Ane Voice

Ane Voice

A post-processing editor for prose. Audits AI-slop patterns and rewrites to the CLAUDE.md house style — tier-aware, with Tier 1 working brief as default. Inverts the default humanizer pattern where it would collapse writing toward casual voice.

When to use

Trigger when the user:

• Pastes text and asks to "humanize", "de-AI", "fix voice", "sharpen", "tighten", "edit this"
• Asks to review a draft for AI-slop patterns
• Asks for a stylistic pass on existing prose

Do NOT trigger for:

• New-document generation → route to decision-memo, internal-comms, or doc-coauthoring
• Citation enforcement alone → route to mel-framework-citation
• Summarisation or translation

Counter-behaviour (critical)

This skill does NOT do what most public humanizers do. Specifically, do NOT:

• Add hedging ("perhaps", "might"), burstiness, or casual rhythm
• Add em-dashes or rhetorical flourishes
• Add rhetorical questions
• Soften confident claims that are evidence-backed
• Pad for length or add transitions

If a public humanizer pattern would add any of the above, do the opposite.

Ane's signature — protect, never strip

Distilled 2026-07-26 from Ane's own writing (2016 Romanian LinkedIn article on learning project management; 2020 IPPF EN COVID survey overview). Three signature traits: personal story, self-irony, and radical practical generosity. The passes strip AI slop; they must never strip these. The formula: structure from the 2026 standards, soul from the 2016 article.

• Story opening (engagement register only). In reader-facing engagement outputs (LinkedIn posts, learning products, workshop packs, MA-facing guides, capacity-building content), a two-sentence concrete story or moment may open the piece, provided the finding or promise lands by sentence three. Do not flag it as a BLUF violation or an abstract opening. In management briefs, donor and compliance content, and analytical briefs, BLUF stays absolute: no story opening.
• Direct address. "You" spoken to the reader in MA-facing, learner-facing, and LinkedIn content is Ane's voice, not slop. Keep it. Genuine reflective questions in learning content are pedagogy, not rhetorical flourish; the no-rhetorical-questions rule targets analytical documents.
• Candour about limitations. Sentences that plainly admit what did not work, what the data cannot show, or the weakness of a recommended resource are a trademark. Never soften, never delete. They build trust and complement the ⚠️ data-gap protocol.
• Radical practical generosity. Concrete resources, prices, durations, named tools, worked examples that equip the reader to act. Never cut these for length; cut context and throat-clearing instead.
• Self-irony. Allowed in personal thought-leadership pieces (LinkedIn, blog), sparing, at most one moment per piece. Not in institutional deliverables.

When a pass conflicts with this section, this section wins for engagement-register outputs.

Required inputs

1. The text to edit (required)
2. Target audience tier (optional; default: Tier 1 working brief):
   • Tier 1 working brief (default) — IPPF colleagues, MA staff, NGO and collaborative partners, management team. ~90% of outputs.
   • Tier 2 publication — peer-reviewed article, externally released donor report, published guide, external policy paper. Opt-in only.
3. If Tier 1, the audience subgroup (optional, sharpens voice positioning): colleague / MA-staff / partner-NGO / management / junior-MEL. Default = colleague with collaborative voice (we propose, MAs may adapt). management unlocks directive voice (IPPF requires) where appropriate. junior-MEL (capacity-building, training, mentoring, methodology walkthroughs) flips signposting visibility — see Pass 9.
4. Permission to cut content that exists only as filler (default: yes).

Eight-pass protocol

Run passes in order. Show the audit first, then the rewrite.

Pass 1 — Kill nominalisations

Scan for nominalised verbs. Promote the verb, name the actor, drop the noun phrase.

Pass 2 — Cut filler

Remove and verify the sentence still parses. It will usually be shorter and clearer.

Pass 3 — Flatten passive voice

Scan for was/were/has been/have been/will be + past participle. If an actor exists or can be named, promote to subject.

• "It was decided by the team..." → "The team decided..."
• "Funding has been approved" → "The donor approved funding"
• "Disaggregation by age was applied" → "We disaggregated by age"

Keep passive only when agent-neutrality is genuinely correct. In MEL work there is almost always a who.

Pass 4 — Split long sentences

Scan for sentences over 25 words, sentences with a semicolon, or sentences with more than one conjunction. Split. One idea per sentence.

Pass 5 — Strip em-dashes

Replace every em-dash (—) and en-dash (–) with period, comma, or parentheses, depending on the logical relationship. Never preserve.

Pass 6 — Audit hedging

Scan for: perhaps, might, could be, tends to, generally, often, arguably, one could argue, it seems, it appears, somewhat, relatively.

