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 the private de-AI editor skill.

Reference Loading Table

Instructions

Phase 0: DETECT MODE

Goal: Determine which rubric to apply based on file location or explicit flag.

Auto-detection rules (in priority order):

1. Explicit --mode writing|instruction flag → use that mode
2. File in agents/*.md → instruction
3. File in skills/*/SKILL.md → instruction
4. File in skills/workflow/references/*.md → instruction
5. File is CLAUDE.md or README.md → instruction
6. Everything else → writing

Load the rubric: Read references/{mode}-rubric.md for the scoring criteria, patterns, and examples relevant to this mode.

GATE: Mode determined, rubric loaded. Proceed to Phase 1.

Phase 1: PRE-FILTER

Goal: Use regex scanning as a fast gate to catch obvious patterns before spending LLM tokens on semantic analysis.

For writing mode: Run the regex-based scanner for grievance patterns:

python3 ~/.claude/scripts/scan-negative-framing.py [file]

For instruction mode: Run a grep scan for prohibition patterns:

grep -nE 'NEVER|do NOT|must NOT|FORBIDDEN' [file]
grep -nE "^-?\s*Don't|^-?\s*Avoid|^#+.*Anti-[Pp]attern|^#+.*Avoid" [file]

Handle hits: Report findings with suggested reframes from the loaded rubric. If --fix mode is active, apply reframes and re-run to confirm clean.

GATE: Regex/grep scan returns zero hits. Resolve obvious patterns before proceeding to Phase 2 — mechanical fixes come first.

Phase 2: ANALYZE

Goal: Read the content and evaluate each item against the loaded rubric using LLM semantic understanding.

Step 1: Read the content

Read the full file. Skip frontmatter (YAML between --- markers) and code blocks.

• Writing mode: Identify paragraph boundaries (blank-line separated blocks). Skip blockquotes.
• Instruction mode: Identify each instructional statement — bullet points, table cells, imperative sentences, section headings. Skip examples, code blocks, quoted user dialogue, and file path references.

Step 2: Evaluate against the rubric

Apply the scoring dimensions from the loaded rubric (references/{mode}-rubric.md). Each rubric defines its own PASS/FAIL dimensions, subtle patterns to detect, and contextual exceptions.

For writing mode: Evaluate through the joy-grievance lens. Watch for the subtle patterns described in references/writing-rubric.md (defensive disclaimers, accumulative grievance, passive-aggressive factuality, reluctant generosity).

For instruction mode: Evaluate through the positive-negative lens. Check each instruction against the patterns table in references/instruction-rubric.md. Apply contextual exceptions — subordinate negatives attached to positive instructions are PASS, as are negatives in code examples, writing samples, and technical terms.

Step 3: Score each item

Apply the scoring scale from the loaded rubric. For any item scoring in the lower tiers (CAUTION/GRIEVANCE for writing, NEGATIVE-LEANING/PROHIBITION-HEAVY for instruction), draft a specific reframe suggestion that preserves the substance while shifting the framing.

When an item seems subtle enough to question flagging — that is precisely when flagging matters most. Subtle patterns are what the regex/grep pre-filter misses, making them the primary purpose of this LLM analysis phase.

GATE: All items analyzed and scored. Reframe suggestions drafted for all flagged items. Proceed to Phase 3.

Phase 3: REPORT

Goal: Produce a structured report with scores, findings, and reframe suggestions.

Step 1: Calculate overall score

Average all item scores. Pass criteria come from the loaded rubric:

• Writing mode: Score >= 60 AND no GRIEVANCE paragraphs
• Instruction mode: Score = 100 AND zero primary negative patterns in instructional context

Step 2: Output the report

JOY CHECK: [file]
Mode: [writing|instruction]
Score: [0-100]
Status: PASS / FAIL

Items:
  [writing mode]
  P1 (L10-12): JOY [85] -- explorer framing, curiosity
  P3 (L18-22): CAUTION [40] -- "confused" leans defensive
    -> Reframe: Focus on what you learned from the confusion

  [instruction mode]
  L33: NEGATIVE [20] -- "NEVER edit code directly"
    -> Rewrite: "Route all code modifications to domain agents"
  L45: PASS [90] -- "Create feature branches for all changes"
  L78: PASS [85] -- "Credentials stay in .env files, never in code" (subordinate negative OK)

Overall: [summary of framing arc]

Step 3: Handle fix mode

If --fix mode is active:

1. Rewrite flagged items using the drafted reframe suggestions
2. Preserve the substance — change only the framing
3. Re-run Phase 2 analysis on rewritten items to verify fixes landed
4. If fixes introduce new flagged items, iterate (maximum 3 attempts)

GATE: Report produced. If --fix, all rewrites applied and re-verified. Joy check complete.

Integration

This skill integrates with content and toolkit pipelines:

Writing pipeline (human-facing content):

CONTENT --> voice-validator --> scan-ai-patterns --> joy-check --mode writing --> de-AI edit (private skill)

Instruction pipeline (agent/skill/pipeline creation and modification):

SKILL.md --> joy-check --mode instruction --> fix flagged patterns --> re-verify

Auto-invocation points:

• skill-creator pipeline: Call the Skill tool with joy-check. Use instruction mode after generating a new skill.
• agent-upgrade pipeline: Call the Skill tool with joy-check. Use instruction mode after modifying an agent.
• voice-writer: Call the Skill tool with joy-check. Use writing mode during validation.
• doc-pipeline: Call the Skill tool with joy-check. Use instruction mode for toolkit documentation.

The joy-check can be invoked standalone via /joy-check [file] (auto-detects mode) or with explicit --mode writing|instruction.

Error Handling

Error: "File Not Found"

Cause: Path incorrect or file does not exist Solution:

1. Verify path with ls -la [path]
2. Use glob pattern to search: Glob **/*.md
3. Confirm correct working directory

Error: "Regex Scanner Fails or Not Found"

Cause: scan-negative-framing.py script missing or Python error Solution:

1. Verify script exists: ls scripts/scan-negative-framing.py
2. Check Python version: python3 --version (requires 3.10+)
3. If script unavailable, skip Phase 1 and proceed directly to Phase 2 LLM analysis -- the regex pre-filter is an optimization, not a requirement

Error: "All Paragraphs Score GRIEVANCE"

Cause: Content is fundamentally framed through grievance -- not recoverable with paragraph-level reframes Solution:

1. Report the scores honestly
2. Suggest the content needs a full rewrite with a different framing premise, not paragraph-level fixes
3. Point the user to the Joy Principle section and Examples for guidance on the target framing

Error: "Fix Mode Fails After 3 Iterations"

Cause: Rewritten paragraphs keep introducing new CAUTION/GRIEVANCE patterns, often because the underlying premise is grievance-based Solution:

1. Output the best version achieved with flagged remaining concerns
2. Explain which specific rubric dimensions resist correction
3. Suggest the framing premise itself may need rethinking, not just the language

References

Rubric Files

• references/writing-rubric.md — Joy-grievance spectrum, subtle patterns, scoring, examples (writing mode)
• references/instruction-rubric.md — Positive framing rules, patterns to flag, rewrite strategies, examples (instruction mode)

Scripts

• scan-negative-framing.py — Regex pre-filter for grievance patterns (writing mode, Phase 1)

Complementary Skills

• voice-validator — Voice fidelity validation (different concern)
• private de-AI editor skill — AI pattern detection and removal (different concern; installed from ~/private-skills)
• voice-writer — Content pipeline that invokes joy-check as a validation phase
• skill-creator — Skill creation pipeline that invokes joy-check in instruction mode