AI Agent Workflow Collection

Design AI-supported collaborative tasks that structure group interaction and address participation problems. Use when students struggle to collaborate effectively on group tasks.

Use when user wants to align docs with the current project status or new…

Use when creating, reviewing, or updating the Artifact Registry and artifact PASS conditions for a product development workspace.

Routes between five assessment pathways — formative, rubric/criteria, authentic/performance, peer/self, and diagnostic — with validity and equity checks. Use when a teacher needs help choosing how to assess.

Review Agiflow Planning tasks for readiness, prioritize approved work, group related tasks into work units, and promote ready tasks to Todo. Use when grooming a backlog, organizing planned tasks, creating work units, or deciding what should be executed next.

Phase 3: Drafting. Plan-Then-Execute chapter generation with parallel writing and continuity validation.

Fiction-specific patterns — Save the Cat! 15-beat, Snowflake Method, Hero's Journey, character bible, scene structure.

Use when the user asks to "agentize a website", "transform a website", "create an agentfront", "build a WWA app", "make an agent-first website", "turn a website into an agent experience", "agentizer", "wwa transform", or mentions "agentfront" in context of building one from a URL.

Map knowledge components and skill hierarchies for a cognitive tutoring system or adaptive learning platform. Use when designing intelligent tutoring software or skill-based mastery systems.

Interactive Control Plan builder — takes PFMEA failure modes and process flow steps as input and builds a complete Control Plan row by row, with correct control methods, sample plans, and reaction plans for each characteristic. Use when building a new Control Plan, updating after a corrective action (8D D7), or converting a PFMEA into Control Plan format. Covers AIAG Control Plan reference manual and IATF 16949 §8.5.1.

Create a governed session override for a policy that would otherwise deny — mandatory justification, server-clamped TTL, blocked for critical-risk…

Check an educational practice or task for cultural bias, WEIRD assumptions, and cross-cultural validity. Use when adapting resources for diverse contexts or questioning universal claims.

Run the Data Contract CLI (datacontract test) against ODCS contracts in the project to verify the live data still conforms — schema, quality rules, and freshness. Handles two kinds of contracts with different semantics: output-port contracts under models/outputports//.odcs.yaml (tested against this project's warehouse — "am I still producing what I promised?") and input-port contracts under models/inputports/.odcs.yaml (tested against the upstream warehouse — "is upstream still producing what I trusted?"). Trigger when the user asks to "test the data contracts", "verify the data product matches its contract", "are we still contract-conformant", "check upstream drift", or "run the contract tests".

Full specialist analysis via parallel agent dispatch. Researcher, Architect, and PM produce a prioritized report of what to build next (30-60s).

Use only when the user explicitly asks for a commit message without review. Trigger on: 帮我写 commit message, 给个 commit message, 生成 commit message, 这次 commit message 怎么写, write a commit message, skip review, 跳过 review, 我自审过了, only message, 只要 message. Writes a message from the current git diff in repository style. Does not review code quality or mutate the working tree; ambiguous commit requests like 帮我 commit route to dev-code-review.

Optimize Django ORM query loading by fixing N+1 queries, duplicate related-object queries, over-fetching, serializer/template query loops, and inappropriate selectrelated, prefetchrelated, only, or defer usage. Use when a Django page, API, admin view, template, serializer, or queryset issues too many queries or loads more model data than needed.

Process large Django querysets and write-heavy jobs with memory-safe reads, values/valueslist, iterator chunking, set-based update/delete, bulkcreate, bulkupdate, F expressions, Func expressions, and batch sizing. Use when a Django command, task, migration, report, or loop reads or writes many rows and is slow, memory-heavy, or query-heavy.

Design a visual complement to verbal content using dual coding principles for stronger encoding. Use when creating slides, diagrams, posters, or visual explanations of complex concepts.

Use the Exa MCP server for internet search and full-page retrieval. Use whenever the user asks to search, browse, look up, verify, research, find sources, find current or latest online information, discover papers or repositories, compare web sources, or read and summarize a URL whose content was not provided. Prefer Exa by default for general web research. Do not use it for local filesystem or codebase search, or when the user explicitly requests a different tool or a specialized connected source is the direct system of record.

Apply the Expectation Effect — the principle that prior expectations measurably change a person''s perception of a product or experience. Use when shaping the framing of new features, designing onboarding, choosing visual cues that signal premium vs. budget, or diagnosing why two functionally identical features feel different to users. Expectations are set by visual cues (premium materials, polished typography), language (precise, confident copy), social proof, price, and prior brand exposure. Setting expectations too high creates disappointment; too low and the product reads as mediocre. The skill is calibrating what to promise.

Bench-aware Frappe operations guidance for app installs, branch switching, updates, migrations, site inspection, ports, and process state. Use when a task involves bench commands or local environment operations.

Balance game difficulty, resources, rewards, probability, progression, economies, and dominant strategies. Use when tuning combat, loot, upgrades, scoring, level curves, AI difficulty, risk-reward choices, or any system where numbers shape player decisions.

Apply the Gestalt principle of Similarity — elements that share visual attributes (color, shape, size, orientation) are perceived as belonging together, regardless of their physical proximity. Use when grouping related items in a list, status indicators, dashboard cards, navigation, or any context where you want the user to perceive certain items as a set. Similarity is one of the strongest grouping cues; it works even when proximity says otherwise. Color is the strongest similarity cue, then size, then shape; orientation is weakest.

Design, implement, test, and ship SQL-backed persistence for Go CLIs using database/sql, explicit pool ownership, bounded startup checks, embedded migrations, transaction boundaries, parameterized queries, adapter error mapping, Scanner and Valuer types, SQLite or server databases, and real-driver integration tests. Use when a Go command stores durable state in SQL, initializes a local database, applies schema changes, or needs safe repository and transaction behavior.