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

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/process/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.

GATE: Mode determined, rubric loaded.

Phase 1: PRE-FILTER

Regex scanning as a fast gate before LLM semantic analysis.

Writing mode:

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

Instruction mode:

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

Report findings with reframe suggestions from the rubric. If --fix, apply reframes and re-run.

GATE: Zero regex/grep hits. Resolve obvious patterns before Phase 2.

Phase 2: ANALYZE

Step 1: Read content

Read full file. Skip frontmatter and code blocks.

• Writing: Identify paragraphs (blank-line separated). Skip blockquotes.
• Instruction: Identify instructional statements — bullets, table cells, imperatives, headings. Skip examples, code blocks, quoted dialogue, file paths.

Step 2: Evaluate against rubric

Apply scoring dimensions from references/{mode}-rubric.md.

For writing: Joy-grievance lens. Watch for subtle patterns in references/writing-rubric.md (defensive disclaimers, accumulative grievance, passive-aggressive factuality, reluctant generosity).

For instruction: Positive-negative lens. Check against patterns table in references/instruction-rubric.md. 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 rubric's scoring scale. For items scoring CAUTION/GRIEVANCE (writing) or NEGATIVE-LEANING/PROHIBITION-HEAVY (instruction), draft specific reframe suggestions preserving substance.

If an item seems "too subtle to flag" — that is precisely when flagging matters. Subtle patterns are the primary purpose of this LLM phase.

GATE: All items scored. Reframe suggestions drafted for flagged items.

Phase 3: REPORT

Step 1: Calculate overall score

Average all item scores. Pass criteria:

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

Step 2: Output

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: Fix mode

If --fix:

1. Rewrite flagged items using drafted suggestions
2. Preserve substance — change only framing
3. Re-run Phase 2 on rewrites to verify
4. Maximum 3 iterations if fixes introduce new flags

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

Integration

Writing pipeline:

CONTENT --> writing workflow --> scan-ai-patterns --> joy-check --mode writing

Instruction pipeline:

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

Auto-invocation points:

• toolkit: after generating a new skill
• agent-upgrade: after modifying an agent
• writing: during validation
• doc-pipeline: for toolkit documentation

Invoke standalone via /joy-check [file] (auto-detects mode) or with explicit --mode.

Error Handling

Error: "File Not Found"

Verify path with ls -la. Use glob to search: Glob **/*.md. Confirm working directory.

Error: "Regex Scanner Fails or Not Found"

Verify scripts/scan-negative-framing.py exists. Requires Python 3.10+. If unavailable, skip to Phase 2 — the pre-filter is an optimization, not a requirement.

Error: "All Paragraphs Score GRIEVANCE"

Content is fundamentally grievance-framed. Report scores honestly. Suggest full rewrite with different framing premise, not paragraph-level fixes.

Error: "Fix Mode Fails After 3 Iterations"

Output best version with remaining concerns. Explain which rubric dimensions resist correction. The framing premise itself may need rethinking.

References

Rubric Files

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

Scripts

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

Complementary Skills

• writing — Voice, prose quality, and content validation
• toolkit — Skill creation and instruction validation