/eval-config

name: eval-config
description: "View and tune the brand's content evaluation settings — per-dimension minimum score thresholds, composite weight distribution, auto-reject floors, and content-type overrides — with validation, before/after scoring comparisons, and industry-based recommendations. Outputs an updated, internally consistent eval configuration. Triggers on \"/digital-marketing-pro:eval-config\", \"raise the hallucination threshold\", \"why did this draft auto-reject\", \"recommend eval settings for healthcare\", \"reset eval scoring to defaults\". Reads the brand profile and guidelines, writes via eval-config-manager.py, and pairs with /digital-marketing-pro:eval-content to see the new bar in action."

/digital-marketing-pro:eval-config

Purpose

Configure the evaluation system for a brand. Set minimum quality thresholds per dimension, adjust scoring weights based on industry priorities and content strategy, configure auto-reject thresholds that prevent substandard content from passing evaluation, and define content-type-specific quality standards that apply different bars to different formats.

The eval config determines how strictly content is scored and what the quality bar looks like for the brand. A healthcare company may weight hallucination risk and claim verification heavily while relaxing readability thresholds for technical audiences. A consumer brand may prioritize brand voice and readability while accepting lighter claim verification for awareness content. An agency managing multiple brands can set different configs per brand. This command makes those trade-offs explicit and adjustable rather than buried in defaults.

Input Required

The user must provide (or will be prompted for):

• **Configuration action**: What to do — `view` (show current settings via `get-config`), `set-threshold` (change a minimum score for a dimension; add `--content-type` to scope the override to one content type), `set-weights` (change dimension weight distribution; add `--content-type` for a per-type override), `set-auto-reject` (change the composite score below which content automatically fails), `recommend` (analysis only — get industry-appropriate settings suggestions), or `reset` (restore all settings to defaults). There is no separate `set-content-type` action — content-type overrides are applied by passing `--content-type` to `set-threshold` / `set-weights`.
• **Dimension name** (for set-threshold): The dimension to configure — `content_quality`, `brand_voice`, `hallucination_risk`, `claim_verification`, `output_structure`, `readability`, or `composite`
• **Threshold value** (for set-threshold): The minimum acceptable score (0-100) for the specified dimension. Content scoring below this threshold on any dimension is flagged as a failure on that dimension
• **Weights** (for set-weights): A JSON object mapping dimension names to their weights — e.g., `{"content_quality": 0.25, "brand_voice": 0.20, "hallucination_risk": 0.20, "claim_verification": 0.15, "output_structure": 0.10, "readability": 0.10}`. Weights must sum to approximately 1.0 (tolerance of +/- 0.02 for rounding)
• **Auto-reject score** (for set-auto-reject): The composite score below which content automatically fails regardless of individual dimension scores — typically 40-60 depending on brand standards
• **Content type** (optional, for set-threshold / set-weights via `--content-type`): The content type to configure overrides for, plus the overrides themselves — custom thresholds or weights that apply only to that content type

Process

1. **Load brand context**: Read `~/.claude-marketing/brands/_active-brand.json` for the active slug, then load `~/.claude-marketing/brands/{slug}/profile.json`. Apply industry context for recommendation generation — different industries have different quality priorities. Also check for guidelines at `~/.claude-marketing/brands/{slug}/guidelines/_manifest.json` — if present, note any quality requirements defined in guidelines that should inform threshold recommendations. Check for agency SOPs at `~/.claude-marketing/sops/`. If no brand exists, ask: "Set up a brand first (/digital-marketing-pro:brand-setup)?" — or proceed with defaults. 2. **Get current configuration**: Execute `python "${CLAUDE_PLUGIN_ROOT}/scripts/eval-config-manager.py" --brand {slug} --action get-config` to retrieve all current settings — global thresholds, dimension weights, auto-reject threshold, and any content-type-specific overrides. Identify which settings are custom (set by the user) and which are defaults. 3. **Present current settings**: Display all configuration in a clear, readable format:

• **Global thresholds**: Each dimension's minimum score with its current value and whether it is custom or default
• **Dimension weights**: Each dimension's weight in the composite score calculation, shown as both decimal and percentage, with a visual indicator of relative importance
• **Auto-reject threshold**: The composite score floor with its current value
• **Content-type overrides**: Any content types with custom settings, showing how they differ from the global config
• **Effective scoring example**: Show what a hypothetical evaluation would look like under the current config — e.g., "With these weights, a piece scoring 90 on content quality but 50 on hallucination risk would get a composite of X"

4. **Process configuration changes**: Based on the requested action:

• **set-threshold**: Validate the threshold value is between 0 and 100. Execute `python "${CLAUDE_PLUGIN_ROOT}/scripts/eval-config-manager.py" --brand {slug} --action set-threshold --dimension {dimension} --threshold {value}` (add `--content-type {type}` to scope it to one content type). Show before/after comparison with the impact on scoring strictness
• **set-weights**: Validate all weights are between 0 and 1 and sum to approximately 1.0. If they do not sum correctly, show the discrepancy and offer to normalize. Ex