oice Detection Rate | Review Consistency | Draft Iterations | |----------|-------------------------|--------------------|------------------| | Monolithic CLAUDE.md + Linear Pipeline | 65-70% | Low (contradictory feedback) | 4-6 | | Dedicated Agents + Constraint-Driven Pipeline | 15-20% | High (homogeneous, actionable) | 2-3 |

Key Findings:

• Separating drafting (redacteur.md) from reviewing (reviewer.md) eliminates context contamination and stabilizes feedback quality.
• Explicit anti-pattern enforcement drastically reduces AI-isms, preserving authorial voice.
• Structured bullet-point knowledge extraction prevents over-writing and accelerates final composition.
• The sweet spot lies in phase-gated constraints: strict guardrails during ideas/knowledge generation, followed by multi-angle critique in isolated sessions.

Core Solution

The architecture shifts from a monolithic prompt file to a modular agent/skill ecosystem with a strict ideas -> knowledge -> output pipeline.

1. Pipeline Restructuring

• ideas: Capture raw concepts, enforce bullet-point structuring, prohibit full prose generation.
• knowledge: Research, source validation, and structured extraction. Guardrails prevent narrative drift.
• output: Final composition using extracted knowledge, applying tone and style constraints.

2. Agent & Skill Architecture

• redacteur.md: Handles ideation and knowledge structuring. Outputs a skeleton with style guardrails. Compensates for AI over-writing by enforcing concise, scannable data points.
• reviewer.md: Multi-angle critique (journalist, skeptical reader, bullshit detector). Runs in isolated sessions to ensure fresh, homogeneous feedback.
• review-voice.md: Audits final output for banned patterns, journalistic standards, and strong passages.
• review-idea.md: Validates ideas for prose violations, pre-oriented blocks, and missing conceptual fields.
• review-knowledge.md: Checks knowledge bases for unsourced claims and narrative drift.

3. Context Optimization

• CLAUDE.md reduced to <200 lines, containing only essential tone definitions, pipeline explanations, and core constraints.
• Anti-pattern list maintained as a living constraint file, updated post-publication or during review cycles.

4. Anti-Pattern Enforcement (Living Constraint List)

• "Le vrai..." / "La vraie..." — overused, signal too heavy
• "Dit autrement :" — condescending
• "C'est exactement ce qui/ça/le piège" — formulaic AI intensifier
• "Et c'est peut-être le point le plus important." — theatrical
• "Et c'est normal." — paternalistic
• "Voici pourquoi." (hook ending) — clickbait
• "X ne disparaît pas. Elle change de nature." — over-smoothed antithesis
• "Ce n'est pas qu'une question de X. C'est une question de Y." — cliché transition
• "Ce qui m'a frappé" / "Ce qui m'a retenu" — naming emotion instead of evoking it
• "C'est le signal." (conclusion) — concluding for the reader

Pitfall Guide

1. Monolithic Context Bloat: Overloading CLAUDE.md with drafting, reviewing, and style rules dilutes context and causes inconsistent model behavior. Keep core configuration under 200 lines and delegate responsibilities to dedicated skills.
2. Conflating Creation & Critique: Running reviews in the same session as drafting contaminates context and triggers contradictory feedback. Always isolate reviewer agents to preserve analytical freshness.
3. Unfiltered AI Phrasing Patterns: LLMs default to formulaic intensifiers, theatrical conclusions, and condescension. Maintain a living anti-pattern list updated from published drafts and enforce it via dedicated review skills.
4. Over-Reliance on Linear Ideation: Allowing AI to freely expand ideas/knowledge results in over-written, AI-sounding content. Enforce bullet-point structuring and strict guardrails during the knowledge phase.
5. Quota & Infrastructure Blind Spots: Long, unstructured sessions exhaust API quotas and trigger rate limits. Implement phase-gated workflows to control token consumption and prevent mid-session throttling.
6. Neglecting Reviewer Freshness: Context carryover from drafting sessions biases critique. Use dedicated reviewer agents with clean context windows to ensure homogeneous, unbiased feedback.

Deliverables

• Blueprint: Complete agent architecture and pipeline configuration available in the GitHub starter pack: agaches/starter-packs/tree/main/blog
• Checklist: AI Voice Anti-Patterns & Style Guardrails (banned phrases, tone rules, structural constraints, and phase-specific validation steps)
• Configuration Templates: Ready-to-deploy skill definitions for redacteur.md, reviewer.md, review-voice.md, review-idea.md, and review-knowledge.md, along with an optimized CLAUDE.md template under 200 lines.