Individual skill
eval-config
Tune eval thresholds: dimension minimums, weights, auto-reject floors. "raise the hallucination threshold"
plugins/indranilbanerjee/digital-marketing-pro/skills/eval-config/SKILL.md
Source commit d780783eba4ecc7c8b9429f95c9362df95f07c61
What was checked
- Matches the observed repository HEAD
- Yes
- Scanner rules are current
- Yes
- Repository scan complete
- No
- Skill scan complete
- No
- Observed behavior
- Network or broad shell
- Last checked
- Nov 4, 2026
These are observations at the recorded commit, not a guarantee of safety. Review the source before running it.
Install this skill
Choose an agent in your Stack or copy a command below. Commands fetch the pinned commit and copy only this skill directory. Existing destinations are refused. Copying a command does not install anything.
An install command is unavailable: current rules, complete repository and skill scans, an eligible behavior level and a permitted license are required.
Repository context
- Repository stars (not skill ratings)
- 1,533
- Repository last updated
- Nov 6, 2026
- License
- Apache-2.0
Observed agent integration: No specific integration evidence. Standard SKILL.md installation does not imply verified compatibility.
- ai-coding Repository topic
- awesome Repository topic
- awesome-list Repository topic
- codex Repository topic
- codex-plugins Repository topic
- openai Repository topic
- codex-skill Repository topic
- codex-skills Repository topic
- marketplace Repository topic
- codex-marketplace Repository topic
- engineering Derived category
Files and excerpt
File metadata and permitted, redacted excerpts come from the recorded scan. Source links are pinned to that commit.
Browse files
Read excerpt
# /digital-marketing-pro:eval-config
> **Script location.** If your host does not set `${CLAUDE_PLUGIN_ROOT}`, the scripts are in this plugin's `scripts/` folder, next to `skills/`. Run them with Python 3.10 or newer: `python`, or `python3` where `python` is not found (macOS, many Linux systems).
## 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):