Agent skill · software engineering · pinchtab
pinchtab-opt
Run the PinchTab optimization loop (Docker, 3 blind subagents on the runner's HIGH model, 108 steps across 47 groups) against chrome, cloak, ghost-chrome, or all three providers. Pass `setup` (optionally followed by a provider or `all`) to run only the setup test (native binary, single subagent forced to the runner's LOW model) that validates the fresh-install OOTB flow per provider. Use when asked to 'run optimization', 'run the opt loop', 'benchmark the agent', '/pinchtab-opt', '/pinchtab-opt cloak', '/pinchtab-opt ghost-chrome', '/pinchtab-opt setup', '/pinchtab-opt setup all', or 'test pinchtab agent'.
Why this skill is useful
Adds a comprehensive executable script for running the PinchTab optimization loop with specific configurations and validations that are not commonly documented.
What it needs
Requires docker installed locally. About 9k tokens when loaded. Last updated 2026-08-03. 9,933 stars on the source repository.
What this skill does
PinchTab Optimization Loop Two independent modes selected by the argument. They use different runtimes, different models, and answer different questions — only one runs per invocation. Think of the arg surface as a matrix: mode × provider. Model role is fixed by mode (not user-selectable). Mode Providers Runtime Model role Asks --- --- --- --- --- Optimization (default) chrome (default), cloak, ghost-chrome, all Docker, 3 parallel subagents HIGH (default/strong) how few browser ops does the agent need across 108 steps vs baseline Setup (setup keyword) chrome (default), cloak, ghost-chrome, all native binary, 1 subagent LOW (small/fast) can an agent go zero→working from the skill docs alone (OOTB doc-quality gate) Model roles This skill names model tiers abstractly so any runner (Claude, OpenAI, …) can map them at launch time: LOW — small/fast/cheap model. Used by the setup test because a weak model passing is the actual doc-quality signal; a strong model passing is unsurprising. HIGH — the runner's default/strong model. Used by the optimization benchmark because we want the realistic agent performance, not a deliberately handicapped run. Suggested mappings (pick whatever the runner has available at the time it executes): Runner LOW HIGH --- --- --- Claude Code Haiku (e.g. claude-haiku-4-5) inherit parent (Opus / Sonnet) OpenAI Agents gpt--mini tier gpt- flagship tier Other smallest capable model default/best model The thresholds below were calibrated for Claude Haiku 4.5 as LOW; if you use a different LOW, recalibrate the token / tool-call numbers on the first run. Argument Parsing /pinchtab-opt [setup] [chrome cloak ghost-chrome all] Positional args, in order. The first token is either a provider (optimization mode) or the literal setup keyword (setup mode); if setup, the second token is the provider. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills pinchtab/pinchtab-opt