augmentedcode-configuration

Route durable personal context and reusable knowledge between augmentedcode-configuration and the personal knowledge vault. Use when the user asks to remember, persist, capture, retrieve, or organize personal knowledge; when deciding whether information belongs in rules, skills, portfolio, notes, maps, or inbox; or when using personal-knowledge-vault as a low-context reference layer.

Use when running git clone, git remote add, git remote set-url, or suggesting GitHub SSH commands to ensure all GitHub access uses the saski SSH host alias.

Build modern data apps, dashboards, and interactive reports using either React + Vite or Streamlit. Includes optional Gemini Data Analytics chat integration for an AI powered "chat with your data" experience. Relevant when any of the following conditions are true: 1. User explicitly requests to build a data dashboard, data application, or visualization UI, and the UI pulls data from a GCP database (defaulting to BigQuery unless otherwise specified). 2. You need to generate a frontend web application to interact with, query, and visualize data from GCP data sources. 3. User wants to build a "chat with your data" experience or integrate the Gemini Data Analytics chat API into a web interface. Do NOT use when any of the following conditions are true: 1. The request is for building backend-only services. 2. The request is for simple CLI scripts or command-line applications. 3. The web application is not data-centric or does not involve visualizing/querying data from GCP sources.

Develops and executes Spark code on Dataproc Clusters and Serverless. Reads and writes data using BigLake Iceberg catalogs, BigQuery and Spanner. Debugs execution failures. Use when: - Writing Spark ETL pipelines on GCP. - Training or running inference with ML models with spark on GCP. - Managing Spark clusters, jobs, batches, and interactive sessions. Don't use when: - Writing generic Python scripts that don't use Spark. - Performing simple SQL queries that can be done directly in BigQuery.

Use when the user wants to clean up AI slop, harden a codebase, or run technical due diligence: review code for security flaws and vulnerable or wrongly-licensed dependencies, find and remove dead code, audit dependency health, run and triage linters, type checkers, test suites and formatters, evaluate design principles (SOLID and the cohesion/coupling equivalents for non-OO stacks), audit logging and observability, find performance smells, check documentation and CI health, scaffold baseline configs for the detected stack, do a structured code review, guide a one-major-at-a-time dependency migration, fix project structure, or produce one aggregated health report across all of these. Triggers on phrases like clean this up, find security issues, remove dead code, check dependencies, lint, typecheck, run the tests, format, review my code, check the design, check logging, is this slow, migrate a dependency, fix the project structure, set up the project, audit everything, due diligence, or this looks like AI slop

Run any question, idea, or decision through a council of 5 AI advisors who independently analyze it, peer-review each other anonymously, and synthesize a final verdict. Based on Karpathy's LLM Council methodology. MANDATORY TRIGGERS: 'council this', 'run the council', 'war room this', 'pressure-test this', 'stress-test this', 'debate this'. STRONG TRIGGERS (use when combined with a real decision or tradeoff): 'should I X or Y', 'which option', 'what would you do', 'is this the right move', 'validate this', 'get multiple perspectives', 'I can't decide', 'I'm torn between'. Do NOT trigger on simple yes/no questions, factual lookups, or casual 'should I' without a meaningful tradeoff (e.g. 'should I use markdown' is not a council question). DO trigger when the user presents a genuine decision with stakes, multiple options, and context that suggests they want it pressure-tested from multiple angles.

Principles for building Unix-composable command-line tools in Go: stdout/stderr discipline, JSON and plain output modes, semantic exit codes, noun-verb commands, structured errors. Use when designing, building, or reviewing CLIs.

Small Safe Steps (S3): breaks work into 1-3h increments with zero downtime. Use when asking "how do I implement/migrate/refactor", "what steps to do X", "plan safe migration", or handling risky DB/API changes. Applies expand-contract pattern for migrations, refactorings, schema changes.

Maintain or create PROJECT_STATUS.md files at the repository root with the canonical structure (Executive Summary, Completed Components, In Progress, Next Steps, Known Issues, Notes). Use when starting or finishing work on a project, when the user asks to update or create PROJECT_STATUS.md, when a session involves significant changes that should be reflected in the project status file, or when reviewing project state before committing.

