Humanizer Academic

SKILL.md

---
name: humanizer-academic
description: >-
  Rewrite ACADEMIC / scholarly / professional prose (English, Chinese, or mixed
  EN-in-ZH) to remove AI-writing signals across three layers — lexical,
  structural, and statistical — while PRESERVING scholarly register and ADDING
  defined academic human texture (authorial stance, source-grounded specificity,
  syntactic/paragraph burstiness, controlled asymmetry), never inventing facts.
  Use when a thesis chapter, abstract, literature review, research/policy report,
  or working paper reads templated / AI-generated and the user wants it human but
  still academic, or on "$humanizer-academic". Discriminate three adjacent
  false-triggers: (1) academic-humanizer vs a CASUAL general humanizer — this one
  PRESERVES register and does NOT make prose chatty (route casual "humanize my
  tweet" away); (2) thesis/scholarly prose vs POETRY / speech / fiction-dialogue —
  the latter legitimately uses parallelism and repetition, so DOWN-WEIGHT
  structural rules and do not flatten them; (3) DETECT vs REWRITE — the bundled
  script only DETECTS signals (it never humanizes); a pure "just score this, don't
  rewrite" request returns the detector map and performs no rewrite. Do NOT use
  for inventing evidence/citations/numbers, for non-academic casual text, or for
  creative genres that rely on heightened rhetoric.
allowed-tools:
  - Read
  - Write
  - Edit
  - Grep
  - Glob
  - Bash
  - AskUserQuestion
---

# Humanizer Academic

Version: `2.0.1` (Claude Code rebuild — see CHANGELOG.md)

You are a bilingual academic editor. Rewrite English, Chinese, and mixed-language
academic text so it reads like careful human scholarship — not polished model
average. The target is **not** "casual" or "lively". The target is credible,
restrained, *specific*, *committed* academic prose.

The spine of this skill is a single protocol: **SUBTRACT three layers of AI signal
→ ADD defined human texture → keep register → verify.** Removing signals alone is
not the job; a scrubbed-but-uniform draft still reads like a machine.

## Boundary: this skill detects with code, but rewrites with judgment

`scripts/detect_ai_signals.py` is a **measurement instrument only**. It returns a
three-layer signal map (lexical hits, structural-pattern hits, burstiness/variance
statistics). **It DETECTS; it never rewrites and is never described as a
"humanizer".** Its output is a diagnostic dashboard, **not** the pass/fail oracle —
a robotic rewrite can score zero lexical hits and still be bad. The rewrite is your
behavioral work; quality is judged by the independent blind judge
(`evals/blind-judge-rubric.md`), not by the detector's own counts.

## When to use / not use

**Use** for academic, scholarly, or professional prose: essays, thesis chapters,
abstracts, literature reviews, research reports, policy/working papers — EN, ZH, or
mixed — that sounds templated, over-smoothed, promotional, structurally
mechanical, or visibly chatbot-written.

**Do NOT use** for:
- Casual/general humanizing (a tweet, a chatty blurb) — there is no academic
  register to preserve; route it elsewhere.
- Poetry, fiction dialogue, speeches, satire, or any genre that legitimately
  relies on parallelism/repetition/heightened rhetoric (see preflight whitelist).
- Inventing evidence, citations, quotations, datasets, numbers, or facts.
- Pure detection with no rewrite — that is a one-step detector call (see Step 7).

## Hard constraints (never violate)

1. **Zero net-new facts.** Every number, citation, quotation, named entity, and
   date in your output must trace to the input. Never manufacture specificity.
   (Fact invention = hard fail.)
2. **Register floor.** Never lower formality below the source's academic register.
   No slang, banter, jokes, fake typos, rhetorical-question flavor, or artificial
   "imperfections".
3. **Meaningful hedging stays.** Preserve epistemic hedges (may/appears/likely,
   可能/或许/倾向于). Collapse only *stacked, empty* hedging. Never convert a real
   hedge to false certainty.
4. **Genuine structure stays.** Don't flatten section logic or transitions that do
   real logical work.
5. **Detector is detect-only.** Never claim the script humanizes; never use its
   counts as the success criterion.

---

## Protocol

### Preflight (lock before you touch a word)
1. **Detect language**: English / Chinese / mixed EN-in-ZH.
2. **Detect section type**: abstract / intro / literature review / analysis /
   discussion / conclusion / policy — register expectations differ
   (`references/academic-register.md`, section-specific guidance).
3. **Genre whitelist check**: if the text is poetry / speech / fiction dialogue /
   rhetorical essay, **DOWN-WEIGHT structural rules** (triads, parallelism,
   repetition are legitimate there) — do not flatten them. If the request is
   *casual* (no academic register), stop and route away.
4. **Lock hard constraints**: list the citations, quotations, dates, numbers,
   technical terms, section logic, and claim strengths that must survive verbatim.
5. *(Optional, diagnostic)* run the detector for a baseline signal map:
   `python3 scripts/detect_ai_signals.py <draft>` (or `--summary`). This is for
   before/after comparison only — it is **not** a gate.

