Browse reusable Agent Skills, each with a clear purpose and practical guidance.
Incorporate review feedback into a plan doc. Reads review files, updates the source doc, adds a disposition table tracking every finding, and deletes review files. Supports multiple review rounds — detects existing disposition tables and adds the next round.
Orchestrate the full planning lifecycle — from input assessment through architecture, plan writing, review cycles with convergence, and sign-off. Designed for concierge/scheduler agents coordinating multi-agent planning work.
Generate a planning summary doc with review statistics, incorporation rates, convergence tracking, finding patterns, and document metrics across plan docs AND test harness docs. Supports multiple review rounds — each round gets its own section.
Surgically trim verbosity and within-doc duplication from a plan doc without losing fidelity. Cuts review residue, restated rationale, illustrative code that prose+signature already covers, redundant adjacent sections, and prose-then-table restatements. Preserves all API/interface specs, acceptance criteria, URP/EO/AA commitments, decision rationale where alternatives matter, and review disposition tables. Run after plan-incorporate, before plan-work-completion-signoff.
Decompose reviewed plan docs into implementation beads (epics + tasks) with correct dependencies. Reads plan docs, identifies components, and creates properly sized bd tasks.
Verify implemented plan docs against their actual code, add completion signoff metadata, and surface any gaps as new beads. Designed for scheduler/concierge agents coordinating multi-agent signoff after implementation is done.
Draft a short update message to a topic-owner reviewer after landing substantial dataset or chart changes on staging. The message lists the indicator changes with staging admin links, surfaces open design questions with option tables, and closes with a Chart Diff sign-off CTA. The output is markdown so the user can paste it into either Slack or a GitHub PR comment. Use after a dataset redesign or restructure when the iteration with the reviewer is back-and-forth and you need them to verify on staging before merge. Not for the canonical comms announcement — for that, see `data-updates-comms`.
Engineering operating model for teams where AI agents generate a large share of implementation output.