1.4K skills · sorted by relevance
Relentless delivery mode for complex, multi-step tasks. Use when: multi-file features, unclear root cause debugging, multi-angle research, architecture decisions with trade-offs. Activates parallel subagent dispatch and strict verification.
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Evidence-first multi-step research with bounded parallel collection, contradiction checks, and cited synthesis.
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Grind through ready beads serially in priority order, one at a time in the current worktree, without stopping between issues. Use when asked to work the beads queue, grind the backlog, keep working through ready issues, or make unattended progress overnight. For concurrent worktrees instead, use beads-parallel-grinding.
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Implements multiplicity control for confirmatory clinical trials using graphical procedures (Bretz-Maurer-Hommel), gatekeeping (parallel, serial, mixed), Hochberg/Hommel/Holm with PRDS, and the closed-testing principle (Marcus-Peritz-Gabriel; Goeman 2021 admissibility). Covers FDA Multiple Endpoints Final Guidance (October 2022), graphical procedures via R gMCP, primary + key-secondary + subgroup hierarchies, and FWER vs FDR distinction. Use when designing the multiplicity strategy for confirmatory trials with multiple primary or key secondary endpoints.
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Download large datasets from NCBI efficiently using EPost, history server, batching, rate limiting, and retry logic. Use when bulk-fetching tens of thousands of sequences, pulling all results of a large ESearch, designing reproducible pipelines, comparing E-utilities to NCBI Datasets v2 CLI, or implementing checksum-validated downloads. Encodes WebEnv TTL (~8h), EPost 200-ID limit, retmax caps, parallelization design, and integrity verification.
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Coordinate staged or parallel jobs across coding-agent runtimes and subagents: routes by capability/risk, isolates writers in worktrees, arbitrates results. NOT for a single-agent task (mk:cook).
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Fast parallel codebase scouting — searches many directories at once, returning a file map + architecture fingerprint. Use before planning or cross-dir work. NOT for deep single-file reads.
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Auto-decompose large tasks into DAG-compatible parallel subtasks
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10-team parallel deep analysis with cross-verification for any topic, repository, or technology. Use when user invokes /research or asks for comprehensive research.
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Aggregate parallel agent results into concise output
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Parallel code verification using multiple models with severity classification
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Multi-angle release quality verification using parallel expert review teams
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Generate 4-6 parallel design mockups for rapid visual comparison — adapted from gstack /design-shotgun pattern
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Autonomous driver loop for ExploitHunter vulnerability-research campaigns. Runs breadth-first waves of research lanes on rented hosts with no per-request human approval, steers lanes via the shared Investigation Graph (demoting dead ends, respawning on high-signal priorities), saturates host capacity, and enforces wave/budget/safety discipline. Use when driving a multi-target bug-hunting campaign, launching parallel research lanes, or resuming a campaign from a state snapshot.
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Run the extended Corpo Net range suite across many models on one remote host with parallel isolated range instances, local Langfuse recording, bounded spend, and verified teardown. Use when asked to run, rerun, or scale a Corpo Net campaign; use run-ephemeral-hosts for the host contract and manage-remote-resources for billable resource ownership.
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Subagent that scans CopilotKit + AG-UI releases shipped in (and just after) the report window, diffs the git tags to extract what each release actually fixed, and returns a fix-map the weekly-report writer cross-checks against open issues. Catches issues that are quietly already-fixed in a shipped release, and is the authoritative source for the "current release vX.Y.Z" version claims. Invoked by weekly-report (spawned in parallel with the Discord/GitHub pulls); runs as a general-purpose subagent so the orchestrator's context stays small.
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Execute a swarm plan wave by wave with filesystem isolation via git worktrees. For each wave, main context decides the scenario recipe + implementation contract for every task, then spawns one swarm-worker per task in parallel. Each worker executes its recipe and reports JSON status. Worktree merge-audit verifies write-set discipline before changes land on main. Aborts remaining waves on any audit or task failure.
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Decompose an approved spec into a dependency-ordered swarm plan — one task per component, each with an explicit write_set. Produces `.claude/state/swarm/<slug>.json` with tasks + waves. The wave scheduler guarantees pairwise-disjoint write_sets within each wave so parallel dispatch is provably conflict-free.
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Remove signs of AI-generated writing from text. Use when editing or reviewing text to make it sound more natural and human-written. Based on Wikipedia's comprehensive "Signs of AI writing" guide. Detects and fixes patterns including: inflated symbolism, promotional language, superficial -ing analyses, vague attributions, em dash overuse, rule of three, AI vocabulary words, negative parallelisms, and excessive conjunctive phrases. Credits: Original skill by @blader - https://github.com/blader/humanizer
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Decompose an MVP vision into a sprint manifest — a prioritized feature list where every feature carries explicit done-criteria (a done-record reference, named edge tests, and a wiring test). Produces the sprint manifest that `sprint-oracle` checks for completeness. Use when planning a sprint of parallel work (Slice A of the sprint-mode epic). Not a workflow phase; the manifest it writes is the completeness contract a sprint is held to.
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Coordinate complex engineering work across bounded subagents while protecting the main context, preventing edit conflicts, and measuring whether delegation was worthwhile. Use when the user asks to delegate, parallelize, orchestrate, use subagents, fan out a review, or handle a large task with independent workstreams; do not use for small, tightly coupled, or inherently sequential tasks.
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Review the changes since a fixed point (commit, branch, tag, or merge-base) along two axes — Standards (does the code follow this repo's documented coding standards?) and Spec (does the code match what the originating issue/PRD asked for?). Runs both reviews in parallel sub-agents and reports them side by side. Use when the user wants to review a branch, a PR, work-in-progress changes, or asks to "review since X".
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Use when facing 2+ independent tasks that can be worked on without shared state or sequential dependencies
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Generate multiple radically different interface designs for a module using parallel sub-agents. Use when user wants to design an API, explore interface options, compare module shapes, or mentions "design it twice".
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