writing-for-agents

Reference for writing any document an agent consumes — a skill, an / , a doc reached by a pointer. The packaging differs; the writing does not: the same levers make each one predictable — the agent taking the same process every run, not producing the same output.

When the document you're writing is a skill, read

SKILL-MECHANICS.md

for frontmatter, invocation choice, and router skills.

A context pointer is a reference held in the agent's context that names some out-of-context material and encodes the condition for reaching it. A skill's description is one; a line in naming a doc is the same object. The pointer's wording, not its target, decides when the agent reaches the material — and how reliably. A must-have target behind a weakly worded pointer is a variance bug: sharpen the wording first, and inline the material only if sharpening fails.

A pointer does two jobs — state what the material is, and list the branches that should trigger reaching it (a branch is a distinct case the document handles, so different runs take different paths through it). Every word of an always-loaded pointer costs on every turn, so it earns even harder pruning than the body:

Every document and pointer you add spends one of two budgets:

Material reached only through a pointer escapes context load at the price of the pointer's own line; material with no pointer at all rides entirely on cognitive load.

A document is built from two content types — steps (the ordered actions the agent performs) and reference (definitions, rules, facts consulted on demand) — that mix freely: all steps (a recipe), all reference (a review's rules, this skill), or both. The core decision is where each piece sits on the information hierarchy, a ladder ranked by how immediately the agent needs the material:

Push too little down and the top bloats; push too much and you hide material the agent actually needs. That tension is the whole decision.

Progressive disclosure is the move down the ladder — out of the main file and behind a pointer — so the top stays legible. Not primarily a token optimisation: it is how the hierarchy is protected. Branching is the cleanest disclosure test: inline what every branch needs, and push behind a pointer what only some branches reach. When a document has steps, in-file reference that should be disclosed buries them and turns attending to them into a coin-flip — a variance lever, not just a legibility one.

Co-location is the within-file companion: where the ladder decides how far down a piece sits, co-location decides what sits beside it once there. Keep a concept's definition, rules, and caveats under one heading rather than scattered, so reading one part brings its neighbours with it. The test: the document should read like documentation written for the agent — grouped material reads that way; scattered material does not. (Distinct from duplication: that repeats one meaning in two places; scattering fragments one meaning across many.)

Sprawl is the failure mode here: a document simply too long, even when every line is live and unique. Attention thins across the excess, and every extra line is one more to keep relevant. The cure is the ladder: disclose reference behind pointers, and split by branch or sequence so each path carries only what it needs.

Every step ends on a completion criterion — the condition that tells the agent the work is done. Two properties make it a lever:

The strongest criteria are both checkable and exhaustive.

Splitting one document into two spends one of the two loads, so split only when the cut earns it:

A leading word is a compact concept already living in the model's pretraining that the agent thinks with while running the document (lesson, fog of war, tracer bullets). Repeated as a token, never as a sentence, it accumulates a distributed definition and anchors a whole region of behaviour in the fewest tokens, by recruiting priors the model already holds. Coining your own works if you define it clearly, but a made-up word recruits no priors — you pay in definition tokens what a pretrained word gives free; reach for an existing word first.

It anchors twice. In the body, execution: the agent reaches for the same behaviour every time the word appears, and inside flat reference it focuses attention on a class of thing to look for. In a pointer, invocation: when the same word lives in your prompts, your docs, and your codebase, the agent links that shared language to the material and reaches it more reliably.

Hunt for opportunities to refactor with leading words. A triad spelled out at three sites, a pointer spending a sentence to gesture at one idea — each is a passage begging to collapse into a single token:

You win twice: fewer tokens, and a sharper hook for the agent to hang its thinking on. Assume every document is carrying restatements that leading words retire — go find them.

Negation is the failure mode beside this lever: steering by prohibition drags the forbidden behaviour into context and makes it more available, not less. Don't think of an elephant, and the elephant is all there is; the negation is a weak modifier the strongly-activated concept overruns, so the ban half-reads as an instruction to do the thing. Prompt the positive — state the target behaviour ("write one-line comments") so the banned one is never spoken. A prohibition earns its place only as a hard guardrail you cannot phrase positively; even then, pair it with the positive target so attention lands on what to do.