Build a detailed, phase-based implementation plan from research or a task description. Use when the user asks for implementation planning, phased rollout, or a concrete execution plan.

Test-driven development with red-green-refactor loop. Use when user wants to build features or fix bugs using TDD, mentions "red-green-refactor", wants integration tests, or asks for test-first development.

Spec-driven development with OpenSpec. Use when the user mentions OpenSpec, OPSX, spec-driven development, SDD, change proposals, delta specs, /opsx commands, or wants structured planning for features, migrations, refactors, or bug fixes.

Interact with Obsidian vaults using the Obsidian CLI to read, create, search, and manage notes, tasks, properties, and more. Also supports plugin and theme development with commands to reload plugins, run JavaScript, capture errors, take screenshots, and inspect the DOM. Use when the user asks to interact with their Obsidian vault, manage notes, search vault content, perform vault operations from the command line, or develop and debug Obsidian plugins and themes.

Use when the user wants agent-friendly vault artifacts or source ingest with text-first tools: Mermaid .mmd diagrams rendered with mmdc, Marp Markdown decks compiled to PDF/PPTX, Obsidian Excalidraw Markdown sketches, notebooklm-py summaries, yt-dlp talk metadata/captions, markitdown document conversion, or Makefile targets that wrap these workflows.

Fetch a URL and distill its content into the Obsidian wiki. If invoked from inside a project directory, the page lands directly in that project's folder (creating the project in the vault if needed). Otherwise it goes to misc/ and gains project affinity over time. Use this skill when the user says "/ingest-url <url>", "add this URL to the wiki", "ingest this link", "save this page", or pastes a URL and asks to save that link or page to the wiki.

Save the current conversation as a permanent, structured wiki note. Use this skill when the user says "save this conversation", "/wiki-capture", "capture this discussion", "file this conversation", "preserve this discussion", or wants to turn what was just discussed into lasting knowledge. The skill classifies the content, rewrites it as declarative knowledge (not a chat transcript), and places it in the correct vault category.

Create dynamic, queryable dashboard views of the Obsidian vault using Obsidian Bases — a native Obsidian feature that turns vault frontmatter into interactive tables, card galleries, and lists. Use this skill when the user says "create a dashboard", "vault dashboard", "show all X as a table", "dynamic view", "create a vault table", "build a content index", "show me all concepts/entities/projects", or wants a structured, auto-updating view of their wiki content. Requires Obsidian 1.8+ (Bases is a core plugin, no external install needed).

Unified wiki-history-ingest entrypoint for conversation/session sources. Use this when the user says "/wiki-history-ingest claude" or "/wiki-history-ingest codex", or asks to ingest agent history without naming the underlying skill. This router dispatches to the specialized history skill.

Autonomously research a topic via multi-round web search, synthesize findings, and file structured results into the Obsidian wiki. Use this skill when the user says "/wiki-research [topic]", "research X into my wiki", "file a deep dive on Z in the wiki", "autonomous wiki research on X", or wants comprehensive, web-sourced knowledge on a topic filed directly into their wiki.

Systematically discover synthesis opportunities across the Obsidian wiki — pairs or clusters of concepts that co-occur frequently across pages but have no synthesis page connecting them. Creates new synthesis/ pages that draw explicit cross-cutting conclusions. Use when the user says "synthesize my wiki", "create synthesis pages", "what concepts keep coming up together", "/wiki-synthesize", or after a large ingest when the vault has grown significantly.

Define, compose, and wire Google ADK agents with function tools and multi-agent orchestration. Use when writing agent definitions, adding Python function tools, building pipelines with SequentialAgent/LoopAgent/ParallelAgent, or designing router/delegation patterns. Trigger on: adk agent, LlmAgent, SequentialAgent, LoopAgent, ParallelAgent, AgentTool, function tool, output_key, output_schema, agent routing.

Bootstrap a Google ADK (Agent Development Kit) project from zero to a running local dev UI. Use when setting up a new ADK agent project, configuring Vertex AI credentials, scaffolding the agent folder, or troubleshooting ADK environment issues. Trigger on: adk setup, adk init, google-adk, vertex ai agent, uv run adk, adk web.

