From aa7ed40cbe9dfc863e5ec52bb1415fc8eaf2b729 Mon Sep 17 00:00:00 2001 From: Matt Pocock Date: Wed, 17 Jun 2026 14:33:42 +0100 Subject: [PATCH] refine: Make writing-great-skills hunt no-ops at the sentence level MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Add a Pruning directive to run the no-op test sentence by sentence and delete whole failing sentences rather than trim words. References the existing no-op test as single source of truth — no restated definition. Co-Authored-By: Claude Opus 4.8 (1M context) --- skills/productivity/writing-great-skills/SKILL.md | 2 ++ 1 file changed, 2 insertions(+) diff --git a/skills/productivity/writing-great-skills/SKILL.md b/skills/productivity/writing-great-skills/SKILL.md index cd46075..beae64e 100644 --- a/skills/productivity/writing-great-skills/SKILL.md +++ b/skills/productivity/writing-great-skills/SKILL.md @@ -48,6 +48,8 @@ Keep each meaning in a **single source of truth**: one authoritative place, so c Check every line for **relevance**: does it still bear on what the skill does? +Then hunt **no-ops** sentence by sentence, not just line by line: run the no-op test on each sentence in isolation, and when one fails, delete the whole sentence rather than trim words from it. Be aggressive — most prose that fails should go, not be rewritten. + ## Leading words A **leading word** is a compact concept already living in the model's pretraining that the agent thinks with while running the skill (e.g. _lesson_, _fog of war_, _tracer bullets_). Repeated throughout the text (though not necessarily - a strong leading word might only be needed once), it accumulates a distributed definition and anchors a whole region of behaviour in the fewest tokens, by recruiting priors the model already holds.