From toml-skills
Grilling session like grill-me, but every recommendation gets independently challenged by an expert architect sub-agent specialised in the relevant tech. The architect reviews each proposed answer through a maintainability and long-term evolvability lens. Use when user wants their plan grilled AND each recommendation peer-reviewed.
How this skill is triggered — by the user, by Claude, or both
Slash command
/toml-skills:grill-challengeThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
Interview the user relentlessly about every aspect of the plan until shared understanding is reached. Walk each branch of the decision tree, resolving dependencies between decisions one by one. For each question, provide a recommended answer.
Interview the user relentlessly about every aspect of the plan until shared understanding is reached. Walk each branch of the decision tree, resolving dependencies between decisions one by one. For each question, provide a recommended answer.
Ask questions one at a time. Wait for feedback before continuing.
If a question can be answered by exploring the codebase, explore instead of asking.
After formulating each question + recommended answer — BEFORE showing it to the user — spawn a sub-agent via the Agent tool (subagent_type: general-purpose, model: opus — mandatory, max reasoning quality) acting as an expert architect in the technology under discussion. Pick the specialty per question:
Self-contained brief (the agent has zero conversation context):
You are a senior <SPECIALTY> architect. Review this design recommendation through one lens only: long-term maintainability and evolvability of the codebase.
Context (project): <2–4 line summary of relevant project context>
Question being asked: <the question>
Recommended answer: <the recommendation, with the reasoning>
Produce:
1. Verdict — agree / agree-with-caveats / disagree
2. Maintainability risks — what hurts in 6–24 months (coupling, lock-in, migration cost, test friction, onboarding cost)
3. Evolvability risks — what blocks likely future changes (scale, new requirements, team growth, tech swap)
4. Counter-proposal if disagree, or refinement if caveats
5. One-line bottom line
Be blunt. No filler. Under 250 words.
Run sub-agents in foreground — the verdict shapes what is shown next. Always pass model: "opus" in the Agent call so the architect reasons with max capability.
For each turn, output in this order:
<SPECIALTY>) — the sub-agent's verdict + key risks + counter-proposal if anyThen wait for user feedback before moving to the next question.
Guides creation, editing, and verification of skills for AI coding agents using test-driven development with subagent scenarios. Use when authoring or debugging skills.
npx claudepluginhub tkubasik-luna/toml-skills --plugin toml-skills