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fable-discipline
fable-discipline enthält 9 gesammelte Skills von petrkindlmann, mit Repository-Berufsabdeckung und Skill-Detailseiten auf SkillsMP.
Skills in diesem Repository
Use when orchestrating multi-agent or long-running work — spawning sub-agents, fanning out parallel tasks, delegating subtasks, running build-then-review loops, or deciding which model tier should do a piece of work.
Use when building a self-contained shippable artifact from scratch or a major rev — an MCP server, a CLI, a library/SDK, a skills pack, a plugin — anything that gets packaged and released rather than merged into an existing app.
Use when maintaining project state across sessions — writing or reading a STATE.md / MEMORY.md, recording what was learned, resuming interrupted work, or deciding what to persist for future sessions.
Use when building or redesigning a product UI/visual system — creating a design system, writing or following a DESIGN.md contract, generating screens in a design tool (Stitch/Figma) then porting to code, or doing a branded visual redesign.
Use during hands-on execution of any coding or agent task — especially when following multi-item instructions, deciding how much to deliberate, about to claim something is done, or producing output at risk of reading as generic AI slop.
Use when building or running an automated measurement/audit/reporting pipeline that scores many subjects on a recurring basis and reports to stakeholders — a compliance monitor, a benchmark matrix, a metrics dashboard, a scheduled scan-and-report.
Use when planning, building, or reviewing a software milestone/release/version — any task framed as "ship vX", "build the next milestone", "plan and execute a feature set", "do a research + build pass", or "review what we shipped".
Use for empirical investigation / analysis / forensics work where the goal is a defensible finding, not a feature — analyzing a dataset, testing a hypothesis, reverse-engineering an artifact, building an evidence-backed report or paper.
Use when diagnosing a reported failure, regression, or unexpected behavior — before proposing any fix, when a fix attempt has just failed, or when the evidence in hand is secondhand (a user report, a log summary, an assumption about what a library or platform does).