AI Agent Workflow Collection

Draft, audit, or minimally revise English- or Chinese-language academic prose to reduce formulaic, vacuous, mechanically repetitive, or process-leaking language while preserving claims, evidence strength, logical relations, manuscript-wide terminology identity, document-level pattern variation, and scholarly register. Use for papers, abstracts, grants, cover letters, and reviewer responses when the user asks to de-AI, humanize, audit AI-like phrasing, or rewrite text without changing meaning. English is primary; Chinese is supported. Not for detector evasion, policy circumvention, pure translation, non-academic copy, or adding facts, citations, examples, or author experiences that the source does not contain.

Turn a completed task, browser flow, artifact pipeline, failure-recovery trace, or repeatedly refined workflow into a new reusable skill package or a reviewed skill-design plan. Use when the user asks to make a new skill from real run history, extract a reusable workflow from conversation/logs/files, summarize lessons into a new skill, or produce a plan before writing files. Do not use to upgrade an existing skill or to execute the business workflow itself.

Use real run evidence, validation failures, source drift, platform drift, and user feedback to plan and, only after explicit approval, apply structural upgrades to an existing skill. Use when the user asks to improve an existing skill from recent runs, recurring failures, outdated sources, excessive bloat, changed platform behavior, or validated workflow feedback. Do not use to create a brand-new skill or to execute the business workflow itself.

Create a concise continuation prompt that a fresh agent session can paste in to resume a long or degraded session. Use when the user asks for a handoff prompt, restart prompt, continuation prompt, context transfer, fresh-session resume, or a compact summary for opening a new session. Do not use for ordinary summaries, task-forest maintenance, durable user-profile updates, automatic session creation, code execution, or external publishing.

Convergent need-alignment system. Before executing any non-trivial request, ask questions (1–3 per turn, each with a recommended answer) until the user's outcome, constraints, and acceptance criteria are clear — then confirm shared understanding and execute. Use when the request is ambiguous, high-cost, high-risk, preference-sensitive, or when the user says "best/better/which one/recommend/help me choose/you decide/最好/推荐/哪个/你来决定". Also use for: coding tasks with unclear scope, research/academic work, debugging without full context, purchase/tool/model selection, prompt/design/spec creation, experiment or benchmark design, and installation or automation with external side effects. Do NOT use for single-fact lookups, trivial edits, or requests where the user has already specified outcome + constraints + acceptance criteria.

Maintains a repo-local task forest or task DAG for the current workspace. Use when the user asks to initialize, update, close a session, summarize evolving project tasks, decide whether a new request is a global task or subtask, track task progress/history/deviations/todos, export a task graph HTML, or provide task data for gap-router/local-agent-control-room. Do not use for executing the tasks themselves.

Local user-profile maintenance skill for Codex, Claude Code, OpenClaw, OpenCode, and other agent harnesses. Use only when the user explicitly invokes this skill or asks to create, initialize, update, query, correct, delete, export, or audit a local persistent user profile. Also use to extract durable collaboration preferences, requirement-expression habits, capability boundaries, recurring omissions, risk preferences, privacy boundaries, and typical events from the current session into auditable, confirmable, retractable local profile data. Do not auto-invoke, upload profile data, or replace task-clarifier's normal clarification flow.