joy-check

Solid

Validate content framing on joy-grievance spectrum.

AI & Automation 393 stars 36 forks Updated today MIT

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Quality Score: 95/100

Stars 20%
86
Recency 20%
100
Frontmatter 20%
70
Documentation 15%
100
Issue Health 10%
80
License 10%
100
Description 5%
100

Skill Content

# Joy Check Validate content framing using mode-specific rubrics. Two modes: - **writing** — Joy-grievance spectrum for human-facing content (blog posts, emails, articles). Evaluates whether content frames experiences through curiosity and generosity rather than grievance and accusation. - **instruction** — Positive framing validation for LLM-facing content (agents, skills, pipelines). Evaluates whether instructions tell the reader what to do rather than what to avoid (ADR-127). By default the skill evaluates each paragraph/instruction independently, produces a score (0-100), and suggests reframes without modifying content. Optional flags: `--fix` rewrites flagged items in place and re-verifies; `--strict` fails on any item below 60; `--mode writing|instruction` overrides auto-detection. This skill checks *framing*, not *topic* and not *voice*. Voice fidelity belongs to voice-validator, AI pattern detection belongs to anti-ai-editor. ## Reference Loading Table | Signal | Load These Files | Why | |---|---|---| | scoring instruction files (agents, skills, pipelines): positive-framing rubric | `instruction-rubric.md` | Loads detailed guidance from `instruction-rubric.md`. | | scoring human-facing prose (blog posts, emails, docs): joy-grievance rubric | `writing-rubric.md` | Loads detailed guidance from `writing-rubric.md`. | ## Instructions ### Phase 0: DETECT MODE **Goal**: Determine which rubric to apply based on file location or explicit flag. **Auto-detection rules...

Details

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

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