Filter Tells

filter-tells: AI Writing Detection and Correction

Detects mechanically, removes editorially, and hands voice restoration to match-voice. Three detection layers — lexical, structural, semantic — flag passages; Claude then rewrites them under the convergence rules below, and re-scans. There is no deterministic strip: a banned word is a one-word swap, but a cadence tell needs the sentence rewritten, and that is judgment.

Filtering leaves prose that is neutral — no tells, but no voice either. When the passage has to sound like the author, that is match-voice's job.

Renamed from de-ai (2026-07). The old name implied the skill only removed; it detects, and the removal is editorial.

Standing Warning: Scripts Are Blind to Rhetorical Patterns

The two scripts (detect-lexical.sh, detect-structural.py) measure surface metrics only — banned words, opening diversity, sentence length variance, dash density. They cannot detect the rhetorical patterns that constitute most of the AI signal in real prose:

• Declarative pairs ("X is Y. Z is W.")
• Definition-by-enumeration ("X extends in two ways")
• Meta-narrative bridges ("The analogy breaks in one place")
• Triple parallels ("clearer instructions, tighter constraints, fewer ambiguous cases")
• Comprehensive enumerated sweeps in parentheses

Partial exception: detect-structural.py now has a detect_antithesis check that catches the lexically-marked subset of negation-then-affirmation ("X is not Y. It is Z.", "The meter was.") and clipped antithesis fragments. It does not catch the purely semantic reversal ("Same quality out. Different bill." used without a negation word). Prompt 6 in Step 3 covers that remainder. Treat the regex as a recall aid, not full coverage of the pattern.

A clean or minor-issues verdict from the structural script means the surface checks passed. It does not mean the prose is in voice. Step 3 (semantic analysis by Opus) is the only layer that catches the rhetorical AI tells the scripts still miss. Skipping Step 3 produces false-negative reports.

The blindness runs in both directions. The scripts flag the bland AI direction (low burstiness, repetitive openings). Text iteratively rewritten against these very detectors overshoots into the opposite register — uniformly maximal polish, epigram-closing paragraphs, coined formulae, word salads — and passes every surface check while reading as obviously machine-made ("the LinkedIn voice"). The structural script now emits overshoot metrics (plain_sentence_rate, punch_clustering, salad_rate, repeated formulae, suspicious-overshoot verdict), but the judgment lives in Prompt 0 (cold read) and Prompt 7 (overshoot assessment). A minor-issues verdict on a document with prior filter-tells history deserves MORE suspicion, not less.

Do not infer voice quality from the structural script's verdict label. Voice is a judgment, not a statistic.

Long unattended runs on a local Ollama: set OLLAMA_WAIT_SERVER=600 — a supervised server can be reaped mid-run, and the transport then polls for its return instead of failing the paragraph (GH-173). Hosted (cohere:) models are unaffected.

When to Use

• Before publishing any document drafted or edited by AI
• When reviewing a file for AI writing tells
• When a document "feels" AI-written but you can't pinpoint why
• After AI-assisted writing sessions to clean the output
• To check for CoT (chain-of-thought) leakage in final text

Prerequisites

• Python 3.8+ available
• The target file is a markdown file
• The writing-style-guide.md is present in templates/

Procedure

Step 0: Cold Read (Model, BEFORE the scripts)

Run Prompt 0 from perplexity-prompts.md on the document before looking at any script output. The cold read answers what no metric can: could a plain reader follow this on one pass, and is the register appropriate for the venue? Record the COLD_VERDICT — it anchors nothing and is anchored by nothing.

Step 1: Lexical scan (no model)

bash <filter-tells>/scripts/detect-lexical.sh <file-or-dir> ...

Line-numbered matches by category — banned words, clichés, false emphasis, narrative-pivot frames, mechanical transitions. Instant and free. Accepts files or directories (*.md recursively).

Venue lexicons (GH-337). The catalog splits into core tells (always on: chat residue, AI clichés, mechanical transitions, narrative pivots, ornate register, filler measurement) and venue-keyed lists selected with --lexicon=NAME — newsletter (default; the full banned-word list), book and industry (share the newsletter word lists; their deltas are prose/structural rules living in tighten-style's hedge policy and the semantic pass, not word lists), academic (drops the newsletter banned words — "critical" and "fundamental" are ordinary methods-section vocabulary — keeps only the non-statistical false-emphasis adverbs, and adds paper-template tells like "novel framework" and "pave the way"), and none (core only). When the document has a venue profile (writing-voice/venues/, see the writing-voice rule), pass its tell_lexicon value; drive.py --lexicon threads it through the rewrite loop's re-scans via FILTER_TELLS_LEXICON.

