Results for “autonomous”

Autonomously improve a real artifact (code, training recipe, agent harness, data pipeline, prompt) against an objective and an evaluator, using Hypothesis Tree Refinement (HTR) from the Arbor paper. Use this whenever someone wants to iteratively optimize something over many experiments without overfitting — e.g. "get my model's eval score up", "improve this agent/harness", "tune this pipeline", "beat the baseline on this benchmark", "run a search over approaches and keep the best", "do an MLE-bench / Kaggle-style optimization", or any long-horizon "make this artifact better and don't just memorize the dev set" task. Trigger it even when the user doesn't say "Arbor" or "hypothesis tree" but describes repeated experiment-and-evaluate loops, branching exploration of competing ideas, or worries about a dev/test gap. Runs Claude itself as the coordinator with subagent executors in isolated git worktrees; for the standalone `arbor` CLI tool see references/arbor-upstream.md.

The English summary is being prepared. Showing the original description.

Expert en agent loops (ARIS, Ralph, recherche autonome, build-score-budget, guardrails)

The English summary is being prepared. Showing the original description.

Drive a whole tdmcp feature BACKLOG to completion as resumable, wave-by-wave releases — the campaign layer ABOVE tdmcp-pipeline/tdmcp-feature-lead. Use whenever the user wants to implement an ENTIRE backlog or discovery file (e.g. _workspace/discovery*/FEATURE_BACKLOG*.md), 'all the features', many features across multiple releases, or a long autonomous build campaign, AND for every follow-up: continue/resume the campaign, run the next wave, re-run a failed wave, fold in QA results, or check campaign status. Backed by a ledger.json so re-running is idempotent (skips shipped work, resumes interrupted work) and resilient (retry-once then quarantine-and-continue). For a SINGLE feature or one small batch, use tdmcp-pipeline instead; this skill is for the whole ledger.

The English summary is being prepared. Showing the original description.

Release tdmcp: write the Keep-a-Changelog entry, bump the SemVer version across every manifest, then (autonomously, per project policy) commit, tag, and push — gated on a td-qa PASS, with hard git safety rails. Use when releasing/shipping/cutting a tdmcp version, writing the CHANGELOG, bumping the version, or tagging a release.

The English summary is being prepared. Showing the original description.

Run autonomous research tasks that plan, search, read, and synthesize information into comprehensive reports.

The English summary is being prepared. Showing the original description.

Autonomous CI/CD error recovery, distributed file-based locking, diagnostic triage, and two-phase GO gate resilience engine for BDB Agent OS multi-agent pipelines.

The English summary is being prepared. Showing the original description.

Use when running the autonomous multi-agent build pipeline after /bdbrainstorm or /grill-me, when the user types /startcycle-graph or "run a startcycle", or when a task needs durable state, a review repair loop and an automated quality gate. Routes to the startcycle-dispatch Dynamic Workflow, which reads production_artifacts/state.json and invokes Architect, TechLead, UI/UX, Engineering, Media/EventTech, Reviewer, and Shipping in turn per .agents/graph.md's edge table — the agents never invoke each other.

The English summary is being prepared. Showing the original description.

AI agent development workflow for building autonomous agents, multi-agent systems, and agent orchestration with CrewAI, LangGraph, and custom agents.

The English summary is being prepared. Showing the original description.

Interactive 9-step prompt crafting and delegation protocol for autonomous multi-agent teams. Enforces objective verification, integrity modes, and acceptance criteria across Antigravity, Claude Code, Cursor, OpenCode, Codex, and Roo Code.

The English summary is being prepared. Showing the original description.

Reference for how BDB structures autonomous software engineering work — the seven-node dispatcher graph (Architect, TechLead, UI/UX, Engineering, Media/EventTech, Reviewer, Shipping) that /startcycle-graph actually runs. Use when you need the high-level lifecycle framing without inventing your own process.

The English summary is being prepared. Showing the original description.

Invocable iOS design pipeline. When called with a design prompt, runs an autonomous loop: designs iOS screens in Pencil MCP following Apple HIG, reviews for taste using Design Philosophy, validates with XcodeBuildMCP, and iterates until done. Use as: /ios-design-stack "Design a [screen] for [app] with [requirements]"

The English summary is being prepared. Showing the original description.

