auto-paper-improvement-loop
Auto Paper Improvement Loop: Review → Fix → Recompile
Autonomously improve the paper at: $ARGUMENTS
Context
This skill is designed to run after Workflow 3 (/paper-plan → /paper-figure → /paper-write → /paper-compile). It takes a compiled paper and iteratively improves it through external LLM review.
Unlike /auto-review-loop (which iterates on research — running experiments, collecting data, rewriting narrative), this skill iterates on paper writing quality — fixing theoretical inconsistencies, softening overclaims, adding missing content, and improving presentation.
Constants
- MAX_ROUNDS = 2 — Two rounds of review→fix→recompile. Empirically, Round 1 catches structural issues (4→6/10), Round 2 catches remaining presentation issues (6→7/10). Diminishing returns beyond 2 rounds for writing-only improvements.
- REVIEWER_MODEL =
gpt-5.4— Model used via Codex MCP for paper review. - REVIEWER_BIAS_GUARD = true — When
true, every review round uses a freshmcp__codex__codexthread with no prior review context. Never usemcp__codex__codex-replyfor review rounds. Set tofalseonly for deliberate debugging of the legacy behavior. Empirical evidence (April 2026): running the same paper withcodex-reply+ "since last round we did X" prompts inflated scores from real 3/10 → fake 8/10 across 5 rounds; switching to fresh threads recovered the true 3/10 assessment. - REVIEW_LOG =
PAPER_IMPROVEMENT_LOG.md— Cumulative log of all rounds, stored in paper directory. - HUMAN_CHECKPOINT = false — When
true, pause after each round's review and present score + weaknesses to the user. The user can approve fixes, provide custom modification instructions, skip specific fixes, or stop early. Whenfalse(default), runs fully autonomously.
💡 Override:
/auto-paper-improvement-loop "paper/" — human checkpoint: true
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