「loop」の検索結果

Perpetual autonomous work loop for Claude Code — no end condition, no memory regression, no context overfill. Maintains a persistent ledger across all sessions.

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。

Build applications where agents are first-class citizens. Use this skill when designing autonomous agents, creating MCP tools, implementing self-modifying systems, or building apps where features are outcomes achieved by agents operating in a 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。

Design effective AI agent systems with clear architecture, capability planning, and tool integration. Use when building agent systems, designing agent workflows, planning tool ecosystems, defining agent roles and boundaries, or architecting multi-agent systems. Covers agent patterns, agentic loops, capability design, and system composition.

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。

Analyze changes and create a meaningful commit with agent authorship. Internal skill for evolve_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。

Provides guidance for automatically evolving and optimizing AI agents across any domain using LLM-driven evolution algorithms. Use when building self-improving agents, optimizing agent prompts and skills against benchmarks, or implementing automated agent evaluation loops.

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。

Monetization and pricing strategy specialist. Use this agent to design revenue models, set price points based on value perception, and create compounding referral loops. It focuses on the "Cuan" (Revenue) aspect of the 1-Man Army pipeline.

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。

Investor Email Specialist. Use to write fundraising outreach, warm intro blurbs, follow-ups, investor updates, and close-the-loop emails with founder-grade brevity and specificity.

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。

Voert de iteratieve desk-review-&-completion-cyclus uit op een regelrecht-corpus — plannen, harvesten/migreren/uitbreiden, multi-agent valideren, bevindingen 4-weg classificeren (modellering-fout / wetgevings-fout / engine-limitatie / acceptabele untranslatable), synthese + heroverweging, documenteren (wetgevings-/stelselfouten-analyse, fixes-plan, engine-limitaties, diagrammen, corpus-status), verifiëren tegen externe bronnen (resolutie-tracker + bronnen-dossier), en een eindrapport per cyclus. Gebruik dit bij corpus-review op correctheid/coverage zonder live expert-sessie, het opstellen van een wetgevings-notitie-bron, schema-migratie, het harvesten van nieuwe wetten, of het aansturen van een autonome completion-loop. Dossier-agnostisch; de regelrecht-methode is de vaste taal. Voor live-validatie met experts: zie regelrecht-audit-products.

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。

Review a diff, commit, branch, PR, or implementation for actionable correctness and risk issues without duplicate reviewer loops or invented findings.

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。

Package interview loops, schedules, feedback follow-ups, and candidate next steps for recruiting operations. Use when the work is routine hiring coordination and recap packaging. Do not use for final hiring decisions, compensation judgment, or sensitive people-policy advice.

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 when creating or restructuring a PlayCanvas app with the Engine API, @playcanvas/react, or @playcanvas/web-components, covering bootstrap, input, resize, physics, update loop, UI, and lifecycle.

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 when building 2D games in Python with pygame - game loop and delta time, sprites and groups, collision detection, drawing and surfaces, events and input, sound and fonts, camera, or performance optimization

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。

Section-by-section media planning and generation. Image generation (GPT Image 2 primary, built-in fallback), logo/icon generation (Ideogram v3 → favicon set), video generation (Veo 3.1), social preview images (OG 1200x630 + AI search optimization), stock photo curation (Pexels, Pixabay), critique/remix loops (max 3 rounds), asset compression pipeline, and media performance budgets.

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。

Implements features in vertical slices, always starting with homepage. Enforces anti-placeholder rules — no lorem ipsum, no TODO stubs, no gray boxes. Real content, real images, real interactions. TypeScript strict mode, Zod validation, and structured file organization.

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。

How every delivered app ships and stays up — Cloudflare-first hosting, prod-only (no staging), the deploy→prod-verify loop, atomic deploy + rollback.

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。

MANDATORY deploy after every code change. Typecheck → deploy → purge CDN → E2E on production → visual verify → fix-forward loop. Workers Builds native CI/CD, D1 Time Travel PIT recovery, D1→R2 long-term backups, wrangler rollback, wrangler secrets management, structured observability, cross-browser smoke tests, rollback procedures, and GitHub auto-configuration.

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。

Provider-agnostic patterns for LLM function calling, tool loops, and agentic workflows

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Phase 4 of the feature-pipeline suite. Use this after /fp-review has posted a sandbox review comment — it reads the outstanding checkbox items, applies fixes, then watches CI until green (or exits cleanly so you can retry). Trigger phrases: "address review items", "fix review feedback", "resolve review checklist", "watch CI", "poll CI", "check if CI is green", "iterate on PR", "fp-iterate", "Phase 4", "after review comment", "fix the review issues and watch CI". Always bounded — max 10 poll rounds, never loops forever. Exits with a clear status: green (hands off to /fp-drift), still-pending (re-invoke), or failed (diagnose before touching anything).

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。

Visual design verification loop for matching a goal PNG to a live render. Use when the user shares a design mock / goal image and asks to match the UI to it, asks to "iterate on this design" or "match this 1:1", or calls out visual drift in a previous UI iteration. Provides histogram color sampling, shape inspection (radius/border/shadow/bg-mode/fill-vs-context), computed-style checking on the live render, snap, and side-by-side diff. After every UI change the assistant MUST snap the live render and Read the snap with the Read tool before reporting the change done — skipping that is the

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。

Analyzes position-independent code and shellcode by identifying GetPC/PEB-walk techniques, API-hash resolution loops, and relocation-free addressing so the payload can be disassembled at the right base. Activates for requests to analyze position-independent code, identify PIC techniques, or understand how shellcode resolves APIs without imports.

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Defeats control-flow-flattening obfuscation by identifying the dispatcher/state- variable structure and reconstructing the original control flow so the logic becomes readable. Activates for requests to defeat control-flow flattening, deobfuscate an OLLVM-flattened function, or recover original control flow from a dispatcher 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。

The investigation harness for "why is this slow?" over a Pulp Perfetto trace (.pftrace). Runs the hypothesis→query→drill-down chain-of-evidence loop autonomously and returns a plain-English root cause + evidence + a concrete fix. TRIGGER on "why is my plugin slow to open", "find the slowest frames", "why is the UI stuttering", "why is my plugin using so much CPU", "which DSP node is expensive", "the load meter looks calm but CPU is pinned", or a named `pulp trace gpu-startup|gpu-health|gpu-probe` analysis. Ships Pulp-specific hints for dsp, frame, js, gpu, and cross-platform symptoms.

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。

SQL discipline for querying Pulp Perfetto traces (.pftrace) with trace_processor — idempotent CREATE OR REPLACE PERFETTO views, GLOB not LIKE, dur = -1 incomplete-slice handling, EXTRACT_ARG for span args, joining on stable utid/upid, SPAN_JOIN PARTITIONED, and the draft→validate→execute loop. Ships Pulp's CPU/UI trace stdlib plus closed GPU startup, health, and probe views. TRIGGER when writing or debugging SQL over a .pftrace, when `pulp trace query` returns wrong/empty rows, or when the trace-analysis skill needs a query primitive.

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。

Build evaluation frameworks for LLM systems — harness design, graders, datasets, regression tracking, and human-in-the-loop eval. Use when you need systematic measurement of model or agent quality.

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。