joy-check

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Validate content framing on joy-grievance spectrum.

AI & Automation 425 stars 46 forks Updated yesterday MIT

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Quality Score: 96/100

Stars 20%
88
Recency 20%
100
Frontmatter 20%
70
Documentation 15%
100
Issue Health 10%
80
License 10%
100
Description 5%
100

Skill Content

# Joy Check Two modes: - **writing** — Joy-grievance spectrum for human-facing content (blog posts, emails, articles). Evaluates curiosity/generosity vs. grievance/accusation framing. - **instruction** — Positive framing for LLM-facing content (agents, skills, pipelines). Evaluates "what to do" vs. "what to avoid" (ADR-127). Evaluates each paragraph/instruction independently, produces a score (0-100), suggests reframes without modifying content. Flags: `--fix` rewrites flagged items in place and re-verifies; `--strict` fails on any item below 60; `--mode writing|instruction` overrides auto-detection. Checks *framing*, not *topic* or *voice*. The writing workflow owns voice fidelity and AI-pattern detection. ## Reference Loading Table | Signal | Load These Files | Why | |---|---|---| | Scoring agents, skills, pipelines, or toolkit documentation | `references/instruction-rubric.md` | Positive-framing patterns, scoring, and examples. | | Scoring articles, emails, posts, or other human-facing prose | `references/writing-rubric.md` | Joy-grievance patterns, scoring, and examples. | ## Instructions ### Phase 0: DETECT MODE Auto-detection (priority order): 1. Explicit `--mode` flag → use that 2. `agents/*.md` → **instruction** 3. `skills/*/SKILL.md` → **instruction** 4. `skills/workflow/references/*.md` → **instruction** 5. `CLAUDE.md` or `README.md` → **instruction** 6. Everything else → **writing** Load `references/{mode}-rubric.md` for scoring criteria and examples. **...

Details

Author
notque
Repository
notque/vexjoy-agent
Created
6 months ago
Last Updated
yesterday
Language
Python
License
MIT

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