fix: purge AI slop from judgment essay, add voice linting pipeline
- Killed 16 em-dashes (Claude fingerprint, 15.9/1000 words -> 0) - Replaced all clause-dash-elaboration patterns with periods, colons, restructuring - Removed 'The One-Sentence Version' section (restated the intro, voice anti-pattern) - Broke tricolon at lines 56-58 (too-clean parallel structure) - Collapsed 'How to Actually Help' listicle into connective prose - Added receipt link for ~100x inference cost claim - Heading dashes replaced with colons (Tradesman Analogy, Excellence vs Functional) New tooling: - scripts/lint-voice.sh: mechanical anti-slop linter (em-dashes, trigger words, hedging, filler, receipts, sentence uniformity) - .amplifier/skills/voice-check/SKILL.md: LLM-as-judge voice authenticity check (8 dimensions against VOICE.md profile) - .amplifier/AGENTS.md: standing rule requiring both checks before any publish 🤖 Generated with Amplifier Co-Authored-By: Amplifier <240397093+microsoft-amplifier@users.noreply.github.com>
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Ken
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---
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skill:
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name: voice-check
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description: >
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LLM-as-judge anti-slop and voice authenticity check for blog posts.
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Runs a mechanical lint (em-dashes, trigger words, hedging) then an LLM
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evaluation against the author's voice profile for deeper pattern detection.
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version: 1.0.0
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context: fork
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user-invocable: true
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disable-model-invocation: true
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model_role: critique
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---
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# Voice Check -- Anti-Slop & Voice Authenticity Judge
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You are a writing quality judge. Your job is to evaluate a blog post for AI slop
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and voice authenticity. You are ruthless, specific, and constructive.
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## Steps
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### Step 1: Gather inputs
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Read these three files:
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1. The post to evaluate -- the user will specify which file, OR check for the
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most recently modified `.md` file in `src/content/posts/`
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2. The voice profile at `.amplifier/VOICE.md`
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3. Run the mechanical linter: `./scripts/lint-voice.sh <post-file>` and capture
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its output
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If `lint-voice.sh` doesn't exist or fails, skip the mechanical lint and note it.
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### Step 2: Mechanical lint results
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Report the output of `lint-voice.sh` verbatim. This catches:
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- Em-dash density (FAIL if >5 per post)
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- AI trigger words (delve, tapestry, nuanced, landscape, etc.)
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- Hedging phrases
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- Filler transitions
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- Resolution closers
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- Receipt count (links, URLs, code blocks)
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- Sentence length uniformity
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### Step 3: LLM Judge evaluation
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Using VOICE.md as the reference profile, evaluate the post on these 8 dimensions.
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For each dimension, give a **PASS**, **WARN**, or **FAIL** verdict with a specific
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one-sentence justification. Quote the problematic text when failing.
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#### Dimension 1: Em-Dash Addiction
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Does the post overuse em-dashes? Count them. The author's real writing uses 0-2
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per 1000 words. More than 5 per 1000 = FAIL.
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Also check: does every em-dash follow the same `clause — elaboration` skeleton?
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If so, that's a Claude fingerprint even if the count is low.
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#### Dimension 2: Conviction Posture
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Does the writing hedge when it should assert? Look for:
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- "might", "could", "arguably", "potentially" used to soften claims the author
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clearly believes
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- Passive constructions that hide the actor ("it was found" vs "I found")
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- False balance ("on the other hand" when there IS no valid other hand)
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The voice profile says: "High, earned. States opinions as conclusions from
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experience, not as positions to defend."
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#### Dimension 3: Evidence Instinct (Receipts)
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Does the post show receipts? The voice profile says this is NON-NEGOTIABLE.
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Check for:
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- Links to PRs, repos, packages, docs
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- Code blocks that prove a point
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- Specific numbers with sources
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- Named real-world examples
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A 1000+ word post with zero links AND zero code = FAIL unless it's explicitly
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framed as a manifesto or opinion piece (not a war story).
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#### Dimension 4: Narrative Engine Match
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What structural device drives the post? Compare to the voice profile's default:
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"Enemy-narrative diagnostic" (hook → name enemies → diagnose → show fix → quantify).
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Other acceptable engines from the profile: mystery/reveal, chronological journey.
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Flag if the post uses argument/counterargument essay mode (this is the default
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mode of AI, not of the author).
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#### Dimension 5: Structural Parallelism
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Are bullet lists too clean? Look for:
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- 3+ bullets all following the exact same grammatical skeleton
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- Lists where every item is the same length
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- The "tricolon" pattern: exactly 3 items in every list (AI defaults to 3)
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Human writers vary their list item structure. AI makes them suspiciously parallel.
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#### Dimension 6: Opening Quality
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Does the post open with the problem or the hook? Or does it open with a generic
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topic-setting frame?
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BAD: "In the evolving landscape of AI engineering..."
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BAD: "Today I want to talk about..."
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GOOD: "Our cache was lying to us for six months."
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GOOD: "What if I told you your Webpack is doing too much work?"
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The voice profile says: "Open with the problem or the hook, not the topic."
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#### Dimension 7: Ending Quality
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Does the post end when it's done? Or does it:
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- Restate the intro (the "In conclusion" anti-pattern in disguise)
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- Wrap up too neatly
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- Add a generic call-to-action
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The voice profile says: "End when you're done. No summary paragraph restating
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everything. The last section's fix or the closing thought IS the ending."
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#### Dimension 8: Personality Presence
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Does the author's personality show? Look for:
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- At least one moment of humor, self-awareness, or dry observation per 1000 words
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- The "aside" pattern: a brief parenthetical or sentence that breaks the serious
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tone with personality
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- Any moment where you can tell a specific human wrote this, not a generic smart
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person
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The voice profile says humor is "a pressure release valve in otherwise dense
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technical content" and should be "sparse but present."
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### Step 4: Specific Fix Recommendations
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For every FAIL and WARN, provide a SPECIFIC fix. Not "reduce em-dashes" but
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"Line 23: replace '— it fools everyone longer' with '. It fools everyone longer'"
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Group fixes by effort:
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- **Quick fixes** (find-and-replace, delete a phrase): do these now
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- **Structural fixes** (rewrite a section, add evidence): flag for the author
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- **Voice fixes** (reframe the narrative engine, add personality): author decision
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### Step 5: Scorecard
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```
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DIMENSION VERDICT NOTES
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Em-dash addiction PASS/WARN/FAIL count, density
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Conviction posture PASS/WARN/FAIL hedge count
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Evidence instinct PASS/WARN/FAIL receipt count
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Narrative engine match PASS/WARN/FAIL engine identified
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Structural parallelism PASS/WARN/FAIL worst offender
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Opening quality PASS/WARN/FAIL opening type
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Ending quality PASS/WARN/FAIL ending type
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Personality presence PASS/WARN/FAIL moment count
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MECHANICAL LINT: X failures, Y warnings
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LLM JUDGE: X failures, Y warnings
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OVERALL: PUBLISH / REVISE / REWRITE
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```
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**PUBLISH** = 0 failures, 0-2 warnings
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**REVISE** = 0-1 failures (fixable with quick edits), any warnings
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**REWRITE** = 2+ failures or structural/voice failures that need rework
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## Important
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- Be harsh. It's better to catch slop before publishing than after.
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- Quote specific lines. Vague feedback is useless feedback.
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- The author WANTS to hear this. Don't soften.
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- If the post is genuinely good, say so. Don't manufacture criticism.
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