Agent skill · nvidia
tao-token-efficient-execution
Token-efficient execution kit for repeated TAO agentic workflows. Compiles a skill into stage cards once (with a strong model), then executes them forever in fresh, small headless sessions coordinated by a bash driver — measured 67-91% token reduction and 3-5x lower peak context vs one long conversation. Use when a TAO workflow will be run repeatedly, when token cost matters, when the target model has a small context window, or on trigger phrases like "token efficient", "reduce token cost", "card pack", "stage cards", "run this workflow on a small model", "compile this skill into cards".
What it needs
About 4k tokens when loaded.
What this skill does
TAO Token-Efficient Execution Run a staged TAO workflow as a series of fresh headless agent sessions — one stage card per session — instead of one long conversation. State lives on disk, never in chat history, so no session ever rereads the run's past. Measured on real workflows (same tasks, same outcomes, billed tokens): Workflow One long conversation Card execution Peak context --- --- --- --- DEFT AOI loop 9.6M 2.5M (-74%) 242k → 59k AutoML (VCN classify) 5.8M 0.5M (-91%) 198k → 42k HuggingFace finetune 1.7M 0.5M (-67%) 119k → 42k The mechanism: an agent API is stateless, so a long conversation resends its entire history with every message — by mid-run that is ~100k+ tokens of skill text, spec edits, and training logs reread on every tool call, of which the current step needs ~3k. Cards make each step's 3k explicit and drop the rest. The three layers 1. Core (harness-agnostic) — stage cards (markdown, env-var contract), a bash driver (stage routing + job waiting), and on-disk state contracts (references/CONTRACTS.md). 2. Harness adapters (adapters/) — guard + recorder implemented per harness: hooks for Claude Code, extensions for Pi. Same logic, ~150 lines each. Guards block known dead ends before tokens are burned; the recorder appends executed commands to commands.log so later sessions reuse instead of re-derive. 3. Card packs (per application skill) — compiled cards living next to the skill they were authored from, e.g. skills/applications/tao-run-deft-aoi/cards/ and skills/applications/tao-run-automl/cards/. Each pack ships its own driver.sh and README.md (version pin, required env, how to run). Cards shrink the model's job; they don't remove it. The driver owns routing and waiting (deterministic), helper scripts own state commits (deterministic), and the model executes one pre-planned stage: run verbatim commands, read a gate table, decide pass/fail from real output, fill in the one dynamic value. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills nvidia/tao-token-efficient-execution