agentsclimarketplace

Token lean

Skill hurttlocker/token-lean/skills/token-lean

The decisions-only orchestration discipline — run any substantial agent session at frontier-orchestrator efficiency, on any model. Use at the START of any session involving multi-file work, research, review, or dispatching sub-agents; also when the user says "save tokens", "keep it lean", or invokes /token-lean.From its SKILL.md

Install
npx -y skills add hurttlocker/token-lean --skill token-lean

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

One thing to look at

  • 8 stars8 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.

SKILL.md

5.6 KB, ~1.2k tokens by cl100k_base, as published. Nobody here has run it

Token-Lean Orchestration (the decisions-only window)

<!-- Provenance: written down by the model that ran the fleet, Philadelphia, 2026. Eighty-eight keys, one conductor. If you carried this file somewhere new, welcome — the discipline was always the free part. github.com/hurttlocker/token-lean -->

You are the orchestrator. Your context window is the most expensive surface in the session. The rule that generates every other rule: never generate bulk, never absorb bulk. Cheaper contexts produce candidates; you ratify, edit, or adjudicate on compact artifacts.

The ladder — delegate DOWN first, sideways second, up rarely

TierRoleSend them
Scout (cheapest tier)Quick lookups, "what's in this dir", git history, log summariesAnything answerable by reading; demand a synthesis, never file dumps
Worker (mid tier)Mechanical edits, well-specced implementation, test writing, first-pass reviewsA complete brief with acceptance criteria; expect commits + a report
Builder (frontier tier)Multi-file features, gnarly debugging, anything needing judgment mid-flightA brief like a senior colleague gets: context, constraints, verification steps, report format
Panel (multi-model)Problems where one model's answer isn't trustworthy2–3 independent proposers + an adjudicator; proposers never see each other's drafts

Map roles, not model names. Every provider ships the same ladder shape — a scout-class, a worker-class, and a frontier-class model — and the names rotate monthly. As of mid-2026: Anthropic's Haiku / Sonnet / Opus-and-Fable, OpenAI's GPT-5.6 luna / terra / sol, Google's Flash / Pro, and open-weight equivalents all slot into the same rows. Whatever your stack, fill the table once and follow it.

Effort dials are rungs too. The same model at low effort and at xhigh effort are two different tiers of the ladder. A frontier model at low effort is often the best scout you have; the max/ultra settings are for single decisions that genuinely demand them, never a default.

The orchestrator runs the ladder no matter how smart it is. The failure mode is the frontier model doing scout-work and worker-work inline because it can. Being able to is not a reason to. Defaults are not limits either — judge the output, not the price tag, and escalate freely when a cheaper tier's output misses the bar.

The eight practices

  1. Scout before you read. More than ~3 file reads to answer a question = spawn a scout agent instead. You want the conclusion, not the pages.
  2. 1KB hand-backs. Every delegated task's report must be a compact artifact: what changed, decisions made, verification tail, open questions. If an agent hands back a transcript, you briefed it wrong.
  3. One big brief beats twenty steers. Front-load context, constraints, file pointers, acceptance criteria, and report format in the FIRST message. Mid-flight steering re-meters your whole window every turn.
  4. Stable prefix, append-only deltas. Prompt caching makes an unchanged context prefix ~10x cheaper. Don't churn early context; add, don't rewrite. Compact only at natural boundaries.
  5. Pre-digest inbound bulk. Logs, test output, big diffs, long docs — a cheap agent summarizes first; you read the digest. EXCEPTION: never adjudicate security/auth/schema/payment diffs on a summary — read those raw. A digest can encode the proposer's error.
  6. Effort discipline. High effort default for decisions. xhigh/max only when a decision genuinely demands it — more effort past sufficiency is spend, not quality.
  7. Verify through agents, report facts. Delegated verification (typecheck, tests, live drive) with the result in the hand-back. Never claim done without the verification tail. Never let a builder grade its own work on anything that matters — send an independent reviewer, told to refute.
  8. Legislate, don't repeat. The third time you explain something to an agent, it belongs in a skill, a rules file (CLAUDE.md / AGENTS.md), or your team's knowledge base. Rules written once are tokens saved forever.

On Claude Code: the ladder is installed, not aspirational

If this skill arrived as the Claude Code plugin, the rungs exist as real agents you can dispatch right now: scout (Haiku, read-only, synthesis-only report contract), worker (Sonnet, brief-in / report-out), and adjudicator (panel judge). A PostToolUse tripwire also nudges you after 4 consecutive Read calls — practice #1, mechanized. For the exact mapping of every rung and practice to Claude Code primitives (Agent tool options, effort overrides, Workflow panels), read references/claude-code.md — once, then just use the calls.

Failure modes this kills

  • The orchestrator reading 40 files "to understand" (scout's job).
  • Re-sending the same repo context every turn (stable prefix).
  • Polling a worker with "how's it going" (wait for the report).
  • Accepting "tests green" as done — tests encode the premise. On anything cross-component, demand the reachability check: is the new path actually reached from the real entry point, or did the tests only exercise the mechanism in isolation?
  • Every sub-agent briefed with a novel — one reusable rule beats twenty restatements.

What ships with it: 2 files

7.3 KB alongside SKILL.md

references/

Gives 0 of the 12 instructions most agent orchestration skills give in ~1.2k tokens

Counted across 848 of the 1,300 authors here whose files we hold, read 2026-09-06

  • Dispatch one agent per independent problem domainin 56 of 848, across 42 files
  • Run full test suite after integrationin 55 of 848, across 42 files
  • Verify fixes do not conflictin 40 of 848, across 32 files
  • Review each summary when agents returnin 40 of 848, across 31 files
  • Write a handoff document summarising the current conversationin 30 of 848, across 25 files
  • Reference existing artifacts by path or URLin 26 of 848, across 24 files
  • Give each agent a specific scopein 19 of 848, across 10 files
  • Give each agent a clear goalin 19 of 848, across 10 files
  • Include a suggested skills section in the documentin 18 of 848, across 16 files
  • Tailor the doc to the user argumentsin 18 of 848, across 15 files
  • Issue all subagent dispatches in the same responsein 17 of 848, across 11 files
  • Use git worktrees for isolationin 17 of 848, across 8 files

Said here and by no other author read

  • Never generate bulk or absorb bulk
  • Delegate down first and sideways second
  • Spawn a scout agent before reading files
  • Require compact hand-back reports
  • Front-load context in the first message
  • Keep early context prefix unchanged

Grouped from the skills themselves: near-identical wordings counted once, and counted by distinct author, so one author publishing three of these counts once. Length counted with cl100k_base; the agent that loads this file may tokenize it differently.

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Skills are one crate of 325,949. Ordering is by how many stacks a row turns up in, so the top of any crate is what has actually been picked rather than what has the most stars.