Two categories are scored by density rather than per hit, because their words are legitimate individually and only the rate says anything. Ornate register flags above 4.0 per 500 words. Conversational filler — just, actually, really, basically, simply — is the register a rewrite lands in when it stops sounding corporate, and it is reported, not gated.

Both densities count occurrences, not matching lines. A markdown paragraph is one long line, so a line-based count scored three flourishes in a paragraph as one, and undercounted worst on the long paragraphs the measure is for (GH-242). The 4.0 ornate threshold survived that correction unchanged: across 193 documents in three corpora — this repository's rules and skills, and the reference writing-voice corpus — one file exceeds it, and that file is banned-patterns.md, which flags itself because it lists the patterns.

That asymmetry is a calibration result, not an oversight. The banned-word list holds leverage and robust, false emphasis holds crucially and notably, and filler was the one machine register with nothing in front of it — a rewrite that traded corporate vocabulary for chatty filler passed every check (GH-233). The obvious fix, a threshold, does not survive contact with the corpus:

Every value that catches the rewrites also condemns every essay in the anchor corpus, which is the register a punchy rewrite is steering toward. A level tells you which register a document is in; it cannot tell you the document got worse.

So filler is judged on movement against the document's own earlier rate:

python3 <agent-dir>/scripts/register_markers.py --compare <before> <after>

which reports filler/500w 0.3 -> 2.8 REGRESSED. Set FILLER_DENSITY_MAX to gate on an absolute level when you already know the register you want.

Two other categories need calibration rather than obedience. Marketing/hype vocabulary (venue-jargon) is human register flagged as venue-inappropriate jargon, not as an AI tell — and never flag quoted text or a source's own title. CoT candidates are patterns that may be scaffolding and may be ordinary prose; they do not fail the scan. Carry them to Step 3, where the removal test decides: delete the sentence and re-read the paragraph, and if nothing is lost it was scaffolding.

reading-scan-output.md lists the candidate patterns and what each metric means.

Step 2: Structural analysis (no model)

python3 <filter-tells>/scripts/detect-structural.py <file-or-dir> ...

Default threshold strict; --threshold=medium for drafts, relaxed for early notes. A verdict of likely-ai, suspicious, or suspicious-overshoot sends you to Step 3. See reading-scan-output.md for the metric signals, and opening-diversity-fixes.md when opening_diversity fires — it is the hardest to fix, since it means rewriting sentence openings across the document.

Voice distance — the positive check. The named detectors are a denylist: a tell must be known to be caught. Distance from a human corpus is the complement, catching deviations nobody has named yet.

python3 <match-structure>/scripts/style.py --db <db> corpus     # writes voice-profile.json
python3 <filter-tells>/scripts/detect-structural.py <files> --voice-profile=<db-dir>/voice-profile.json

A document that passes every named check but sits far from the corpus profile (any |z| ≥ 2) is NOT clean. Report "passes named checks; voice-distance high" and route to Step 3 with the deviating metrics and their direction as seeds. No corpus, no check — say so in one line and continue.

Burstiness (sentence-length dispersion). The structural metrics carry sentence_length_cv alongside the mean, stdev, min, and max, and the voice-distance block scores CV against the corpus. Only stdev is thresholded — CV is reported, because the flags above are calibrated on stdev and moving them is a separate decision. Quote CV when comparing documents or registers: stdev alone conflates dispersion with sentence length, so a paper and a newsletter differ on it without differing in uniformity.

Report CV before and after a rewrite pass, next to the Pangram score. On a plain stylometric model the two discriminative features are AI-phrase density and burstiness; this skill attacks the first, and the second only shows up if someone measures it. The canonical before/after number is style.py burstiness <draft.md> --baseline <before.md> --text — the CV here runs lower, since split_sentences() drops fragments under four words and those are the ones that widen the spread. Keep both sides of a comparison on one tool.

