AI & ML

microsoft Skill

Use when the user wants to design or redesign the Dataverse schema and connector plan for an existing mobile app, or has an ER diagram (image, Mermaid, or text) to apply. Skip when the user is creating a brand-new app — /create-mobile-app handles the data model inline.

amhuppert Bundle

Create or iterate on a software design via a multi-agent workflow with parallel research and review phases. Stores artifacts in memory-bank/planning/{name}/.

amhuppert Bundle

Reflect on the conversation and propose improvements to durable AI-agent instruction files such as CLAUDE.md and AGENTS.md.

amhuppert Skill

Audit agent instruction and context files (CLAUDE.md, AGENTS.md, steering files, skills) against current context-engineering best practices. Use when asked to audit, review, or modernize agent instructions.

amhuppert Bundle

Create a research or review agent for the /design workflow.

andyhsutw Skill

Pattern for progressively refining context retrieval to solve the agent context problem

andyhsutw Skill

Prompt-driven learning system that extracts reusable patterns from Copilot CLI sessions, creates atomic instincts with confidence scoring, and evolves them into skills/prompts/agents.

talont-org Skill

Fetch PR review comments, run intent validation (ACCEPT/REJECT/DISCUSS) before applying fixes, and post inline replies. MCP-only — used exclusively by recipe orchestration via run_skill after review_pr reports changes_requested or needs_human verdict.

microsoft Skill

Configure the Canvas Authoring MCP server for the current coauthoring session. USE WHEN "configure MCP", "set up MCP server", "MCP not working", "connect Canvas Apps MCP", "canvas-authoring not available", "MCP not configured", "set up canvas apps".

amhuppert Bundle

Bootstrap web performance debugging tooling: install chrome-devtools-mcp and related MCP servers/skills.

amhuppert Bundle

Create a high-quality image generation prompt for Google's Nano Banana 2 (NB2) model on Gemini.

amhuppert Bundle

Compare AI model prices across Anthropic, OpenAI, and Google. Generates pages-per-dollar / cost-per-token visualizations.

amhuppert Bundle

Save current session context to a handoff file so a future AI agent can continue with no prior context.

georgekhananaev Skill

Run OpenAI Codex CLI for coding tasks, implementation, reviews, and second-opinion audits with mandatory task-based routing across GPT-5.6-or-newer models. Use when a user asks to run, ask, or use Codex; says "codex prompt"; wants a Codex code/logic/plan audit; or wants Claude to delegate work to OpenAI models. Inspect the live Codex model catalog, explicitly pin an eligible model and reasoning effort on every invocation, route clear high-volume work to Luna, everyday work to Terra, and difficult or high-value work to Sol. Use Sol with max reasoning for plan audits. Never invoke or fall back to GPT-5.5, GPT-5.4, GPT-5.3-Codex-Spark, OSS, or any model older than GPT-5.6.

talont-org Skill

Apply versioned migration notes to an AutoSkillit recipe. Use when user confirms migration, called by agent or autoskillit migrate CLI, or invoked directly.

amhuppert Bundle

Covers the agent-offloading principle for AI-assisted development — offload everything deterministic (git operations, validation commands, iteration accounting, output parsing, message routing) from the agent onto code, and reserve the agent for judgment, language understanding, and code synthesis. Includes the division-of-labor table, decision heuristics, orchestrator-owned fields, and typed side channels for agent-produced artifacts. Use when designing an agent workflow or orchestration loop, deciding whether a check belongs in a script or a prompt, tempted to ask an agent to track its own iterations or decide when to halt, or when parsing structured data out of agent response text.

amhuppert Bundle

Make a tool's deliberate constraints legible to agents — one-line rationale at the point of refusal, mechanisms stated where a wrong inference is invited, both sides of a ledger reported, and a published taxonomy of which friction is the product versus which is a defect. Use when agents work around or lobby against a constraint you intend to keep, when a tool refuses actions without saying why, when agents avoid a permitted action they have mis-priced, when designing refusal and status output, or when deciding what an agent should report as a bug versus absorb as the design.

