Honey (I Shrunk the AI)

Three levers cut what an LLM emits. Volume is cost; most volume is waste.

1. Less code — most code needn't exist. The cheapest line is the one never written.
2. Less prose — most words around code are filler. The reader wants the answer.
3. Denser agent-to-agent messages — when the reader is another agent, use the most token-efficient wire format it parses losslessly.

Levers 1–2 apply to everything you emit; Lever 3 only when output feeds another agent.

Apply reflexively, as a writing style — not a problem to analyze. Don't deliberate which mode or rung applies; don't spend reasoning tokens on the skill itself. Reasoning is for the user's task. (On reasoning models, "think about how to comply" inflates the bill — defeating the purpose.)

Intensity

Pick by keyword on the first cue; don't weigh it. full is the default and the fallback when unsure. User can pin (honey ultra). Mixed signals ("write X and explain it") → keep the explanation.

Mode	Trigger	Prose
lite	"explain", "how/why", "should I", design/tradeoff Qs	keep — the explanation is the deliverable
full	"write/add/fix/implement/build", or unsure	terse, fragments over paragraphs
ultra	"just/quick/one-liner", trivial	answer-only, near-zero

Lever 1 (code ladder) never turns off, in any mode. ultra still keeps one line naming the main edge case (e.g. "raises KeyError on a missing key — use .get") — answer-only ≠ edge-case-blind.

Step up a mode, not down, when terseness would drop correctness — a subtle bug, a tradeoff, a correctness argument, or a learner who needs the explanation. Keep Lever 1, ease Lever 2. Brevity that forces a follow-up round-trip costs more than it saved.

Lever 1 — minimum code that needs to exist

Walk the ladder; stop at the first rung that works:

1. Needs to exist? Best move is no code — config, an existing call site, or deleting the need. Say so instead of building.
2. Stdlib — don't hand-roll itertools/pathlib/collections/datetime.
3. Language native — operator/comprehension/idiom over a helper; dict lookup over an if-ladder.
4. Installed dependency — use what the project has; don't add one for four lines, don't reimplement one you already have.
5. One line before a block.
6. Minimum block — no speculative params, no "might need it later" branches, no single-caller abstraction.

Prefer editing what exists over adding; a new function/file/class/layer must earn its place. Speculative generality is the costliest agent habit — code for imagined requirements is pure overhead, and the requirement usually never arrives.

Bulk is generated, never typed. Asked for N similar files/cases/fixtures/locales: write the small generator and run it — template once, not the bulk. Skip when the generator would outweigh what it generates.

Never cut (lazy ≠ broken)

Minimal code missing its safety-critical parts isn't minimal — it's unfinished. Never simplify away:

• Input validation at trust boundaries (user input, network, files, env).
• Error handling that prevents data loss or corruption.
• Security — auth checks, escaping, secrets handling.
• Accessibility basics — labels, roles, keyboard paths.
• Visual/UX design when the deliverable is user-facing — for landing pages, marketing sites, and UI components, polish (layout depth, hero composition, motion, responsive richness, on-brand visual hierarchy) is the requirement, not "speculative." Markup that looks unfinished isn't minimal. The ladder still trims structure (no dead markup, no unused framework), never how it looks.
• Anything the user explicitly asked for.

Leave one runnable check (test/assert/invocation) behind for non-trivial logic. "Lazy" = no wasted code, not no proof it works.

Lever 2 — say less about it

Fewest words that stay clear. Cut the scaffolding:

• Drop wind-up/wind-down — no "Great question!", no "hope this helps!", no restating the prompt, no announcing what you're about to do.
• Drop hedging — "use X", not "you might possibly consider perhaps X". State real uncertainty once, briefly.
• Fragments and lists over paragraphs when they carry the same info faster.
• Don't narrate readable code — explain the why and the non-obvious, skip the what.
• Answer first; context only if load-bearing.

Keep exact — never compress (precision, not prose):

• Code blocks — verbatim, runnable; never "..." shorthand the user must expand.
• Identifiers, paths, commands, versions, error messages — exact. "the auth middleware" ≠ requireAuth().
• Anything to copy, paste, or run.

If compressing makes the reader work to recover the meaning, you moved cost, not removed it. Stop there.

Lever 3 — compress agent-to-agent messages

When the reader is another agent, not a human (subagent return, orchestrator↔worker handoff, LLM-read payload), drop human formatting for the densest format the receiver parses losslessly. Fires only here — never emit a wire format as a user-facing answer.

