「loop」の検索結果

Govern FriendlyBet agent values, professional standards, anti-hallucination behavior, truthfulness, risk reporting, AI resource discipline, feedback loops, skill improvement, performance reviews, decision-rights hygiene, and team quality. Use when creating, reviewing, promoting, correcting, or retiring agents; reducing hallucinations or mistakes; reviewing AI/tool usage; handling agent incidents; or aligning work with Eyal's philanthropic user-first approach.

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Shape FriendlyBet Pundit content, bilingual product copy, community voice, share-card copy, social moments, video briefs, Hebrew/English wording, and group-chat friendly communication. Use when a task touches copy, Pundit tone, social videos, share text, community loops, or public-facing FriendlyBet voice.

日本語の概要は準備中です。原文の説明を表示しています。

Layer-by-layer remediation loop using adversarial audits, targeted fixes, and fresh-run verification enforcement

日本語の概要は準備中です。原文の説明を表示しています。

Execute per-layer adversarial audit/remediation loop, persist fixes metadata, and manage fresh-run advancement

日本語の概要は準備中です。原文の説明を表示しています。

Set up and optimize repositories for AI coding agents. Creates minimal AGENTS.md, CLAUDE.md symlink, docs/REQUIREMENTS.md, docs/BUSINESS-RULES.md, feedback loops, and deterministic enforcement (Claude Code hooks, OpenCode plugins). Use when user wants to make a repo AI-friendly, set up AGENTS.md/CLAUDE.md, document requirements/business rules for AI, add pre-commit hooks for AI workflows, or optimize codebase structure for coding agents.

日本語の概要は準備中です。原文の説明を表示しています。

Scheduled health sweep that closes the factory loop - reads CI failures, recent commits, dependency and security advisories, and live queue labels, then files issues and writes an immutable run record. Use for the nightly or weekly monitor routine.

日本語の概要は準備中です。原文の説明を表示しています。

Schedule AI agent runs with cron, loops, or external clocks while avoiding unsafe tight autonomous timers.

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Find and fix WCAG 2.2 accessibility issues. Two modes — report (sweep a codebase or page, produce a prioritized written report, no edits) and fix (audit→edit→verify loop on a target). Prefers di

日本語の概要は準備中です。原文の説明を表示しています。

Route a goal through acc's scored-memory loop via acc_act(runtime="solve"); deliberate any returned brain_frame and submit via continue.

日本語の概要は準備中です。原文の説明を表示しています。

Use for auto error capture, categorization and skill gen.

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Run and grow the OpenAmer Agent-to-Agent swarm: identity, trust, node-to-node ask, signed skill/insight sharing, and the autonomous self-learning loop.

日本語の概要は準備中です。原文の説明を表示しています。

{"tool": "web_search", "params": {"query": "Continuous Learning Loop: Fehler-Capture +

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Conclusion Key takeaways Agent reflection is a feedback loop where AI agents critique their own outp

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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.

日本語の概要は準備中です。原文の説明を表示しています。

RED-GREEN-REFACTOR for process documentation - baseline without skill, write addressing failures, iterate closing loopholes

日本語の概要は準備中です。原文の説明を表示しています。

Scan a codebase for deepening opportunities, present them as a visual HTML report, then guide a user-directed decision loop for the selected candidate.

日本語の概要は準備中です。原文の説明を表示しています。

two-stage review (spec compliance first, then code quality), refactoring, and quality improvement. Use when reviewing code, eliminating code smells, reducing technical debt, refactoring methods, running self-critique loops, or improving maintainability and readability.

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Evaluate and improve AI-generated output with explicit rubrics, reflection loops, and stop conditions. Use when building self-critique workflows, evaluator-optimizer pipelines, or acceptance gates for code, docs, analysis, or plans.

日本語の概要は準備中です。原文の説明を表示しています。

Expert en developer advocacy (communauté, contenu, docs, demos, feedback loop, métriques)

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Estimate AI-assisted and hybrid human+agent development work with research-backed PERT statistics and calibration feedback loops

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Motor de crescimento para produtos digitais -- growth hacking, SEO, ASO, viral loops, email marketing, CRM, referral programs e aquisicao organica.

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Expert in LangGraph - the production-grade framework for building stateful, multi-actor AI applications. Covers graph construction, state management, cycles and branches, persistence with checkpointers, human-in-the-loop patterns, and the ReAct agent pattern.

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Run the startcycle-graph pipeline: durable state, repair loop, quality gate, escalation. Generated from plugin-commands.json; invoke as $bdb-aos:graph.

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Linear multi-agent build pipeline with file hand-offs in production_artifacts/, run after /bdbrainstorm or /grill-me. This is the linear variant — no state.json, no repair loop, no dispatcher graph — distinct from startcycle-graph, which adds durable state and automated escalation. Use when an approved plan should be built, when the user types /startcycle or "run a startcycle", or when you pick a startcycle variant from the situation yourself (no user prompt needed).

日本語の概要は準備中です。原文の説明を表示しています。