amhuppert Bundle

Define a three-tier feedback vocabulary — hint, reminders, instruction — for steering agents from tool output, with strict tier contracts and an evidence-gated admission rule for reminders. Use when designing tool output that guides agents, adding hints or reminders to a CLI or tool, deciding where mid-flow guidance belongs, or reviewing whether guidance text is in the right tier.

amhuppert Bundle

Run structured retrospectives on any agent setup — instructions (AGENTS.md, CLAUDE.md, system prompts), skills, slash commands, workflows, processes, and tooling — after real use, mining finished runs for durable improvements. Covers the quality → cost → speed review order, deterministic extraction before judgment, spot-checking agent self-reports, and routing every finding to a concrete owner. Use when asked for a "retrospective on how the agent did", "post-mortem on this run", "improve my CLAUDE.md from this session", "audit how the workflow performed", or after rolling out new instructions, skills, or tooling and wanting to know what to change.

amhuppert Bundle

Design a purpose-built project CLI as the primary tool surface for AI coding agents, instead of in-process tool or MCP servers. Covers text-by- default output with opt-in --json (format follows the consumer), the exit- code taxonomy, structured errors, file-based payloads with a validate verb, preflight verbs for gated transitions with findings named by the transition they block, job-shaped long operations, identity resolution, a doctor self-check, and build-parity stamps. Use when building a CLI for agents, migrating away from in-process tool servers, designing tool output and error contracts, deciding between text and JSON output for an agent- facing tool, designing a lint or check command that runs ahead of a gate, choosing severity levels for findings, or deciding how agents should invoke project actions.

amhuppert Bundle

Applies an evidence-earned discipline to agent-facing documentation — a durable rule is written down only after it has bitten, and historical rationale lives in a dated lessons log rather than in code comments. Also covers instruction architecture: a small tool-agnostic root contract plus read-on-demand reference docs treated as persistent project memory with a drift-sync lifecycle. Use when setting up AGENTS.md or CLAUDE.md, when an instruction file keeps growing, when starting a lessons file such as PERFORMANCE.md, when deciding whether a rule has earned a place in agent guidance, or when agents appear to ignore instructions because the document is full of speculative advice.

amhuppert Bundle

Covers how to get reliable machine-readable output from AI agents — separate the thinking turn from the formatting turn, keep schemas flat and bounded, offload substantive content to files and return a small manifest, inject orchestrator-owned fields after parsing, and validate the manifest and the referenced files at the boundary. Includes schema design do/don't lists, prompting rules, the failure model, layered extraction, and one bounded repair turn. Use when an agent's JSON response drives workflow state, when designing a response schema or JSON Schema/Zod contract for an agent, when structured output keeps failing validation, or when deciding what belongs in JSON versus in a generated file.

amhuppert Bundle

Covers configuring development tooling — linters, type checkers, test runners, formatters — so AI agents can consume their output efficiently: keep success output O(1) rather than O(n), preserve every failure detail, strip colors, bail early, and truncate huge diffs. Includes tool-by-tool flags and config for ESLint, TypeScript, Vitest, Jest, and Prettier, the `:ai` package script convention, and a backend-neutral `AI_OUTPUT=1` switch. Use when an agent burns context on passing-test noise, when setting up pre-commit hooks or agent-invoked check commands, when asked to "make the test output quieter for the agent", when deciding what a command should print on success, or when configuring a project's lint/typecheck/test scripts for agent use.

amhuppert Bundle

Design progressive disclosure for the data agent-facing query commands return — bounded summary/outline defaults with stable zoom-in handles, explicit omission lines naming the exact follow-up command, and an escalation ladder from counts to scoped detail to file spillover. Includes a pattern menu (detail levels, field selection, filtering, pagination, search, ranked aggregation, sampling, delta output) and how query output doubles as an instruction surface. Use when designing list/show/search/status verbs for an agent CLI, when a query command can return large output, when an agent burns context reading query results, or when choosing default output size and drill-down flags for a read command.