Use when using the AWS CLI against a corporate AWS account after SAML/OIDC federation (e.g. saml2aws, aws sso login), when STS fails with invalid XML or empty responses, when selecting profiles or regions for Terraform or CI/CD stacks, or when validating RDS engine versions with describe-orderable-db-instance-options in the target account and region.

Suggests manual context compaction at logical intervals to preserve context through task phases rather than arbitrary auto-compaction.

Implements Manus-style file-based planning to organize and track progress on complex tasks. Creates task_plan.md, findings.md, and progress.md. Use when asked to plan out, break down, or organize a multi-step project, research task, or any work requiring >5 tool calls. Supports automatic session recovery after /clear.

Select and implement the right test double with a default preference for fakes, stubs, and spies over mocks. Use when writing or refactoring tests, especially when users mention test doubles, mocking strategy, interaction assertions, or flaky tests caused by over-mocking.

Design robust A/B test experiments. Use when testing a new feature, validating a hypothesis, or optimizing conversion rates.

Use when asked to "define our core action", "North Star metric", "accruing benefits", "improve retention mechanics", "hierarchy of engagement", or "Sarah Tavel framework". Helps consumer products identify the actions and benefits that drive long-term retention. The Hierarchy of Engagement framework (created by Sarah Tavel at Benchmark) maps progression from core action to mounting loss.

Use when asked about "marketplace strategy", "chicken and egg problem", "liquidity", "two-sided market", "tipping a marketplace", "GMV growth", or "Sarah Tavel marketplaces". Helps founders and product leaders build defensible marketplace businesses by sequencing supply and demand. The Hierarchy of Marketplaces framework (created by Sarah Tavel / Benchmark) provides a progression from focused launch to market dominance.

Use when asked to "build habit-forming products", "Hooked model", "trigger action reward investment", "create sticky behavior loops", or "design habit loops". Helps design products that form unprompted user habits. The Hooked Model (created by Nir Eyal) explains how products create habits through Trigger, Action, Variable Reward, and Investment.

Use when asked to "shape up", "run a shaping session", "set an appetite", "scope a project without estimates", "betting table", or "ship in fixed cycles". Helps teams escape estimate-driven development and Scrum fatigue. The Shape Up method (created by Ryan Singer at Basecamp/37signals) uses fixed time boxes, variable scope, and collaborative shaping to ship meaningful work predictably.

Use when asked to "run an A/B test", "design an experiment", "check statistical significance", "trust our results", "avoid false positives", or "experiment guardrails". Helps design, run, and interpret controlled experiments correctly. Based on Ronny Kohavi's framework from "Trustworthy Online Controlled Experiments".

Design, organize, evaluate, benchmark, and improve portable Agent Skills. Use when creating a new skill, deciding whether a workflow deserves a skill, auditing a skill library, comparing a skill against baseline or prior versions, optimizing a skill description for better triggering, or retiring outdated skills across Claude, Codex, ChatGPT-compatible tools, and other Agent Skills-compatible environments.

Pragmatic property-based testing review and first-wave adoption for this listings-webapp monorepo (Jest + fast-check, unit tests only).

JS/TS/JSX toolkit with Bun awareness. Use when using Bun as a runtime, test runner, or package manager in JavaScript and TypeScript projects.

Use when a task should trigger a local completion notification through code-notify, or when configuring cross-tool notification behavior for Codex, Cursor, Claude, Gemini, or similar agent workflows. Verify the installed command or existing local integration before relying on it.

Guide AI adoption decisions in software development using Lean, Extreme Programming, and pragmatic simplicity. Use when evaluating AI tools, agents, workflows, prompts, automations, or rollout guardrails; deciding whether to automate or keep work manual; detecting tool sprawl, prompt graveyards, or low-value AI assets; or defining team standards for AI usage.

Execute an approved implementation plan phase by phase with verification. Use when the user asks to implement a plan file from thoughts/shared/plans.

Research and document the current codebase state without proposing changes. Use when the user asks to investigate existing behavior, architecture, or implementation details and capture findings.

Validate that an implementation plan was executed correctly. Use when the user asks to verify completion, compare code changes to a plan, or produce a validation report.