AI Skills
AI Skills we built, yours to install.
Focused AI skills we use in our own work, packaged so you can add just the ones you want — one command each.
Each skill is its own plugin. Add the marketplace once, then install only the skills you want.
ikigai-discovery
Guides you through a rigorous Ikigai interview to find your reason for being, then produces a polished report.
- ikigai
- purpose
- coaching
- career
- self-discovery
refine-epic
Interactively shape a well-formed Agile Epic through an SME interview — a falsifiable Benefit Hypothesis, measurable Business Outcomes, predictive Leading Indicators, quantified NFRs, and Out-of-Scope — then hand back a structured result and a markdown brief for any backlog tool.
- epic
- agile
- safe
- backlog-refinement
- benefit-hypothesis
- business-outcomes
- leading-indicators
- product-management
- lean-startup
- mom-test
build-a-great-elite-question
Interviews you to turn a real founder situation into a sharp, well-diagnosed question for Dan Martell's Elite group, framed as "I'm Here" / "I want to be there" / "My bottleneck is...", and produces a one-page prep sheet.
- dan-martell
- elite
- coaching
- founder
- saas
- question-prep
- hot-seat
decompose-epic
Decomposes a refined Agile Epic into skeleton Features and Stories through an SME interview — Mom-Test behavior mining, KJ affinity clustering, per-feature benefit hypotheses, a two-way outcome coverage gate, and an MVP partition — rendered as a feature map for any backlog tool, or materialized as a backlog store of markdown files (optional Miro Card story-map mirror). Optional Miro card-wall mode. Pairs with refine-epic — run that first on a raw epic.
- epic
- features
- user-stories
- decomposition
- story-mapping
- affinity-mapping
- kj-method
- product-management
- agile
- mvp
- sme-interview
- miro
refine-feature
Takes one skeleton feature from the backlog store to build-ready through an SME interview — four-risks triage (fast lane / full pass / discovery-brief exit), a behavior-change hypothesis, a quantified success signal, dual-test feature acceptance criteria, critical-few NFRs, and shallow story-set curation with a nominated first slice. Writes back to the backlog store in place (optional Miro Card mirror). Pairs with decompose-epic — run that first to create the store.
- feature
- feature-refinement
- backlog-refinement
- acceptance-criteria
- benefit-hypothesis
- success-signal
- nfr
- story-splitting
- discovery
- product-management
- agile
- sme-interview
- backlog-store
refine-story
Takes one story Card from the backlog store to ready-for-sprint-planning through an SME interview — a real happy path, Example-Mapping rule/example mining with Break-the-Model refutation, an adaptive Confirmation (checklist by default; example table or Given-When-Then only when the cases demand it), INVEST E/S/T engines with honest splitting, and the Demo-and-Read-Back gate. The Card line never fattens. Writes back to the backlog store in place (optional Miro Card mirror). Pairs with refine-feature — run that on the feature first.
- story
- user-stories
- story-refinement
- backlog-refinement
- acceptance-criteria
- example-mapping
- given-when-then
- invest
- story-splitting
- product-management
- agile
- sme-interview
- backlog-store
statusline
Installs a two-line Claude Code status line — model + reasoning effort, directory, git branch, session name, a color-coded context-usage bar, session cost and duration, and 5-hour/7-day rate-limit usage. Install the plugin, then say "set up the status line" — the skill configures it for your platform (Windows, macOS, or Linux) and verifies it works. Requires Python 3.
- statusline
- status-line
- status-bar
- prompt
- context-usage
- rate-limits
- session-cost
- git-branch
- claude-code
- terminal
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