Step 3: Semantic Analysis (Requires Opus) — MANDATORY

If Pass 1 or Pass 2 found any issue, Step 3 is required, not optional. The scripts are blind to rhetorical patterns. Reporting a verdict without Step 3 is a procedural error and produces false negatives.

Step 3 may be skipped only when Pass 1 and Pass 2 both return zero matches and zero issues.

Load the prompts from perplexity-prompts.md and run against the target text:

Load the prompts from perplexity-prompts.md and the index of which does what — and which are mandatory when — from prompt-catalog.md.

Required output of Step 3

The final report from Step 3 must follow assets/report-template.md and must include:

1. The rewrite priority list from Prompt 5's output, structured by issue with line references.
2. Explicit confirmation that Step 3 was run (which prompts were applied, summary findings from each).
3. An honest plain-language verdict. Options: "in voice — no rewrite needed", "isolated issues — spot edits only", "pervasive rhetorical patterns — section rewrite needed", "heavy AI fingerprints — paragraph-by-paragraph rewrite needed", "over-corrected — needs plain-prose rewrite toward the venue register".

Freeform summaries without these elements are not a valid Step 3 output.

Step 4: Targeted Rewrite

For each flagged passage (in priority order from Prompt 5):

1. Load the rewrite instructions
2. Load the project's voice reference for this document type, if one exists — for example a .claude/rules/<voice>.md file the project provides (per-format voice guides are common: one for articles, one for long-form, etc.). If the project defines none, infer the target voice from existing published work in the same venue. Do not assume any specific file name.
3. For CoT leaks: remove the flagged sentence and re-read the paragraph. If no information is lost, the sentence is a true CoT leak — delete it. If information is lost, the sentence uses CoT-style wording on real content — reword to remove the scaffolding phrase while preserving the content.
4. Rewrite ONLY the flagged passage using the rewrite prompt template
5. Constraints: preserve meaning, match author voice, don't introduce new AI patterns
6. For a .tex source: apply the rewrite to the source file in place at the flagged line (the detex prose view is analysis-only, never an edit target). Preserve surrounding \citep/\ref/comments/math/macros; never reconstruct the file or round-trip through markdown. See "Editing LaTeX sources in place" in the rewrite instructions.

Step 5: Recursive Validation

After each rewrite:

1. Re-run detect-lexical.sh on the rewritten section
2. Re-run detect-structural.py on the rewritten section
3. If issues remain AND count decreased: iterate (max 3 total passes)
4. If issues remain AND count same or increased: STOP, flag for human review
5. If clean on both scripts: the rewrite has passed the surface check — proceed, but do not treat this as approval of the rewrite's rhetoric

The scripts confirm surface metrics only (banned words, opening diversity, length variance). They are blind to most rhetorical patterns — including ones a rewrite can introduce, such as parallelism that moves from sentence heads to tails. A green Step 5 is necessary, not sufficient. The rewrite is not accepted until it clears the independent Step 6 check below; Step 5 green is not that approval.

Step 6: Final Semantic Verification (independent evaluator)

The agent that wrote the rewrites cannot be the only judge of them. It shares the blind spots that produced them — it broke a mirror pair's heads and kept its tails, and its own re-read reads the result as fixed. This is the maker–checker gap: an auditor who watched the code get written blesses the blind spots that wrote it. So Step 6 runs as an independent evaluation, in a fresh context.

Run it as a separate session or agent that sees ONLY the final rewritten text and the Step 3 report — never the rewrite history, the reasoning, or the diffs. (In Claude Code, spawn a subagent for this; in any tool, at minimum start a clean context and do not carry the rewrite conversation into it. Portability: the requirement is a maker≠checker separation, not a specific mechanism.)

Give the evaluator this stance: assume the rewrites introduced new patterns. Its job is to find them, not to confirm the fix. Specifically:

• Enumerate every parallelism, mirror pair, or echo in the rewritten passages — checking both ends of each sentence, heads and tails, since a rewrite commonly relocates parallelism from the front to the back (run the Prompt 6 / antithesis and Prompt 3 Part-B enumerations on the rewritten spans specifically).
• Check for new patterns the rewrite introduced (banned words, mechanical transitions, CoT leaks).
• Did the rewrites sand off texture? Over-compression is its own tell (rewrite-instructions.md §3b): confirm dated/personal asides, first-person hedges, deliberate two-beat rhythms, and conversational gestures survived; that clipped fragments did not stack at adjacent paragraph or section boundaries; and that sentence_length_std held or rose. If the prose reads "too sleek," restore slack — a reverted over-edit is a fix, not a regression.
• Consistency between rewritten and preserved sections; overall flow.
• For a .tex source, verify against the edited .tex itself (analysis runs on its detex prose view); the in-place edits live in the source file, and untouched lines keep their numbers, so any still-flagged finding maps to source as before.

