claude-skills

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.

Build self-contained, single-page HTML reports and dashboards from data using Python + Jinja2 + Bootstrap 5 with Chart.js or Plotly. Use whenever the task is to produce an HTML report, data dashboard, metrics page, status page, or any single-file web artifact rendered from data — even if the user only says "report", "dashboard", "summary page", "visualize this", or hands you a CSV/DataFrame/JSON and wants it shown. Covers the render harness (Jinja2 autoescape discipline, passing data to JS safely with tojson), the Chart.js-vs-Plotly decision, Bootstrap 5 layout, and producing a portable single file. Pairs with markdown-converter (to ingest source docs) and is read by code-builder when it implements front-end report work.

Provider-agnostic CLI for targeted search and inspection of the Terraform Registry via its JSON API — any provider (AWS, GCP/google, Azure/azurerm, GitLab, OpenStack, Kubernetes, ...). Use to find modules by keyword, inspect a module's inputs/outputs/versions, or look up a resource type's attributes, WITHOUT web-scraping or dumping pages into context — it fetches the JSON payload, strips it locally, and returns only what you asked for. Caches every payload as a provenance-stamped snapshot so repeat calls are offline and token-free. Use whenever you need accurate, current Terraform module/provider facts for writing or reviewing IaC.

Heuristic usability and accessibility audit of existing web pages — load a rendered page, evaluate it against Nielsen's 10 usability heuristics + WCAG 2.2 (A/AA) + responsive behavior + the page's own goal, and return prioritized findings with a concrete fix for each. Use whenever the task is to review, audit, critique, or "find usability/UX problems" on one or more web pages, do an accessibility (a11y) check, assess responsiveness, or propose usability fixes — even if the user just says "is this page any good", "what's wrong with this UI", "make this more usable", or hands you a URL/screenshot. This is a review/audit method, not a design-decision debate (that's design-council) and not functional testing (that's webapp-testing). Read by the ux-auditor agent, which renders each page with a browser driver and fans out one auditor per page.

Retrieve up-to-date documentation and code examples for any software library, framework, or API via the Context7 service, using the `c7search` CLI. Use this skill whenever looking up how to use a library or framework, finding current code examples for a specific API or feature, verifying the correct signature or usage of a library function, or checking library behavior that may have changed since the training cutoff — even when the user doesn't name Context7 or c7search explicitly. `c7search` is a single Go binary wrapping Context7's public v2 API with on-disk caching, retries, and predictable exit codes.

Convert documents and files to Markdown using markitdown. Use when converting PDF, Word (.docx), PowerPoint (.pptx), Excel (.xlsx, .xls), HTML, CSV, JSON, XML, images (with EXIF/OCR), audio (with transcription), ZIP archives, YouTube URLs, Outlook (.msg), or EPubs to Markdown format for LLM processing or text analysis.

Convert images (PNG, JPG, etc.) to WebP format. Use when saving screenshots or images for web projects, Hugo sites, or any context where WebP is preferred.

Resize and format images for social media platforms. Use when the user needs to prepare an image for LinkedIn, Bluesky, Reddit, Hacker News, or other social platforms. Triggers on "resize for linkedin", "image for social", "prepare image for posting", "social media image", "resize for bluesky", or any request to optimize an image for a specific social platform.

Construct, review, score, and rewrite written content so it reads like a specific human wrote it, not an AI. Use this skill whenever the user wants to: write or draft prose for humans (docs, README, runbook, ADR, PR/commit message, blog post, LinkedIn post, email, Slack message, or a spoken explainer/tutorial video script); humanize or de-slop AI-generated text; check whether writing "sounds like AI"; review a draft for AI texture; rewrite content in their own voice; score a draft for authenticity or clarity; or tighten and sharpen prose. Also trigger on "humanize", "make it sound human", "sounds like AI", "does this sound like AI", "voice check", "review my draft", "rewrite in my voice", "tighten this up", "edit for clarity", "video script", "explainer script". Auto-detects content type and applies channel-specific rules. Defaults to a neutral, factual voice and never invents specifics to add texture. For deliberately persuasive marketing copy (ads, hooks, LinkedIn/Bluesky growth posts, video titles and thum

Write, punch up, and review marketing copy that stops the scroll and converts, while still sounding like a real person. Use this skill whenever the user wants to: write a LinkedIn post, X/Twitter post, Bluesky post, cold or marketing email, ad, landing-page section, headline, hook, lead magnet, or YouTube packaging (video title, thumbnail text, description hook, end-screen/CTA, community post); "punch up" or "make this convert" or "make this land"; sharpen a CTA; or review copy for stopping power and conversion. Also trigger on "hook", "scroll-stopper", "make it punchy", "marketing copy", "sales copy", "cold email", "ad copy", "landing page copy", "lead magnet", "bluesky", "youtube title", "video title", "thumbnail", "make this convert". Deploys persuasion patterns (hooks, frameworks, one strong CTA) on purpose, reads WRITING_CONTEXT.md for voice and approved facts, and never fabricates specifics. For neutral, credibility-first writing (docs, README, plain explanation, or a teaching/explainer video script) us