From kadai
Autonomous executor for kadai-tracked work. Picks up where kadai-writing-plans leaves off — walks the picked story's plan task-by-task, dispatches an implementer subagent for each ### Task block, runs spec + code-quality reviews per task, marks tasks done as it goes. Pauses for human review at story boundaries. When an implementer reports BLOCKED with reason `needs-feature: <desc>`, surfaces a prompt to plan a fast-follow-up unblocker via kadai-brainstorming, executes the unblocker, then resumes the original story. State persisted in `.kadai/runner.json`; resumable across sessions via `kadai run --resume`.
How this skill is triggered — by the user, by Claude, or both
Slash command
/kadai:kadai-runnerThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
Use after `kadai-writing-plans` (or whenever a story is picked and has a plan.md). Combines `superpowers:subagent-driven-development` (per-task implementer + reviews) with kadai-specific orchestration (state machine, fast-follow-up unblocking).
Use after kadai-writing-plans (or whenever a story is picked and has a plan.md). Combines superpowers:subagent-driven-development (per-task implementer + reviews) with kadai-specific orchestration (state machine, fast-follow-up unblocking).
User says "run it" / "execute the plan" / "kadai run":
.kadai/runner.json (via kadai run --status) — what state are we in?idle and a story is picked → start.paused-needs-feature → present the unblocker decision (see below).paused-review → present the story summary, ask "advance to next?"paused-blocked → present the blocker, ask user how to proceed.For each ### Task N in the picked story's plan.md:
in_progress (kadai set-status TASK-XXX in_progress).superpowers:subagent-driven-development's implementer-prompt. Pass: full task text, surrounding context (story title, spec, relevant prior tasks).DONE / DONE_WITH_CONCERNS / BLOCKED / NEEDS_CONTEXT.DONE: dispatch spec compliance review subagent; on review pass, dispatch code quality review subagent; on quality pass, mark task done. (Same two-stage review as the upstream pattern.)BLOCKED: parse the reason. If it starts with needs-feature: → see "Fast-follow-up flow" below. Otherwise → write state paused-blocked and surface the blocker.NEEDS_CONTEXT: provide the missing context if you have it (story spec, surrounding plan tasks); otherwise pause.After all tasks: set the story review, write state paused-review, summarize, ask user to advance.
When an implementer says BLOCKED: needs-feature: <description>:
paused-needs-feature. The original story + task are pushed onto the pausedStack.STORY-XXX hit a blocker on Task N.
The implementer believes a fast-follow-up feature is needed:
Title: <suggested>
Why: <description>
Plan it now and resume after? [Y/n/skip]
kadai-brainstorming in fast-follow-up scope:
mvp phase, propose 1-3 stories max.recordDependencyEdge(STORY-XXX, FEAT-NEW) so the original story's frontmatter shows dependsOn: [FEAT-NEW].kadai-writing-plans for the new feature. First story of the unblocker auto-picks.kadai run (it will detect the new picked story and execute the unblocker).blocked (kadai status), runner state → idle, tell user manual intervention is needed.bypass.log entry noting the skip reason.When the unblocker finishes (story-done AND pausedStack is non-empty), the runner automatically:
kadai pick STORY-XXX)/kadai-run (or kadai run --resume) consults .kadai/runner.json:
idle + story picked → start freshrunning → resume the loop (means a prior session crashed; pick up where state.currentTaskId says)paused-* → present the appropriate decision UIIdempotent: re-running while still in paused-needs-feature re-presents the prompt; no auto-advance.
kadai run process at a time. (Easy follow-up: pid file.)kadai run (informational; just prints state) and kadai run --status.kadai plan compose <epic-id> — render all per-story plans as one document, e.g., before kicking off a fresh run.npx claudepluginhub fintanislost/kadai --plugin kadaiProvides behavioral guidelines to reduce common LLM coding mistakes, focusing on simplicity, surgical changes, assumption surfacing, and verifiable success criteria.
Searches, retrieves, and installs Agent Skills from prompts.chat registry using MCP tools like search_skills and get_skill. Activates for finding skills, browsing catalogs, or extending Claude.
Creates, edits, and optimizes skills for Claude Code, including drafting, evaluating with test prompts, iterating on performance, and improving skill descriptions for better triggering accuracy.