan as a design tree — every decision branches into the decisions that hang off it — and interviews the user in rounds until every branch is resolved and nothing is silently assumed.

Combines the phase discipline of the original with the frontier-rounds mechanic from mattpocock/skills' grilling.

When to Use

  • User says "grill me", "interview my plan", "stress test this idea"
  • Before complex work: auth flows, schema changes, migrations, payments
  • A plan has unresolved decisions or seems vague
  • Before subagent-driven-development decomposition

Do NOT use for existing code (use requesting-code-review) or simple one-off tasks.

Prerequisites

None. The skill works on any plan or raw idea.

Core Mechanic: Frontier Rounds

Map the plan as a design tree. The frontier is every decision whose prerequisites are already settled — the questions you can ask NOW without guessing at answers you haven't heard yet.

Work in rounds: ask the whole current frontier in one message, numbered, each question carrying your recommended answer. Then wait. A question whose answer depends on another question still open in this round belongs to a LATER round, not this one.

Format each round like so:

❓ Q1 — <question title>: <question body, options if relevant>
➡️ Recommendation: <your recommended answer + one-line why>

❓ Q2 — <question title>: <question body>
➡️ Recommendation: <...>

Each answer reshapes the tree: settled decisions push the frontier outward and unblock dependent questions. Recompute the frontier and ask the next round.

Facts are your job; decisions are the user's. When a frontier question needs a fact from the environment (codebase, filesystem, config, docs), find it yourself with search_files / read_file / terminal — or dispatch a subagent via delegate_task for a heavy exploration. Never ask the user for anything you could look up. Don't block on an exploration: only the questions downstream of it wait; ask the rest of the frontier now.

Question Coverage (work these branches into the tree)

Understanding — the real goal and boundaries:

  • What is the ACTUAL objective? What is explicitly IN and OUT of scope?
  • What are the constraints (time, tech, team, budget)? Who are the users?

Technical decisions — for each architectural choice:

  • "Why this approach and not X?" / "What happens if Y fails?"
  • "What's the worst case?" / "How would you roll back?"
  • Cross-reference the existing codebase; if the project already has a pattern for this, call it out.

Edge cases:

  • "What happens if the user does Z?" / "What if dependency X goes down?"
  • "What if volume is 100x expected?" / "What are the security implications?"

Synthesis (when the frontier is empty)

  1. Summarize ALL decisions in bullet points
  2. List anything left open, and what is explicitly OUT of scope
  3. Ask: "Aligned? Should I start implementing, or adjust anything?"

Do not act on the plan until the user confirms shared understanding.

Pitfalls

  1. Asking questions out of dependency order. A question that depends on an unanswered question is a guess wearing a question mark. Keep it for a later round.
  2. Skipping the codebase. Find facts in code with Hermes tools instead of asking the user.
  3. Accepting "I don't know" as final. Suggest options, explain trade-offs, make a recommendation.
  4. Writing code during the interrogation. Alignment only — code after the explicit green light.
  5. Being too agreeable. Your job is to find problems. If everything looks fine, look harder.
  6. Not adapting to the user's language. Interview in whatever language the user speaks.

Verification

  • Every question in a round had all its prerequisites already settled
  • Provided a recommendation with each question
  • Explored the codebase for facts instead of asking the user
  • Frontier empty (no branch silently assumed) before synthesizing
  • Produced a clear summary of all decisions and open items
  • Confirmed user alignment before stopping

Reviews

No reviews yet. Tell others what you thought.

Comments

No login needed. Share how it worked for you, ask a question, or note where it did not run.

No comments yet. Be the first to write one.

Comments are published right away. Do not include personal information such as email addresses or phone numbers. Up to 2 URLs.

0 / 1,000 characters

This form is protected by reCAPTCHA, and the Google Privacy Policy and Terms of Service apply.

More skills from this repository

1password

FreeSummary

Set up the 1Password CLI and use stored passwords or API keys in commands and configuration templates. Choose an authentication method, retrieve secret references, and pass credentials to processes through environment variables.

  • Installing and configuring the 1Password CLI
  • Running commands with stored API keys
  • Filling configuration templates with stored secrets
NousResearch/hermes-agent255KUpdated Oct 18, 2026

Build an Excel workbook linking the income statement, balance sheet, and cash flow statement. Populate historical figures, create formula-based forecasts, compare scenarios, and check that balances and cash totals agree across all three statements.

  • Populating a financial model template with historical data
  • Building linked forecasts across three financial statements
  • Comparing base, upside, and downside scenarios
NousResearch/hermes-agent255KUpdated Oct 18, 2026

accelerate

FreeSummary

Adapt existing PyTorch training scripts to use multiple GPUs or machines with limited code changes. The skill guides configuration, launch commands, mixed precision, which uses lower-precision numbers, and DeepSpeed or FSDP settings for distributing training work and memory.

  • Move a single-GPU training script to multiple GPUs
  • Configure mixed precision training
  • Set up DeepSpeed or FSDP to reduce memory use
NousResearch/hermes-agent255KUpdated Oct 18, 2026

actual-setup

FreeSummary

Configure Hermes to run AI models through Actual Computer on your own hardware. Set up either the hosted relay or a local connection, select models, verify responses, and troubleshoot request errors or empty output.

  • Connecting Hermes with an Actual inference key
  • Running models through a local Actual daemon
  • Downloading and loading local models
NousResearch/hermes-agent255KUpdated Oct 18, 2026

Test an app from the perspective of a frustrated, less technical user. Find confusing steps and barriers, separate actionable usability problems from personal complaints, and produce screenshots, suggested fixes, and prioritized tickets.

  • Reviewing usability before a launch or demo
  • Checking the experience of a new user
  • Finding friction in signup and subscription flows
NousResearch/hermes-agent255KUpdated Oct 18, 2026

Resolve Git merge conflicts between two agents by comparing both changes and their stated goals. Combine compatible work, explain choices where designs differ, and verify the merged result with project builds or tests.

  • Resolving conflicts between branches edited by two agents
  • Integrating changes from parallel worktrees
  • Reviewing reasons for conflicting design choices
NousResearch/hermes-agent255KUpdated Oct 18, 2026

See all skills by NousResearch

Report a problem with this skill