Autonomous build-verify-fix loop that runs until an explicit Definition of Done passes. Use when named (mini-loop) or when the user says exactly "run a loop".

The English summary is being prepared. Showing the original description.

MUST USE whenever the user wants to automate a real desktop or sandbox - clicking, typing, scrolling, screenshotting, running an OS shell command, or handing a high-level 'open browser and do X' task to an autonomous computer-use agent. Wraps the trycua/cua Python toolkit via its `cua` CLI - pynput-based, cross-platform (macOS / Linux / Windows). NO custom tools are registered; you call `cua` through pi's built-in bash and read screenshots back through the Read tool. Triggers: cua, computer use, computer-use, GUI automation, screenshot the desktop, click on the screen, type into the active app, scroll the page, control my computer, drive my browser, sandbox, docker sandbox, QEMU sandbox, Lume sandbox, ComputerAgent, cua do, cua sandbox, 컴퓨터 자동화, 스크린샷 찍어, 내 컴퓨터 조작, 브라우저 열어서, 샌드박스, 화면 자동화, 마우스로 클릭, 키보드 타이핑, computer use 위임, 자동으로 클릭, 자율 에이전트로 처리.

The English summary is being prepared. Showing the original description.

Autonomously deep-scan entire codebase line-by-line, understand architecture and patterns, then systematically transform it to production-grade, corporate-level professional quality with optimizations

The English summary is being prepared. Showing the original description.

Multi-agent autonomous startup system for Claude Code. Triggers on "Loki Mode". Orchestrates 100+ specialized agents across engineering, QA, DevOps, security, data/ML, business operations, marketing, HR, and customer success. Takes PRD to fully deployed, revenue-generating product with zero human intervention. Features Task tool for subagent dispatch, parallel code review with 3 specialized reviewers, severity-based issue triage, distributed task queue with dead letter handling, automatic deployment to cloud providers, A/B testing, customer feedback loops, incident response, circuit breakers, and self-healing. Handles rate limits via distributed state checkpoints and auto-resume with exponential backoff. Requires --dangerously-skip-permissions flag.

The English summary is being prepared. Showing the original description.

Full autonomous execution from idea to working code

The English summary is being prepared. Showing the original description.

AI agent development workflow for building autonomous agents, multi-agent systems, and agent orchestration with CrewAI, LangGraph, and custom agents.

The English summary is being prepared. Showing the original description.

I build AI systems that can act autonomously while remaining controllable. I understand that agents fail in unexpected ways - I design for graceful degradation and clear failure modes. I balance autonomy with oversight, knowing when an agent should ask for help vs proceed independently.

The English summary is being prepared. Showing the original description.

Skill for discovering and researching autonomous AI agents, tools, and ecosystems using the AgentFolio directory.

The English summary is being prepared. Showing the original description.

Autonomous DevSecOps & FinOps Guardrails.

The English summary is being prepared. Showing the original description.

Autonomous DevSecOps & FinOps Guardrails. Orchestrates Gemini 3 Flash to audit Linux Kernel patches, Terraform cost drifts, and K8s compliance.

The English summary is being prepared. Showing the original description.

Autonomous model validation and benchmarking. Investigates any ML model (LLM, image gen, TTS, time series, etc.), runs it on GPU cloud, evaluates quality and performance, and generates HTML reports. Use when user asks to verify, benchmark, evaluate, or test a model. Triggers on "verify model", "benchmark", "evaluate model", "test model", "run benchmark", "model evaluation", "モデルを検証", "ベンチマーク", "モデルを試して".

The English summary is being prepared. Showing the original description.

Keep iterating on code changes until the tests pass, the build succeeds, or linting is clean. Runs in a tight loop of fix → run → check → repeat. Use when you want the agent to autonomously grind through test failures or build errors.

The English summary is being prepared. Showing the original description.

Framework for building LLM-powered applications with agents, chains, and RAG. Supports multiple providers (OpenAI, Anthropic, Google), 500+ integrations, ReAct agents, tool calling, memory management, and vector store retrieval. Use for building chatbots, question-answering systems, autonomous agents, or RAG applications. Best for rapid prototyping and production deployments.

The English summary is being prepared. Showing the original description.