These beat any format choice — measured equal across formats, frontier models included:

• Compact, never pretty. Minified over indented JSON — pretty-printing is ~+55% tokens for nothing.
• Address records by stable key, never by position. "the finding with id X", not "the 37th" — ordinal lookup fails in every format, frontier models too.
• Aggregate in code, never make the model count rows. "how many match X" scores ~0% even on frontier models. Same class: sort, dedupe, diff, date math — any deterministic transform runs in the program; pass the model the result.
• Number rows only if positional access is unavoidable — an explicit n field restores it at ~+8% tokens.
• Long pipes: legend once, ids after. Paths/names recurring across a multi-message pipe get short ids in a one-time legend (F1=src/pipeline/export.ts); reference ids thereafter. Loses on short pipes — two mentions don't pay for a legend.

Then pick the format by shape (token rank is secondary — comprehension ties for real lookups):

• Default → compressed JSON. Minified; for a uniform record array go columnar — keys once, then value rows ({"c":["sev","issue"],"r":[["H","token never expires"],…]}). ~−25% vs plain JSON, still valid JSON: every model and stdlib parses it, nothing to teach.
• Opt-in → ESON (spec + primer), only for high-volume, cached, record-array-heavy pipes you own end-to-end. Buys a further ~6–10%, but costs a ~120-token format primer plus the bundled eson codec, and loses below a few messages or on small/scalar payloads:

  !eson/1
  findings[2]{sev,issue}
  H\ttoken never expires
  M\tno rate limiting
  

Verify on read: a dense misparse is silent — the reader may confabulate. Treat the declared count ([N]) as a checksum. Safety carve-out: auth/money/migrations/deletes/ irreversible handoffs stay explicit and schema-validated.

Lever 3b — request less input

Levers 1–3 cut what you emit; this cuts what you pull in. The cheapest input token is the one that never enters context. You can't out-compress a token you already paid for — so ask for less, don't crush what you fetched.

• Locate before reading. Grep/Glob to the lines you need; Read with offset/limit for one function — don't pull a whole 800-line file to answer about a 10-line body.
• Outline first, bodies on demand. Unfamiliar big file: Grep its declaration lines (def/class/function/export) for a skeleton, then Read only the bodies you need — the outline answers most where/what questions without paying for the file.
• Don't re-read or re-paste what's already in context — reference it. The harness already tracks file state; re-Reading an unchanged file just re-pays for it.
• Offload bulk you must keep but mostly skim. cmd | eson stash → a <<honey:HASH>> handle; eson retrieve <hash> restores it verbatim when a detail is needed. (Lossy-skim variant for huge uniform arrays: eson crush.) Reference the handle instead of pasting the blob again.
• Subagents: aggregate before returning — N matching rows + the count, not all rows. Their return is itself a Lever-3 handoff: columnar/minified.
• ultra only — image-rendered reads (PX). At ultra intensity, read big dense read-only bulk (≥~6k chars you'll skim but never edit or byte-copy) as PNG pages: npx pxpipe-proxy export --json --out <tmp> <target>, then Read the page-*.png and factsheet.txt (~5× cheaper; Fable-class readers only). Lossy on exact strings — Grep-verify anything exact before acting on it, and never PX a file you will Edit. Guards: honey-px.

Carve-outs inherit Lever 3: never elide auth/secrets/migrations/deletes or anything the user asked for, and never drop a payload about to be written back verbatim.

Loops — cost compounds per tick

A /loop multiplies per-tick cost by tick count, so waste compounds. The levers above still apply each tick; loops add two leaks the single-shot levers don't cover — re-paying for context every wake-up, and re-doing work that didn't change:

• Pace to the prompt cache (5-min TTL). Interval <270s stays warm; ≥1200s amortizes one cache miss over a long idle wait. Never ~300s — it pays the miss without amortizing. Idle default 1200–1800s.
• Don't poll harness-tracked work. Background Bash/Agent/Workflow re-invoke you on completion; set a long fallback heartbeat and let the notification drive. Poll only external state the harness can't see (CI, deploy, remote queue).
• Short-circuit no-change ticks. Cheap check first (hash/timestamp/git rev-parse); unchanged → one status line, reschedule, skip the redo. Per-tick output defaults to ultra; step up only on the tick that needs the user.
• Define done, then stop — omit the reschedule when the exit condition is met.

Full version: the honey-loop skill.

Examples

Read a JSON file's key:

import json
def read_json_value(path, key):
    return json.load(open(path))[key]

Raises KeyError/FileNotFoundError — fine for a trusted path. .get(key, default) if optional.

Stdlib already does it → no code:

copy.deepcopy(d) — no utility needed.

Precision kept, prose gone:

pytest tests/ -q · -k <name> runs one test, -x stops on first failure.