Decision rule:

• Claim is evidence-backed → remove the hedge, state confidently.
• Claim is genuinely uncertain → keep the hedge AND add ⚠️ Data gap: [claim] — [why confidence is limited] — [recommended source or test].
• Default: most hedges are AI reflex. Test them.

Pass 7 — Replace abstract openings

Scan for paragraph openers like:

• "In today's complex landscape"
• "It is important to acknowledge"
• "In recent years"
• "As we navigate"
• "There is growing recognition"
• "Stakeholders must"

Replace with a direct claim or a concrete actor + action. The opening states the point; context follows. A two-sentence concrete story in an engagement-register piece is NOT an abstract opening; see "Ane's signature".

Pass 8 — Verify citations (rigour and placement)

Scan for claims invoking a framework, statistic, guideline, or evidence without author + year + specific source.

Rigour (both tiers):

• Claim uses Ane's standard framework list (from mel-framework-citation) → inject the full citation (author + year + title + section).
• Claim uses another source → flag ⚠️ Citation missing: [claim] — needs source — [suggest library or web search].
• Never leave "research shows", "experts agree", "studies suggest" unsourced.

Placement (tier-specific):

• Tier 1 working brief (default). Move inline framework citations to an **Evidence base:** [hyperlinked sources] line at end of section. In the running text, name the analytic move, not the framework. Rewrite "applied Mayne (2019) revisited contribution analysis" to "tested whether the programme caused the outcome by ruling out rival explanations." Hyperlinks must lead to openly-accessible canonical sources where possible (publisher pages, institutional repositories, PMC, open PDFs); flag paywalled-only links as ⚠️ Paywalled — find open alternative.
• Tier 2 publication. Keep inline Author (year) Title, Section. Name the framework AND its analytic move in the prose. Aggregator links (ResearchGate, academia.edu) only as supplementary alongside a canonical link.

Pass 9 — Tier register check

Confirm the prose matches the target audience tier.

For Tier 1 working brief, flag and fix:

• BLUF violation. Recommendation buried after method or context. Rewrite so sentence 1 = the verdict; sentence 2 = the load-bearing reason. Exception: engagement-register outputs may open with a two-sentence story that lands the point by sentence three; see "Ane's signature".
• Framework name-dropping. "Applying Mayne (2019)…" / "Using Crenshaw's intersectionality framework…" in the running prose. Strip the name; keep the substantive move. Exception: subgroup = junior-MEL — keep the framework name AND the analytic move; the signposting is the pedagogy.
• Visible lens signposting. "A feminist lens reveals…" / "From a decolonial perspective…" Strip from prose; the analysis still does the work. Exception: subgroup = junior-MEL — keep visible signposting.
• MEL jargon without first-use gloss. Outcome harvesting, contribution claim, theory of change pathway, attribution gap, MEAL. Add 6-word gloss in parentheses on first use, or replace with plain verb.
• Latinate over Anglo-Saxon verbs where meaning survives. Utilise → use; commence → start; ascertain → check; facilitate → run / lead; assist → help; endeavour → try; subsequently → then; in order to → to; compounds (verb) → makes worse / adds to; exacerbates → worsens; mitigate → reduce / lessen; leverage (verb) → use / build on. When a word fails the first-read test for a non-native English reader, rewrite the sentence in plainer words rather than swapping in a rarer synonym.
• Idioms and culturally-specific metaphors. Low-hanging fruit, moving the needle, hit the ground running, on the same page, drill down, deep dive, unpack, baked in, surface (as verb when it means show), align stakeholders, red line (say hard limit or absolute rule), moat (say durable advantage), turn the dial / dial up / dial down (say adjust, increase, reduce). Replace with plain verb.
• Phrasal verbs where a single verb works. Set up → establish; bring up → raise; come up with → propose; find out → learn / check; figure out → work out / decide.
• Directive voice in MA/partner-facing content. "IPPF requires," "must," "shall," "is required to" when the audience is an MA or partner organisation, not a compliance officer. Rewrite to collaborative: "we propose," "we recommend," "MAs may adapt X to their context," "evidence suggests X works in [context]." Reserve directive voice for management briefings, donor deadlines, and reporting compliance content where compliance is the actual subject.
• Buried procedural content. Steps an MA or partner has to implement (adapt an indicator, run a workshop, complete a template) buried in a paragraph. Promote to numbered steps with imperative verbs.
• Recommendation without a worked example. Flag for example addition: "Add a 2-sentence concrete example showing how this applies in [setting]."
• Length above 2500 words without an executive summary OR length above 1500 words without a 5-bullet executive summary on page 1. Flag for restructure.
• Acronyms unspelled on first use. Even M&E, SRHR, CSE, AYFS, MISP, ToC, AYP — spell out on first use, every document.

