Open source · MIT · Any capable agent

Skills that make your agent grow

Three skills for AI coding agents. One evaluates the interface you built, one designs the research you're planning, and one quietly learns how you like to work. Research-grounded, agent-agnostic, free.

A folder of markdown, read on demand

No install step, no runtime, no API key. A skill is instructions and references your agent reads when the task calls for it — and ignores when it doesn't.

Drop it in

Clone the folder into ~/.claude/skills/ for every project, or .claude/skills/ for one. The agent reads the description, decides when it's relevant, and loads the rest only then.

Works with your agent

Written against generic capabilities, not one vendor's tools — Claude Code, Cursor, Codex, OpenCode, local models via Ollama, and any MCP-capable agent.

Grounded, not vibes

Every method cites its evidence. Two of the three carry the bloom- prefix, after Bloom's taxonomy — a series about agents that climb from recall toward judgment.

Three of them, so far

A sprout, a branch, a bloom. Each one is MIT licensed and lives on GitHub — read it before you run it.

bloom-ux-check

Persona-driven UX evaluation

GitHub

Reads your PRD, invents the people who'd actually use the thing, then drives your running app through each of their flows in a real browser. Most UX bugs are invisible in code review — a stale closure, a caret that lands one character off, a menu clipped at the viewport edge — and only surface when someone walks the path.

  • Personas from your PRD, each with a concrete flow to run
  • Nielsen's 10 heuristics, scored 0–4 with evidence
  • Accessibility audit — WCAG checks plus a keyboard-only walkthrough
  • Edge states — empty, extreme, loading, multi-tab, refresh

Grounded in 20+ peer-reviewed papers · needs browser automation · MIT

ai-research-methodologies

Research design, distilled from the literature

GitHub

A practitioner's guide to designing AI research, distilled from 25+ published Anthropic papers (2024–2026). It's deliberately about how to do the work — the frameworks, experimental designs, and measurement approaches — rather than what any one paper concluded. Findings age; method travels.

  • Safety & alignment — red-teaming, constitutional classifiers, sabotage arenas
  • Interpretability — concept injection, persona vectors, circuit tracing
  • Behavioral evaluation — automated pipelines, model-as-judge calibration
  • Six cross-cutting patterns that recur across all seven domains

25+ papers · 7 domains · SKILL.md plus deep references · MIT

bloom-skill-evolution

Skills that learn how you work

GitHub

A meta-skill — it works on your other skills. Most skills are frozen the day they're written, while you have a thousand quiet preferences you never spell out: dates as YYYY-MM-DD, summary first, and please edit the one sentence rather than regenerating the whole document. Bloom catches those leaks, acts on them next time, and eventually folds the durable ones back into the skill files themselves.

  • Capture — mines each run for the assumptions you never said out loud
  • Recall — applies what it learned by default, and tells you so you can override
  • Evolve — injects durable lessons into your skill files, at the point of action
  • Seed it — declare your own pain points and skip the wait for evidence

Four modes · keeps its memory in plain markdown you can read · MIT

Plant one

Clone any of the three into your skills directory and keep working — your agent reads the description and reaches for it when the moment fits. Nothing runs until it's relevant.

MIT licensed · no account, no API key · built in Montréal