---
name: "evaluate-help"
description: "Overview of evaluate commands"
---
<!-- GENERATED by claude-power-pack - scripts/codex-skill-sync.py; edit .claude/commands/evaluate/help.md instead -->

## Codex harness adaptations

Generated from a Claude Code command. Where the procedure references these Claude-only surfaces, adapt as follows:

- MCP tools: use the MCP servers configured in `~/.codex/config.toml`, or fall back to the referenced repo scripts and CLI entry points.
- Helper scripts referenced as `scripts/<name>` are bundled under `scripts/` in this skill directory (byte-identical copies from the claude-power-pack checkout); some expect sibling repo resources, so prefer a full checkout when one is available.

# Evaluate Commands

Multi-model evaluation flow for structured decision-making and spec generation.

## Commands

| Command | Purpose |
|---------|---------|
| `/evaluate-issue` | Full 4-phase evaluation: divergence scan, reasoning, validation, spec output |
| `/evaluate-help` | This help page |

## How It Works

The evaluate flow uses multiple LLM models to analyze an issue or idea from different perspectives, then synthesizes the analysis into spec-ready artifacts.

```
Input (description + domain)
  ↓
Phase 1: Multi-Model Divergence Scan
  → Models analyze independently, surfaces disagreements
  → Human Checkpoint: select focus areas
  ↓
Phase 2: Sequential Reasoning (12-15 steps)
  → Structured problem → options → trade-offs → convergence
  → Human Checkpoint: accept, redirect, or skip
  ↓
Phase 3: Multi-Model Validation
  → Models validate the recommendation against original proposal
  → Human Checkpoint: choose output type
  ↓
Phase 4: Spec Output
  → Generates .specify/specs/{feature}/ (spec.md, plan.md, tasks.md)
```

## Domain Types

| Domain | Best For |
|--------|----------|
| `architecture` | System design, APIs, infrastructure, data models |
| `concept` | Ideas, workflows, business logic, feature proposals |
| `algorithm` | Performance, data structures, optimization |
| `ui-design` | UX flows, components, accessibility |
| `workflow` | CI/CD, automation, tooling, DX |

## Prerequisites

- **Required:** the Second Opinion MCP server, connected via the root `.mcp.json` (external `cooneycw/mcp-second-opinion` server; see `/second-opinion-help` for setup)
- **Optional:** Sequential Thinking MCP (enhances Phase 2 reasoning; falls back to inline reasoning if not installed)
- **Skill:** `evaluate` skill provides domain-specific prompts (loaded automatically)

## Quick Example

```bash
# Evaluate an architectural decision
/evaluate-issue "Should we use event sourcing or CRUD for the order system?"

# Evaluate a feature concept
/evaluate-issue "Add real-time collaboration to the document editor"

# No arguments - interactive prompts guide you
/evaluate-issue
```

## Output

Generates spec artifacts in `.specify/specs/{feature-name}/`:

```
.specify/specs/{feature-name}/
├── spec.md      ← Requirements, user stories, acceptance criteria
├── plan.md      ← Technical approach, architecture, risks
└── tasks.md     ← Actionable items organized in waves
```

Use `./scripts/speckit-tasks-to-issues.sh` to create GitHub issues from the generated tasks.

> Dependency: `scripts/speckit-tasks-to-issues.sh` calls `scripts/speckit-context.py` at runtime to render each issue's task-context block (#858). Both ship together: if the helper is missing or fails, the converter stops with an error before creating anything, because a packaging fault is not a context-free installation. `--no-context` is the explicit opt-out.


## Cost

Typical evaluation costs $0.10-0.30 depending on model selection and depth. Phase 1 and 3 use multi-model calls; Phase 2 uses sequential reasoning (no external LLM cost if using Sequential Thinking MCP).

## Related Commands

- `/second-opinion-start` - Quick single-file code review
- `/second-opinion-models` - Interactive model selection for reviews
- `/spec:adopt` - Install the official spec-kit for structured spec authoring
- `scripts/speckit-tasks-to-issues.sh` - Turn a spec `tasks.md` into GitHub issues