ade35dc0d8
The repeated "The load-bearing constraint:" opener on every page read as an agent tell. Strip the label across all skill pages and let the constraint stand as a plain declarative sentence; update the writing-docs template so it isn't regenerated. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
3.2 KiB
Quickstart:
npx skills add mattpocock/skills --skill=writing-great-skills
npx skills update writing-great-skills
What it does
writing-great-skills is the reference you write and edit skills against — the shared vocabulary and principles that make a skill predictable.
A skill's job is to wrangle determinism out of a stochastic system, so the goal is not the same output every run but the same process. Predictability is the root virtue, and every design choice is judged against it — not against how clever, complete, or exhaustive the skill reads.
When to reach for it
You invoke this by typing /writing-great-skills — the agent won't reach for it on its own.
Reach for it whenever you're authoring a new skill or editing an existing one and want it to behave the same way every time: deciding invocation mode, writing a description, choosing what lives in SKILL.md versus a linked file, or diagnosing why a skill misfires.
Cognitive load
The concept the whole reference turns on is cognitive load — and its counterpart, context load. Every skill spends one or the other:
- A model-invoked skill keeps a description in the window every turn, so it costs context load but fires on its own.
- A user-invoked skill strips that description; it costs zero context load, but now you are the index that has to remember it exists — that's cognitive load.
Most of these skills are user-invoked, which is why cognitive load is the pressure the whole system is built to manage: when user-invoked skills multiply past what you can hold in your head, the cure is a router skill that names the others and when to reach for each. Once you're thinking in these two loads, most authoring decisions — split or don't, inline or disclose, model- or user-invoked — become the same trade made in different places.
The other levers
The rest of the reference is the toolkit for spending those loads well:
- Leading words — a compact concept already in the model's pretraining (tight, red, tracer bullet) that the agent thinks with while running the skill. It anchors execution and invocation in the fewest tokens; hunt restatements that a single word can retire.
- Information hierarchy — the ladder from in-skill step, to in-skill reference, to external reference behind a context pointer. Progressive disclosure is the move down that ladder so the top stays legible.
- Pruning — single source of truth, relevance, and the no-op test applied sentence by sentence, against sediment and sprawl.
- Failure modes — premature completion, duplication, sediment, sprawl, no-op — to diagnose a skill that isn't behaving.
Where it fits
This is a reach-for-it-anytime standalone reference — the meta-skill you consult while building the rest of the set, not a step in a chain. Its natural neighbour is any router you maintain, because a router is the direct cure for the cognitive load that user-invoked skills pile up; when you're unsure which skill or flow fits a task, ask-matt routes you over the whole set.