Anything the independent evaluator flags goes back through Step 4. A rewrite is accepted only when a checker that never saw it made pass it clean.

Voice injection (when the repository defines a target voice)

A writing repository may carry a writing-voice/ directory of exemplars — the contract, manifest schema, and author-voice/venue-voice roles are in this repository's writing-voice rule. When one is discoverable from the file under review, measure against it and steer rewrites toward it. Absent, all of this is skipped and behaviour is exactly as before.

The scripts live in match-structure, where exemplar retrieval belongs with the rest of the profile work.

python3 <match-structure>/scripts/voice_anchors.py discover <file>
python3 <match-structure>/scripts/voice_anchors.py profile --for <file>
python3 <filter-tells>/scripts/detect-structural.py <file> \
  --voice-profile=<repo>/writing-voice/.voice-profile.json
python3 <match-structure>/scripts/voice_anchors.py anchors --text <passage-file>|- --for <file> -k 3

With the profile passed in, voice_distance reports each metric as a distance from the author's own register rather than against fixed thresholds — the same mechanism a match-structure corpus profile uses, one flag with two sources. The profile caches in writing-voice/.voice-profile.json and rebuilds when a sample's mtime changes; --force overrides.

Anchors are the point of the feature. Before rewriting a flagged passage, retrieve the topically nearest exemplars (tf-idf over paragraphs, stdlib, preferring author-voice) and inject them into the rewrite prompt's {voice_anchors} slot — see rewrite-instructions.md. A rewrite with no target register aims only at not tripping detectors, and that is the documented cause of overshoot into uniform polish. Anchors give it a register that exists.

Pass the same anchors into Prompt 7. Drift toward heavier polish is an overshoot signal even when every surface check passes: the anchors make "too sleek" measurable against something concrete.

Author-baseline calibration (idiolect.yaml)

Some constructions the structural pass treats as AI tells are, at the right rate, this author's voice: the colon-verdict, the em-dash aside, the "X, not Y" antithesis. When writing-voice/idiolect.yaml is discoverable from the file under review (auto-discovery walks up from the first input; --voice-dir= overrides, --no-voice-calibration opts out), those constructions flag only above the author-calibrated ceiling — the marker's essay target plus the bank's ±30% tolerance — instead of on sight. The idiolect file's marker list drives the calibrated set; the generic flat thresholds stay in force for every other check and for every repository without an idiolect file, where behaviour is exactly as before.

Calibrated flags are distinguishable in the output: the issue carries "calibration": "author-ceiling" and its detail says reduce toward the target, not to zero with the allowed count for the document — so a rewrite pass steered by the flag trims the excess instead of eliminating the construction. A ceiling below the flat threshold never tightens the check; the flat threshold is the floor.

Migration note. The motivating counter-example is the Strategy Theatre antithesis 15→1 reduction: an uncalibrated antithesis flag steered a rewrite that cut fifteen instances down to one, and some of the fifteen were the author (the idiolect measures "X, not Y" at 2.29/1000 in the journal against 0.24 native — the construction is the voice, the excess is the tell). Ceilings come from idiolect.yaml, never hand-written here: re-measure the corpus upstream and this pass follows.

Scoped mode (GH-209): scan only what the pipeline changed

scripts/scoped_scan.py runs the lexical and structural scans over only the paragraphs the humanize chain's drivers report as changed — the pre-terminal recheck of humanize Phase 4b. Scope comes from the drivers' own artifacts (drive.py's changed_paragraphs manifest line, tighten.py's <out>.tighten.json sidecar, accent-dial's edit log) or an explicit --changed "1,3,7-9". It builds a scoped view, runs both detectors on it, and maps every view paragraph back to its source lines; --lexicon and --voice-dir pass through, and voice-dir discovery walks up from the ARTICLE path, not the temp view. Repairs stay editorial per Step 4, and they route through the rewrite transport. The scoped scan never gates author text: an untouched paragraph is out of scope by construction. An empty scope exits 2 with a message — nothing changed means nothing to scan.

