「autonomous」の検索結果

Route ordinary Proto UI development through a minimal composition of repository skills. Use when starting, continuing, or handing off feature, fix, spec, contract, Module, Host Capability, Adapter, Prototype, component, test, documentation, review, or release-preparation work. Do not use for autonomous-maintenance runs; use pui-maintain.

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。

Independently attempt to falsify one Proto UI autonomous-maintenance finding in a fresh non-mutating context. Use after pui-observe produced a candidate finding and route the result directly to remediation or closure when authority is clear.

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。

Close one technically complete Proto UI autonomous-maintenance transition after adequate independent review and required validation. Use to synchronize the finding, remediation packet, mission, run ledger, residual-risk queue, and recorded integration decision.

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。

Establish Proto UI repository state, live contributor authority, Agent comprehension scope, task risk, and authorization before work begins. Use at the start of a development session, before autonomous task selection, after a material repository update, or when permission or assessment state may have changed.

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。

Independently review one Proto UI autonomous-maintenance remediation in a fresh read-only context. Use after pui-remediate produces a packet and actual diff. Classify technical-completion eligibility without making product or integration decisions.

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 one eligible transition in Proto UI's governed autonomous-maintenance protocol. Use for maintenance missions, drift discovery, finding verification, accepted remediation, independent maintenance review, run-ledger closure, or residual-risk planning. Do not use for ordinary user-directed development.

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。

Record one authorized non-remediation terminal outcome in Proto UI's autonomous-maintenance state. Use after a supported no-finding result, an independently rejected finding, or a blocked mission whose terminal evidence is complete.

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 one bounded read-only Proto UI autonomous-maintenance observation mission. Use for drift hunting, external-oracle comparison, repeatability control, or a no-finding maintenance run. Stop before verification or remediation.

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。

Run Proto UI's dynamic comprehension assessment and derive an unsigned local task-fit result. Use when an Agent needs review calibration or a fresh autonomous task and review ceiling. Do not use a self-result as identity, permission, or acceptance evidence.

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。

This skill should be used when the user asks to "explore the app", "/qa", "auto-test the app", "find bugs in my app", "猴子测试", "自动探索", "随便点一下看看", "测一下全 app 看有没有崩", "smoke explore", or wants autonomous exploration of an Android or iOS app to surface crashes / ANRs / unexpected states. Drives the app via ui-mcp + mobile-mcp, tracks visited pages and clicked elements via report-mcp's state graph, captures crashes via log-mcp, and produces a Markdown report with bug list + coverage stats.

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。

This skill should be used when the user says "找 bug" / "看一下有没有 bug" / "smart-qa" / "/smart-qa" / "帮我测一下这个项目" / "智能 QA" / "推断业务流程测一下"，or asks for an autonomous bug-hunt where they DON'T already know what to test. Distinct from `qa` (blind exploration) and `devtest` (verifies a specific change). smart-qa READS the project source — prefers PRD/requirements docs, falls back to code (activities, routes, click handlers, API calls) — then proposes a focused test plan for confirmation before driving the app. v1 stops after the user confirms the plan and hands off to the existing `qa` skill for execution. v2 (planned) adds per-step assertions.

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 ANY bug bounty, penetration test, or web/API/mobile security assessment: recon, testing endpoints, analyzing Burp traffic or any bug-bounty platform's MCP (HackerOne, Intigriti, Bugcrowd, YesWeHack, Immunefi), reviewing APKs, bypassing a WAF, enforcing scope, hunting a specific vuln class, validating findings, writing reports, or when you are stuck on a target and about to give up. Activates automatically for all bug-bounty and pentest sessions. Runs an autonomous, never-surrender predator methodology: refuses to conclude "secure", exhausts each vuln class to depth, proves a real attacker-path with impact and no false positives, tracks coverage so nothing is re-tested or skipped, diagnoses failed tooling from full logs, and hunts at maximum effort with intelligent token use until the goal is found. Activates and follows instructions in ANY language the operator writes in (the skill itself is English). Bundles a per-class web-exploitation KB, a writeup library, and PayloadsAllTheThings.

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。

Slice a NowStack Mobile app's .agents/plan/PRD.md + .agents/plan/ARCHITECTURE.md into well-scoped, implementable task files under .agents/plan/tasks/, each an autonomous 1–3h vertical slice (Convex schema/function → Expo screen → wiring) with success criteria, dependencies, and verification commands. Use for "create tasks", "ns tasks", "break this into tickets", or as the third planning step after ns-prd and ns-architecture. Both docs must exist first.

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 multi-step AI agent architectures with tools, memory, and orchestration. TRIGGERS - Use when user wants to design AI agents, multi-agent systems, or autonomous AI workflows.

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 Solana trading applications combining DFlow trading APIs with Helius infrastructure. Covers spot swaps (imperative and declarative), prediction markets, real-time market streaming, Proof KYC, the DFlow Agent CLI for autonomous trading, transaction submission via Sender, fee optimization, shred-level streaming via LaserStream, and wallet intelligence.

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 Solana trading applications combining DFlow trading APIs with Helius infrastructure. Covers spot swaps (imperative and declarative), prediction markets, real-time market streaming, Proof KYC, the DFlow Agent CLI for autonomous trading, transaction submission via Sender, fee optimization, shred-level streaming via LaserStream, and wallet intelligence.

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。

Intake for an autonomous research run (Arbor). Use when the user wants to optimize a metric in a repo over many isolated experiments — 'optimize this benchmark', 'improve the model F1 overnight', 'beat the leaderboard'. Discovers the repo/eval/splits, measures the baseline, confirms a Research Contract, then emits a ready-to-send /auto-research command. Does NOT start the run itself.

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。

AI-powered autonomous data extraction that navigates complex sites and returns structured JSON. Use this skill when the user wants structured data from websites, needs to extract pricing tiers, product listings, directory entries, or any data as JSON with a schema. Triggers on "extract structured data", "get all the products", "pull pricing info", "extract as JSON", or when the user provides a JSON schema for website data. More powerful than simple scraping for multi-page structured extraction.

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 autonomous LLM agents operating in live environments. Protects against prompt injection, tool abuse, data exfiltration, and runaway loops. Keywords: guardrails, prompt injection, IPI, safety, sandboxing, NeMo, LLM Guard.

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 autonomous code editing, bug fixing, or software engineering agents. Keywords: SWE-agent, code agent, bug localization, patch generation, AST, diff, TDD loop, repository indexing.

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 in designing, orchestrating, and managing multi-agent systems (MAS). Specializes in agent collaboration patterns, hierarchical structures, and swarm intelligence. Use when building agent teams, designing agent communication, or orchestrating autonomous workflows.

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。

Agentic workflow patterns for autonomous LLM reasoning. Use when building ReAct agents, implementing reasoning loops, or creating LLMs that plan and execute multi-step tasks.

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。

Generates Claude Code plugin agent .md files: writes YAML frontmatter (name, model, color, tools, hooks, disallowedTools), crafts system prompts, and creates triggering example blocks with context and commentary. Validates agent structure against naming, length, and format constraints. Use when the user asks to create an agent, add an agent, write a subagent, configure agent frontmatter, design agent system prompts, set agent tools or colors, build an autonomous agent, or add agent hooks.

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。