De la autor
Runs a Gauntlet Loop: turns an ambitious goal into a builder/critic prompt judged against a concrete quality bar, confirms with the user, then leads the run until the output beats the bar or the user stops it — or exports a paste-ready /goal prompt for a fresh session. Use for long-horizon goals with an inspectable reference. Long-running; spawns many subagents, edits project files, and commits and pushes finished pieces to a feature branch.
Instalare
npx skills add oprogramadorreal/optimus-claude --skill gauntlet -a claude-code
Se instalează în .claude/skills/gauntlet/ · Fiecare comandă descarcă skill-ul direct de pe GitHub cu utilitarul open source skills.
Identic octet cu octet cu commit-ul scanat, cu licența depozitului și un fișier SOURCE.txt.
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Verificări de siguranță pe SKILL.md
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De ce 83
aiqbench calculează scorul de încredere din semnale publice. Nu este un audit de securitate, așa că citește un skill înainte să-l instalezi.
Skill-uri similare
Cele mai apropiate de acesta prin ce fac și când sunt folosite.
- craft-goalhashgraph-online/awesome-codex-pluginsDraft or lint a bounded long-running goal prompt with a finish line and hard limits. Use when: selected by name; one change goes to Plan.Planificare★ 1.4K
- build-loopnelsonwerd/idea-to-ship-skillsDrive a build to actually-works, near-finish-line craft by looping build → see → exercise → check → critique → rebuild over the agent's existing tools (bash build/test, headless screenshot + vision, Playwright, axe/Lighthouse) until acceptance criteria pass or an explicit stop-condition fires — no infinite thrash. ALWAYS invoke when the user says any of "tighten this build", "iterate until it passes", "self-verify the UI", "make it actually work, not just compile", "loop until the core flows pass", or "drive this to near-finish-line". Also invoke proactively after a scaffold or feature lands and before handing a draft to a human. Composition-first over existing tools, not a new program; it states its own honest bounds — objective craft only, taste self-graded, ~80% ceiling, market validation out of scope. Do NOT use to decide WHAT to build (that's ideate) or whether a design/strategy is sound (deep-dive).Testare★ 90
- goal-testundefined-ui/second-brain-osTurn a vague task into a testable definition of done and generate an executable goal-test script for it, optionally with a bounded retry loop around a headless agent. Use when the user wants to run an agent in a loop, asks how to know when an agent task is finished, or says a goal like "improve X" needs to become checkable. Do NOT use for building eval suites over many cases — that is evals-bootstrap.Testare★ 1.1K
- prompt-packnelsonwerd/idea-to-ship-skillsAuthor and execute "prompt packs" — a big job broken into a sequence of self-contained, independently-shippable prompts, each run in its own fresh chat so a single change never dies to a context/token limit. Also writes paste-ready handoff briefings to resume a dying chat or relay work to another tool (e.g. Codex). ALWAYS invoke when the user says any of "make a prompt pack", "turn this into prompts", "break this into phases", "prompts I can run in separate chats", "I'm running out of tokens/context", "write me a handoff", "a prompt I can paste into a new chat", "this is too big for one chat", "build plan I can hand off", or "execute Pxx / phase X from the pack". Also invoke proactively when a requested change is too large or too risky to finish safely in one session and should be decomposed into ordered, verifiable steps.Redactare★ 90
- prompt-engineeringgiuseppe-trisciuoglio/developer-kitProvides workflows to write, debug, and optimize prompts for LLMs, including few-shot example selection, chain-of-thought structuring, system prompt design, and template composition. Use when the user asks to write or improve a prompt, wants help with few-shot examples, chain-of-thought, system prompts, prompt templates, or asks how to get better results from an LLM.Design★ 362
- prompt-optimizegaasher/Agent-Loop-SkillsUse when the user has a prompt that feeds a system they can already score, and wants that prompt automatically improved to raise the score against their own evaluation command. Makes one targeted quality edit per iteration — clarity, context, specificity, structure, examples, decomposition, guardrails — re-runs the user's eval to measure the metric, and keeps the edit only if the metric improves, else reverts; loops to a target, plateau, or budget. The metric is whatever the user's eval command prints (task accuracy, an LLM-judge score, a pass rate, a tool-call success rate); the loop is metric-agnostic and never edits the eval. Not for writing a prompt from scratch, not for tuning model weights or hyperparameters, and not for one-off manual prompt edits without a score.Redactare★ 177
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