Paragraph extraction (shared)

scripts/md_paragraphs.py is the canonical markdown paragraph extractor for the prose skills, and lives here because filter-tells is what the others already import from. It classifies every body line — prose, heading, figure, figure-caption, table, code, reference, blockquote, list, rule, blank — and returns prose blocks with the source line ranges a rewrite can be spliced back into. match-voice/scripts/drive.py imports it rather than carrying its own parser (GH-167); a sibling import works because every agent surface carries the full skill set.

python3 .claude/skills/filter-tells/scripts/md_paragraphs.py <file.md> [--json] [--coverage-only]

--coverage-only prints the classification tally, which is the answer to "did the driver skip paragraphs, or does the document just not have any there?"

Two splitters stay separate on purpose, each with the reason recorded at its definition: detect-structural.py runs on flattened text including detexed LaTeX and its boundaries are baked into the eval baseline, and match-structure/scripts/style.py chunks exemplars for comparison and never needs line fidelity.

External check (Pangram — optional, uploads the document)

Every detector above was written by reading model output and writing down what it does. That makes "the detectors stopped firing" a circular answer to "did the rewrite work?" — this skill grading its own homework. Pangram has never seen our denylist, so its result is evidence rather than an echo. It is the only outside measurement here, and the only step that sends text off the machine.

Consent first, every document — grantable in standing form. See the upload rule in the writing-voice/ directory rule: the operator can record a standing, revocable grant (writing-voice/pangram-consent.yaml, consent: standing; env PANGRAM_CONSENT overrides either way), under which the drivers score by default and --no-pangram opts a run out (GH-210). A key in the environment is still not consent. No grant and no per-document yes, or no key, means skip and say so — never substitute a local result for it.

python3 <agent-dir>/scripts/pangram.py --check                      # key + reachability, spends nothing
python3 <agent-dir>/scripts/pangram_report.py payload --article <file.md>   # prose-only payload + span map
python3 <agent-dir>/scripts/pangram.py --text <file>.payload.txt --json > before.json
python3 <agent-dir>/scripts/pangram_report.py report --response before.json --spans <file>.payload.spans.json

What this skill takes is a point-in-time reading. The before/after comparison belongs to match-voice, whose driver captures the baseline before it rewrites anything (drive.py --pangram) — the one moment it can still be captured. Both skills invoke the same two scripts from the shared root; neither owns them.

Only prose is submitted — the shared extractor drops code fences, tables, and front matter, which both keeps non-prose from skewing a prose detector and holds down cost, since billing counts started 1,000-word blocks. A full before/after comparison costs two scans. What an account allows is the account's business and no limit is enforced here, so check rather than remember: pangram.py --check confirms the key and the endpoint without spending a scan, and the API reports 402 when credits run out and 429 when requests arrive too fast.

Read the result as three document fractions (fraction_ai, fraction_ai_assisted, fraction_human) plus per-paragraph scores. There is no single AI score. And per the Verdict Validity Rules below, a favourable result never issues a clean verdict on its own.

Calibration (eval corpus)

The scripts are calibrated against the labeled corpus in eval/: python3 eval/run_eval.py reports per-detector fire rates on human vs ai classes and diffs against the committed baseline. Every new detector passes the gate there before merge (fires on an ai sample; fires on ≤20% of human samples; no existing human-class rate rises). Only the author populates the human class.

Threshold provenance lives in references/detector-thresholds.md: every numeric gate with its justification or an explicit "uncalibrated" marker, the boundaries between overlapping detectors, and the standing noise-audit results.

Failure taxonomy

references/failure-taxonomy.md maps every detector, prompt, and pattern class to its linguistic level (lexical / syntactic / rhetorical / discourse / pragmatic) and lists the known-empty territory. New tells are classified there before their detector is written.

Prevention (drafting time)

When the user is about to DRAFT — not repair — hand the model references/drafting-guidance.md as part of the drafting context instead of running this pipeline after the fact. It is the compact DO-form counterpart to banned-patterns.md: the tells are cheaper to prevent than to remove, and every confirmed failure class feeds both files (the pair rule in banned-patterns.md "Updating This List"). The detection pipeline still runs on the result; prevention lowers the pass count, it does not replace verification.