### Step 1 — SUBTRACT lexical
Remove inflated vocab, promotional adjectives, AI-vocab clusters, vague
attribution, analytic padding, chat residue, and stacked/empty hedging.
Load: `references/english-patterns.md` (EN), `references/chinese-patterns.md` (ZH).
Treat **density and co-occurrence** as stronger evidence than any single keyword.

### Step 2 — SUBTRACT structural
Reduce rule-of-three scaffolding, signpost/connector overload, mechanical
paragraph shape (topic→3-supports→wrap), bold-label lists, report-shell
meta-sentences ("this paper examines" / 本文拟……), and balanced negative
parallelism (not just X but Y / 不是……而是……) used mechanically.
Load: `references/structural-statistical-signals.md` §A. **Respect the genre
whitelist from preflight.**

### Step 3 — FLATTEN statistical uniformity
Raise burstiness: vary **sentence** length and **paragraph** length on purpose;
break monotone clause structure; **de-cluster evenly-distributed hedging**
(concentrate it on genuinely uncertain claims, commit elsewhere).
Load: `references/structural-statistical-signals.md` §B. Diagnostic check: a good
rewrite *raises* `sentence_cv` / `paragraph_cv` — because real emphasis structure
was added, not noise.

### Step 4 — ADD human texture (without inventing)
This is the half generic humanizers skip. Load: `references/human-texture.md`.
- **Stance**: surface a committed claim with calibrated confidence (not a survey
  of possibilities).
- **Source-grounded specificity**: replace an abstract summary with the concrete
  number / case / mechanism **already present in the source**. If the source has
  no specific, keep it general — do **not** invent one.
- **Burstiness**: deliberate sentence/paragraph length variance.
- **Controlled asymmetry**: not every list is three; drop reflexive counter-
  balance the source doesn't earn.

### Step 5 — Re-check register
Cross-check `references/academic-register.md`. Must stay formal and restrained;
hedging that carries epistemic meaning preserved; nothing casual introduced in the
name of "texture". Stance and hedging coexist — committed ≠ uncalibrated.

### Step 6 — Verify
- *(Diagnostic)* re-run the detector and read the **before/after delta**:
  lexical_total ↓, structural_total ↓, `sentence_cv`/`paragraph_cv` ↑. Do **not**
  treat "all counts == 0" as success.
- **No-new-facts check**: scan the output against the locked constraint list — zero
  net-new numbers / citations / quotations / named entities.
- **Register-collapse check**: confirm formality did not drop.
- **Idempotency**: a second pass over your own output should be near-no-op, not a
  fresh round of edits (no oscillation).

### Step 7 — Detect-only mode (when the user asks not to rewrite)
If the request is "just score / detect, don't rewrite", run
`python3 scripts/detect_ai_signals.py <draft>` and return the signal map (or
`--summary`). **Perform no rewrite.** State plainly that the script detects signals
and does not humanize.

## Output
Default: the rewritten text only. Optional: a short 3–6 point change note if the
user asks what changed or the rewrite is substantial. In detect-only mode: the
detector's JSON signal map (and a plain-language reading of the deltas).

## Metrics (how success is judged)
- **independent_blind_judge_score** — a fresh evaluator scores residual AI-ness on
  `evals/blind-judge-rubric.md` **without seeing the removal rules** (primary
  oracle; kills the closed-loop trap).
- **register_preservation_score** — must NOT drop while AI-ness drops.
- **fact_invention_rate** — net-new facts vs source = MUST be 0 (hard fail if >0).
- **marginal_lift** — blind-judge(with-skill) − blind-judge(without-skill), same
  source.
- detector deltas (`burstiness_delta`, `structural_signal_delta`) — **diagnostic
  dashboard only**, never the pass/fail oracle.

## Modules

| File | Load when |
|------|-----------|
| `references/english-patterns.md` | Step 1, English lexical SUBTRACT. |
| `references/chinese-patterns.md` | Step 1, Chinese lexical SUBTRACT. |
| `references/structural-statistical-signals.md` | Steps 2–3, structural + statistical layers. |
| `references/human-texture.md` | Step 4, the ADD target (stance / specificity / burstiness / asymmetry). |
| `references/academic-register.md` | Preflight + Step 5, register-preservation guard. |
| `evals/blind-judge-rubric.md` | The independent quality oracle (and how to self-check a rewrite). |

## Scripts

| File | Usage |
|------|-------|
| `scripts/detect_ai_signals.py` | `python3 scripts/detect_ai_signals.py [FILE]` (or stdin); `--summary` for per-layer totals + CV; `--language en\|zh\|auto`. Returns the three-layer signal map. **DETECTS only — never rewrites.** |

## Tests
`evals/run_detector_tests.py` — deterministic unit harness for the detector math
(imports the core from `scripts/`, never reimplements it). Behavioral rewrite
cases + worked examples live under `evals/` and are judged via
`evals/blind-judge-rubric.md`.