323 skills · sorted by relevance
Turn existing decision records, product requirements, specifications, and other documents into GSD planning files. Create a new planning setup or add requirements and phases to an existing one, while identifying conflicts and blocking writes when contradictions remain unresolved.
Build a paid advertising plan around business goals and financial constraints. Define channels, campaigns, audiences, budgets, creative, tracking, and experiments, then organize the work into phases with owners, success measures, and conditions for stopping or reversing changes.
Build and tune enemy behavior for action games, including targeting, movement, attacks, retreats, and boss phases. Organize decisions into explicit states, make attacks readable to players, and check behavior with repeatable tests and browser encounters.
Design, implement, tune, and test action combat for web games with clear attack and defense timing. Define reliable hit outcomes, give defensive moves distinct purposes, and verify that visual feedback matches combat results in repeatable scenarios.
Trace how AI tooling developed in a Git repository by identifying configuration file origins, monthly commit activity, major changes, and relevant releases. Produce a Markdown report that explains adoption phases and turning points using Git evidence.
Get reminders to compact conversation context, meaning summarize it for continued work, during long Claude Code sessions. A tool-call counter prompts consideration of manual compaction, with guidance on choosing useful breaks between task phases.
[omh] Decided cross-module refactor to phase: refactor planning - turn a decided boundary-changing refactor into a phased plan - reconnaissance, contracts-first phase order, per-phase verification and rollback, a files table, and an explicit approval gate before any edit. Use when the user says: refactor-plan, refactor plan, plan this refactor, plan the refactor, refactor planning, refactor phases, phased refactor, refactor in phases.
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Use when a milestone audit reported gaps and you want the fix phases created in one pass. Reads MILESTONE-AUDIT.md, groups gaps into phases, adds them to ROADMAP.md and offers to plan each one.
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Use when .planning/phases/ has accumulated directories from completed milestones. Shows a dry-run of what moves to .planning/milestones/v{X.Y}-phases/ and archives only after confirmation.
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Use when designing space missions, computing launch windows, optimizing trajectories, analyzing payload constraints, or planning mission phases and contingencies. Use when "mission design, launch window, trajectory optimization, mission timeline, mass budget, propellant budget, mission phases, porkchop plot, C3, gravity assist, low thrust, mission architecture, LEO, GEO, interplanetary, " mentioned.
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Writes implementation plans before coding: phases, plan files, specs, definition of done. Use when planning a multi-step feature or breaking a large refactor into phases.
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Audit a skill by running a paired probe — the same task once with the skill loaded and once without — segment both traces into goal-directed phases, align phases, and emit a SIP report (surface anchoring, template copy, excess planning, task recovery, off-task artifact). Use whenever a skill is created, modified, or proposed for retirement. Pass-rate is BLIND to most skill effects: CTA (arxiv 2605.11946v1) shows a single skill can produce 522 measurable behavioural changes across 49 tasks while pass-rate moves only +0.3%. Triggers: "audit this skill", "is this skill helping", "retire skill", "before shipping skill", "behavioural impact of skill X", or any skill review event.
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6-phase multi-agent pipeline with approval gates. Orchestrates Plan→Design→Implement→Review→Test→Deploy phases with explicit entry/exit criteria, phase-level agent assignment, and human-in-the-loop approval checkpoints.
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Coordinate a new feature from research and planning through tests, implementation, review, and commit. Assign phases to matching ECC agents, write failing tests before implementation, and pause for approval of the plan and before committing.
Plan market, competitor, consumer, or financial research with chapter outlines, data requirements, and chart plans. Then turn collected evidence into a structured Markdown report with comparisons, citations, and strategic implications, while clearly identifying missing data.
Turn an approved specification into pseudocode, a plain outline of program logic, and an architecture defining module responsibilities and API inputs and outputs. After user confirmation, proceed with implementation and unit tests based on those designs.
Review implemented features against their requirements, add missing tests, and check performance constraints. Produce documentation, a table linking requirements to code and tests, and evidence showing whether the feature is ready for deployment.
Capture lessons from completed tasks, train patterns from explicit examples, and consolidate accumulated learning into long-term storage. Use SONA and MicroLoRA adaptation tools for individual domains, then verify results in the appropriate stores.
Start AI Engineering from Scratch with a brief interview and placement quiz. Create a persistent LEARNING.md study plan recording your goals, starting phase, pace, and learning path for later sessions with the learn skill.
Build and review HyperFrames animations using motion recipes, scene templates, and transitions. Find guidance for seven animation runtimes, including GSAP and Three.js, and inspect existing scenes for timing gaps and inconsistent motion.
Organize API documentation work from endpoint discovery to OpenAPI specifications, developer guides, and tested code examples. The workflow also covers interactive documentation, documentation sites, and maintenance, helping select the phases and supporting skills needed for a task.
Plan AI application development by selecting relevant phases and companion skills. The workflow covers language model integration, retrieval from documents, agents, machine learning pipelines, monitoring, and security, with prompts and quality checklists.
Assess a new or existing AI project by examining its code, goals, and constraints. Recommend skills confirmed to exist in the available registry, then outline tasks and implementation phases with dependencies, completion criteria, and checks.
Plan enterprise customer onboarding from contract handoff through rollout and continued use. Provides four phases, readiness checklists, ownership checks, and adoption measures to address stalled communication, weak pilot usage, and declining engagement after launch.