Abstract Mode

Abstracts are read ~100x more than the body and have the most rigid, best-documented structure — the easiest text to diagnose and repair. Trigger: the user asks to check or fix an abstract, or a publication-verdict run encounters one. Standards: references/abstract-standards.md.

Analyze (default):

1. Mechanical checks:

   python3 .claude/skills/filter-tells/scripts/abstract-check.py <paper.md> \
     --body <results.md> <other-chapters.md...> --limit 200
   

   Locates the abstract (LaTeX environment, front matter, heading, or whole file), counts words against the venue limit (default 200), runs the number-traceability check against the body, and sweeps self-containedness (citations, section/figure references).
2. Run Prompt 10 (move map: one row per sentence — move, conforming, action), plus Prompt 8 and Prompt 9 Part B on the abstract's sentences.
3. Report the move map and verdict: conforming | repairable | rebuild.

Fix (opt-in, only on a failing verdict): run Prompt 11 — rebuild move by move from body excerpts (written-last principle: intro -> moves 1-2, results numbers verbatim -> move 3, conclusion -> move 4; never reuse failed phrasing). Then verify: abstract-check.py traceability must pass (any number absent from the body rejects the rewrite), re-run Prompt 10 (max 3 passes), and run the standard scans for register regressions.

Convergence Rules

• Rewrites must never be validated by script metrics alone — that is how overshoot happens. After each rewrite pass, re-run Prompt 0 on the rewritten section; a rewrite that improves the metrics but worsens the cold read is a regression. An external detector score counts as a metric here: a falling fraction_ai alongside a worse cold read is still a regression.
• Maximum 3 rewrite iterations per passage
• If iteration N finds >= issues as iteration N-1, stop immediately
• Never rewrite direct quotations
• Never rewrite formal specifications or requirement statements
• When in doubt, flag for human rather than risk meaning loss

Verdict Validity Rules

• A "clean" or "in voice" verdict requires Step 3 to have been run and to have returned no high-priority issues. Verdicts issued without Step 3 are invalid.
• A clean or minor-issues label from the structural script is not a substitute for Step 3. The label is a surface-metric summary, not a voice assessment.
• An external detector result is not a verdict either. A Pangram Human Written, a low fraction_ai, or an empty flagged list does not certify a document and does not substitute for Step 3. It is one more input to the read, and it errs in both directions — a false negative on polished AI prose, a false positive on a terse human author writing in an unusual register. "Pangram says human, so it is clean" is the same procedural failure as anchoring on the structural script.
• "Looks mostly fine" / "largely in voice" / similar freeform softening language is not a valid verdict. Use one of the four plain-language options from Step 3's required output.
• If you anchor on the structural script's verdict and skip Step 3, the report is a procedural failure regardless of how the prose actually reads.

Model Selection

Use Opus for ALL passes. Rationale:

• Detection requires deep model self-awareness (simpler models can't recognize their own patterns)
• Rewriting requires preserving technical meaning while transforming style
• Validation must be strict enough to achieve convergence
• Cost per document is low (sections are small, 3 iterations max)

Reference Documents

• Banned patterns database
• CoT leakage patterns
• Opening diversity fixes
• Perplexity proxy prompts
• Rewrite instructions
• Report template

Quick Mode (Lexical + Structural Only)

For in-progress drafts where you want a fast surface-pass without burning model calls:

bash .claude/skills/filter-tells/scripts/detect-lexical.sh <file-or-dir>
python3 .claude/skills/filter-tells/scripts/detect-structural.py <file-or-dir> --json

Quick Mode is for working drafts. It is not valid for a publication verdict. Quick Mode catches the surface-detectable patterns. The rhetorical patterns that account for most of the AI signal are invisible to the scripts and require Step 3. Do not report a verdict based on Quick Mode output alone.

Working and specification documents are the exception that most needs more than Quick Mode: a system requirements document or design doc another session will execute is exactly where compressed-conversation phrases accumulate (undefined coinages, metaphors for mechanisms, editorializing adjectives). When the scripts surface coinage_candidates or editorializing hits on such a document, run Prompt 8 (whole-document, mandatory) and Prompt 8b before signing off — a spec that ships private vocabulary to an executor who was not in the conversation is a defect Quick Mode cannot rule on.