amhuppert Bundle

Verify features against the live running system — real user flows, real API and model calls, and durable backend state — before calling them done. Covers scratch-datastore isolation, evidence engineering with greppable markers, round-trip persistence checks, and the change classes that only break live. Use when asked to "verify it actually works", "test end to end", "confirm this works in the real app", or before declaring a feature complete on the strength of a green test suite.

amhuppert Bundle

Design structured logging that AI agents can actually debug from — a stable documented event vocabulary, ambient trace context, scoped routing with a global timeline, documented query gotchas, a bounded log-analysis CLI, and forensic per-run logs for long agent executions. Use when designing logging for a project agents will debug, when an agent cannot find what it needs in the logs, when building an agent-friendly log query or performance-analysis tool, or when adding observability to multi-step agent runs.

zangxin75 Bundle

Multi-agent orchestration system that coordinates PM, Architect, DevOps, QA, Tech Lead, and Security agents for complex tasks. Use when building full products, creating SaaS applications, or any end-to-end implementation requiring multiple specialized roles. Implements hierarchical orchestrator-worker pattern.

talont-org Skill

Create Narrative Story Arc visualization planning spec showing visual consistency across the report (same color = same model everywhere), logical figure progression, redundant figure detection, and narrative dependency between figures. Narrative lens answering "Do the figures tell a coherent story across the report?"

georgekhananaev Bundle

Guide for creating high-quality MCP (Model Context Protocol) servers that enable LLMs to interact with external services through well-designed tools. Use when building MCP servers to integrate external APIs or services, whether in Python (FastMCP) or Node/TypeScript (MCP SDK).

tdimino Bundle

Summon Claude Fable 5.1 (Mythos-class, xenos theoros) by default, or Fable 5.0 (--naos). Handles model override, cost awareness, and task routing. Availability probed at invocation.

tdimino Bundle

Search, post, monitor, and archive on Twitter/X via five tools: the official hosted X MCP server (full-archive search, trends, bookmarks, Articles via xurl bridge), x-search CLI (cost-tracked API v2 research with calendar-day windows, volume counts, multi-query fan-out, feeds, watchlists, posting), xurl (official CLI for any endpoint incl. media/DMs), bird CLI (free session-based reads/writes, frozen at 0.8.0), and Smaug bookmark archival. Triggers on: search tweets, tweets from today, what did X tweet, latest tweets from, tweet volume, expand a Twitter query, check Twitter for, post a tweet, tweet this, post to X, reply on Twitter, full-archive search, what's trending, check mentions, my mentions, timeline, DMs, monitor account, research topic on Twitter, my bookmarks, archive bookmarks.

georgekhananaev Skill

Multi-agent orchestration patterns. Use when multiple independent tasks can run w/ different domain expertise or when comprehensive analysis requires multiple perspectives.

georgekhananaev Skill

Master orchestrator, peer-to-peer, and hierarchical multi-agent architectures

kishorkukreja Skill

When the user wants to forecast using deep learning, LSTMs, transformers, or neural networks. Also use when the user mentions "neural network forecasting," "LSTM," "GRU," "transformer forecasting," "attention mechanisms," "seq2seq," "temporal convolution," "deep learning time series," or complex non-linear patterns. For traditional forecasting, see demand-forecasting. For general ML, see ml-supply-chain.

kishorkukreja Skill

When the user wants to model inventory systems with uncertain demand, optimize safety stock levels, implement (s,S) or (Q,r) policies, or analyze service levels under uncertainty. Also use when the user mentions "stochastic inventory," "probabilistic inventory," "(Q,r) policy," "(s,S) policy," "base stock policy," "safety stock optimization," "service level constraints," "lead time demand distribution," "fill rate calculation," or "inventory with demand uncertainty." For deterministic models, see economic-order-quantity or lot-sizing-problems. For single-period uncertainty, see newsvendor-problem.

kishorkukreja Bundle

Frame a messy supply-chain pain point as a specific, owned decision; determine whether AI is appropriate; and select a bounded delegation model and use case. Use when leaders, planners, consultants, or transformation teams need to scope an AI-agent opportunity, compare candidate use cases, define human approval boundaries, or decide that process, data, or governance must be fixed before AI.