Create Excalidraw diagram JSON files that make visual arguments. Use when the user wants to visualize workflows, architectures, or concepts.
Comprehensive Terragrunt 1.x skill for generating, validating, reviewing, and debugging Terragrunt configurations (root.hcl, terragrunt.hcl, terragrunt.stack.hcl, units, stacks, catalogs) across AWS, Azure, and GCP. Use this skill whenever the user mentions Terragrunt, terragrunt.hcl, root.hcl, stack files, units, HCL orchestration of OpenTofu/Terraform, remote state DRY configuration, run --all, dependency blocks between modules, or asks to scaffold/lint/diagnose multi-environment IaC layouts — even if they don't say "Terragrunt" explicitly but show Terragrunt HCL.
Orchestration playbook for driving the development agent fleet (code-builder, fact-verifier, coherence-checker, code-reviewer) through a build → verify → cohere → review → commit pipeline with explicit, deterministic hand-offs. Use when implementing a non-trivial change where correctness and fact-accuracy matter and you want the steps to actually run in order rather than hoping the orchestrator delegates. Also use when deciding which fleet agent to dispatch for a task, or when wiring a new agent into the pipeline. Covers how to pass context between agents, the fact-gate and coherence-gate before commit, the loop-back semantics on each gate, and handing off to commit-pr.
Use when running Linear.app operations from the command line — creating, updating, archiving, listing, or filtering issues, setting project milestones, or wiring blocked-by relations via the `linearis` CLI (binaries `linear` and `linearis`, JSON output) instead of an MCP or the web UI. Triggers on any Linear issue/project/milestone task in a terminal, and on syncing Kevin's Ogham roadmap with its shared-memory database. Covers generic install/auth setup, the CLI's sharp edges, and the pre-loaded Ogham project IDs. Not a full reference — that's `linear <cmd> --help`.
Tailor and polish a CV/resume to maximise its score in applicant tracking systems (ATS), covering both keyword/JD-matching screeners and modern LLM-rubric scorers (which often take no job description at all). Use this whenever the user wants to optimise, tailor, rewrite, or "ATS-proof" a CV or resume, asks why their resume isn't getting through screening, wants a keyword/gap analysis against a job description, wants to remove AI-generated texture from a CV, or wants to check whether a CV parses cleanly. Trigger even if the user only supplies a CV (with or without a job description), or asks to "make my resume match this job" or "pass the ATS". Do NOT use for writing a CV from a blank page with no material to work from, or for generic career advice unrelated to a document.
Playbook for getting external data into the repo deterministically — fetch a web page, doc, or API/registry result once, extract and normalize it, and cache it as a pinned, provenance-stamped artifact that agents read instead of re-fetching live. Use when you need facts an LLM or agent will rely on (docs, library/API behavior, Terraform/provider registry data, prices, schemas) to be reproducible and offline-readable rather than varying per run. Also use when deciding whether to snapshot vs. fetch live, and which extractor to use for a given source. Pairs with fact-verifier (snapshots become its tier-1 sources) and markdown-converter.
Deterministic, offline-first lookups over Azure Policy, Policy Initiative, and RBAC Role metadata sourced from AzAdvertizer's CSV exports — including the cross-references that exist nowhere else in one place (which roles a policy uses, which initiatives include a policy, which policies a role is used by). Use whenever you need accurate current facts about an Azure built-in policy, initiative/policy-set, or RBAC role — definitions, effects, allowed effect values, categories, role actions/dataActions, or the policy↔role and policy↔initiative relationships — for writing or reviewing Azure IaC, governance, or landing-zone work. Fetches each CSV once into a provenance-stamped cache and answers queries offline; stdlib only, JSON envelope output. Pairs with terragrunt-skill, azure-architect, and fact-verifier (snapshots become tier-1 sources).
Generate and edit images using Google's Nano Banana Pro (Gemini 3 Pro Image) API. Use when the user asks to generate, create, edit, modify, change, alter, or update images. Also use when user references an existing image file and asks to modify it in any way (e.g., "modify this image", "change the background", "replace X with Y"). Supports simple prompts, style presets (cinematic, film, fashion, studio), JSON-configured camera/lighting/composition parameters, photorealistic enhancement, aspect ratios, and WebP output. This is the default image generation skill. DO NOT read image files first - use --input-image parameter directly.