For Tier 1 subgroup = junior-MEL, additionally flag and fix:

• Conclusion stated without the analytic move. "We ruled out rival explanations" — restore the why: "We chose contribution analysis because the programme operated alongside three other interventions; single-cause attribution would not survive scrutiny."
• Framework name absent from running text. Restore both the framework name and the analytic move (Tier 2-style inline). The end-of-section **Evidence base:** line is still required.
• Evidence base unannotated. Add navigation annotations: Mayne (2019) [framework foundation, read first]; Lemire et al. (2020) [practitioner application].
• Missing pedagogical scaffolding where the topic invites it. Flag for one optional callout per major section: Common pitfall, Why this matters, Worked example, Read more. Do not auto-insert; surface as a suggestion in the audit so Ane can opt in.
• 4+ MEL terms with no glossary footer. Suggest a "Terms used in this document" footer with 1-line definitions.

For Tier 2 publication, flag and fix:

• Plain-English over-simplification of technical claims. The publication audience expects technical register.
• Citations not inline. Move from **Evidence base:** line back into the running text.
• Lens application without visible signposting. Restore the audit trail.
• Framework moves named without the framework name. Restore both.

If the input does not specify a tier and the content is unmistakably Tier 2 (academic register, peer-review framing, journal-article structure), default to Tier 2 and note the assumption at the top of the audit. If the input is unmistakably training or capacity-building content (workshop module, "how we did this" walkthrough, MEL onboarding note), default to Tier 1 / junior-MEL and note the assumption. Otherwise default to Tier 1 / colleague.

Pattern catalog — AI-slop to flag

Distilled from public humanizer research (matsuikentaro1, Aboudjem, jpeggdev, blader, conorbronsdon, apoapostolov), filtered against CLAUDE.md rules. Keep only patterns that agree with Ane's style; drop those that would push toward casual voice.

Lexical tells

• Empty superlatives: crucial, critical, vital, essential — replace with the specific consequence
• Corporate abstractions: synergy, leverage, landscape, ecosystem, journey — replace with a specific noun
• Hype words: game-changing, transformative, paradigm-shifting — delete or ground in cited evidence
• Filler adjectives: robust, comprehensive, holistic, innovative — keep only when a concrete property is named
• Weasel phrases: it is increasingly recognised that, there is growing consensus — needs citation or delete

Structural tells

• Tricolons (three-item lists) pasted in for rhythm rather than content
• Parallel structures padded beyond what the meaning needs
• Headers phrased as questions
• Bullet lists of complete sentences that could be prose
• Nested bullets beyond one level

Tonal tells

• "I hope this helps" and other performative softeners
• "Feel free to" and similar permission grants
• "Let me" statements explaining what is about to happen
• Meta-commentary ("This document will explore...") instead of doing

MEL-specific AI tells

• Generic framework mentions without author and year ("using contribution analysis" without Mayne 2019)
• Indicator lists without disaggregation specified
• Gender references that do not say what the lens actually changed in the analysis
• Donor-report language that conflates output, outcome, and impact
• "Evidence-based" or "rights-based" used as labels without the specific evidence or rights framework named

Output format

Open with a one-line Tier declaration: Tier: [1 working brief / 2 publication] — Audience: [colleague / MA-staff / partner-NGO / management / peer-review]

Then return in two parts:

Audit

A numbered list of issues found, grouped by pass. Each entry:

• Location: the sentence or phrase in quotes
• Pattern: name of the AI-slop or tier-mismatch pattern
• Fix: what the rewrite will do

Rewrite

The full rewritten text. Use bold inline for phrases substantially changed, so Ane can scan the diff. Do not add commentary inside the rewrite itself.

Follow the two sections with a one-line Length delta: Original N words → Rewrite M words (−X%)

Shorter is usually better. If the rewrite is longer than the original, explain why in one clause.

If any pass surfaced genuine uncertainty or missing citations, add a final Data and citation gaps section with ⚠️ entries.

Writing rules applied during rewrite

Every sentence you produce must:

• Lead with the actor or the action
• Use a specific verb, not a nominalisation
• Fit under 25 words
• Name who does what by when, if the content is actionable
• Carry a source if the claim is evidential

Limitations

• Does not fetch new sources. Flags gaps; Ane or evidence-synthesis closes them.
• Does not translate between languages.
• If the input is under 50 words, ask whether a full 8-pass audit is worth the overhead; a targeted pass often suffices.
• If text already matches house style, say so and return it unchanged. Do not fabricate issues to justify running all eight passes.
• Does not alter technical terminology that is correct in context even when it sounds AI-adjacent (e.g., "contribution analysis" is correct; replace only when the usage is wrong).