How does 'Grill with JEV' prevent decision paralysis compared to standard chat-based planning?

This skill uses a "grilling" process where an automated persona evaluates your design choices against strict schemas, forcing you to resolve dependencies in structured rounds to prevent decision paralysis.

What specific assets are included in this skill package?

The skill includes the `ask-jev.ts` and `setup.ts` TypeScript scripts, a library of JSON schemas for validation, and a standardized session persistence system for audit logs.

Can I use this skill with different LLM providers, or am I locked into a specific one?

Yes, this skill is designed to integrate with any LLM provider; you simply configure your API keys and provider profiles during the initial `scripts/setup.ts` configuration.

Does the JEV persona make final project changes autonomously, or do I retain control?

While the skill automates decision-making, it is designed with a 'human-in-the-loop' safeguard that requires user sign-off on the final decision record before any external actions are executed.

How does the skill handle complex projects with multiple overlapping technical dependencies?

The skill manages complexity by building a dependency-aware decision tree, ensuring that high-level architectural questions are resolved before trivial implementation details are addressed.

How are updates and versioning handled for the TypeScript scripts?

Updates are delivered through the marketplace platform, providing refined schemas and script optimizations to ensure compatibility with evolving TypeScript environments and LLM models.

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grill with jev

Automated project decision-making using schema-validated rounds and dependency-aware planning.

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grill with jev

Example session with this skill installed

Grill the migration plan to PostgreSQL. Focus on the schema changes for the auth module.

Read your context and instructions
Compiled the grill with jev

I have mapped the decision frontier for the PostgreSQL migration. There are 3 questions across 2 rounds.

PostgreSQL Migration Grill

ID	Title	Question	Recommendation	Risk
Q1	UUID Selection	Use UUIDv7 for primary keys to improve sortability?	Yes	Low
Q2	JSONB vs Relational	Store session metadata in a JSONB column?	Yes	Medium
Q3	Migration Strategy	Use a zero-downtime Blue-Green deployment?	Yes	High (Data)

Decision Record


# Decision Record: Auth Migration

## Decisions
- **UUID Selection**: Accepted (jev). Using UUIDv7.
- **JSONB vs Relational**: Accepted (jev). Metadata will reside in `session_meta` JSONB.
- **Migration Strategy**: Pending (Depends on Q1, Q2).

## Risk Summary
- **High**: Potential data loss during Blue-Green switch if replication lag is not monitored.
- **Medium**: JSONB querying may impact index performance if not optimized.

## Assumptions
- Target DB is PostgreSQL 15+.
- Application layer handles UUIDv7 generation.

Next steps

Run scripts/ask-jev.ts for Round 1 to settle Q1 and Q2.
Review the updated decision-record.md for Round 2 dependencies.
Finalize the migration script once the frontier is empty.

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About this skill

Large design or architectural decisions often stall because of trivial implementation details. This skill implements a structured "grilling" process where an automated persona named JEV makes schema-validated decisions on your project frontier. It forces you to build a decision tree and resolve it in rounds, preventing decision paralysis.

What it does

Automated decisioning through a validated persona that accepts or rejects plan recommendations.
Tree-based planning that identifies question frontiers and manages complex decision dependencies.
Audit trail generation by recording session logs and generating a decision-record.md with full provenance.
Risk labeling for security, financial, and data privacy implications within the decision flow.

How it works

Map the frontier by identifying current design questions and their dependencies.
Present recommendations to the user in a structured format before execution.
Execute decision rounds by piping JSON to the ask-jev.ts script for validated answers.
Finalize results by compiling the decision record and requesting user sign-off before any action.

Frameworks & tools

This tool uses TypeScript scripts (ask-jev.ts, setup.ts) and JSONL for session persistence. It integrates with any LLM provider via a secure setup process that handles keys and profiles.

Why this beats prompting it yourself

Manual prompting lacks the strict dependency validation and schema enforcement provided by the scripts/ask-jev.ts utility. This skill ensures that no question is answered out of order and provides a permanent, auditable trail of why every design choice was made.

Use cases

Refine a complex software architecture by grilling a plan in rounds.
Delegate implementation choices to a defined project persona.
Generate an immutable audit log of design decisions for compliance.
Manage risky project changes with explicit risk labeling and sign-off.

Known limitations

Requires a one-time configuration via scripts/setup.ts. Decisions cannot be made if dependencies in the same round are unresolved. Output is restricted to allowlisted Markdown paths.

How to use

Skills on Agensi run inside your AI agent. Copy the prompt, paste it into your agent, and it connects to Agensi and installs this skill for you. If a payment is needed, your agent shows it to you first.