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Runs a multi-step procedure as a Python DAG, so ordering, branching and retries are enforced by the runner rather than described in prose a model can generate past. Use for "run these steps in order and retry the flaky one until the check passes", "build a pipeline that fetches, validates, then skips the upload when nothing changed", "make sure these steps cannot be skipped", "resume from where it broke instead of redoing the expensive early stages", "run these independent calls at once and merge the results", or any procedure of 3+ steps with branches, input contracts, or side effects that must not block the critical path. Primitives are depends_on, when=, validate=, retry_until=, detached= and journal_path=. Not for a single sequential call, for steps needing reasoning between them that no predicate captures, or for async and distributed work. To audit whether one verification check can actually go red, use gating. To fan work out across many subagents, use a dynamic workflow.

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Authors and caches a personalized container environment from a Dockerfile- like spec, as a single layer or as a composition of independently cached layers. Use when the user mentions "container layer", "Containerfile", "custom container", "cache my installs", "composable layers", "uv shim", or wants package installations, skills and environment config to survive an ephemeral session. Also for snapshotting, restoring or rebuilding that environment, and for capturing ad-hoc installs into a reproducible spec.

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Decide and implement how to support a new LLM provider or agent engine in AWF - either as a proxied provider (api-proxy adapter) or a direct-API engine (domain allowlist only), e.g. Cursor, Aider, or any tool that calls its own endpoint.

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Debug the AWF firewall by inspecting Docker containers (awf-squid, awf-agent), analyzing Squid access logs, checking iptables rules, and troubleshooting blocked domains or network issues.

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The parallel fan-out primitive. Routed to by any skill or command that splits work across independent units and dispatches an agent per unit — subagent-driven-development, /sprint, parallel /review. It owns the dispatch/collect/funnel discipline: bound concurrency, isolate units, collect every result, dedupe overlap, and funnel through finding-triage then the read-only verdict-aggregator. Raw agent output is never consumed before the funnel runs; an agent that errors records its unit as incomplete without corrupting the batch.

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Create, manage, and cleanup git worktrees with Claude Code agents across all projects. USE THIS SKILL when user says "create worktree", "spin up worktrees", "new worktree for X", "worktree status", "cleanup worktrees", "sync worktrees", or wants parallel development branches. Also use when creating PRs from a worktree branch (to update registry with PR number). Handles worktree creation, dependency installation, validation, agent launching in Ghostty, and global registry management.

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Generate cross-platform install scripts for any software or library — a standalone install.sh runnable via a curl/wget one-liner with OS, arch, and package manager detection. Don't use for Dockerfiles, CI/CD pipelines, or one-off shell scripts.

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Build and verify a HyperGrok trading desk from the pinned public release. Use for first-run setup, repair, or a readiness check. Starts with a live zero-key Opening Bell, installs the seven role profiles and seventeen shared skills, prepares the Trading Floor, and returns an evidence receipt. Read-only by default; never requests a wallet or places an order.

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Find new skills on GitHub or check a specific skill before installing. Use when the user wants to search for skills, evaluate a skill URL, check overlap and security risk before installing, or compare a local skill against alternatives. Trigger with '/janitor-discover'.

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Migrates Neo4j driver code and Cypher queries from older versions (4.x, 5.x) to current (2025.x/2026.x, Cypher 25). Covers Python, JavaScript/Node.js, Java, .NET, and Go drivers — package renames, removed APIs, version requirements, diff-ready fixes. Also handles Cypher syntax migration — QPE paths, CALL subqueries, id() → elementId(), PERIODIC COMMIT → CALL IN TRANSACTIONS, and all Cypher 25 removals. Does NOT write new Cypher queries — use neo4j-cypher-skill. Does NOT cover DB administration or server ops — use neo4j-cli-tools-skill. Does NOT provision new Neo4j instances — use neo4j-getting-started-skill.

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Prepare a meeting-ready alternative-investments brief for a client, household, IRA, trust, or named account before a review — pulls the alts book from iCapital or Addepar (positions, NAV vs. commitment, called and unfunded amounts, contributions/distributions/subscriptions/redemptions, lockups, and the platform's own IRR/TVPI/DPI/RVPI) and sits it beside the liquid book and IPS target from the connected portfolio system (Orion Connect, Addepar, or Envestnet), then flags cash needed for capital calls, alts % vs. target, concentration, unfunded concentration, and stale as-of data. Cites the source platform on every figure and keeps disclosures on the page. Does not recommend funds, forecast NAVs/IRRs, or simulate allocation changes. Triggers on "alts brief", "/alts-brief", "prep the [client]'s alts review", "what's going on with [client]'s PE/private investments", "how's [client] doing on their alts", or any request to review a household's alternative investment positions before a meeting.

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Product Hunt launch optimization with specific specs, timing, and gallery strategy. Covers taglines, gallery images, maker comments, and launch day tactics. Use for: product launches, startup launches, side project launches, Product Hunt optimization. Triggers: product hunt, ph launch, product hunt launch, launch strategy, product launch, startup launch, product hunt tips, product hunt gallery, ph optimization, launch day, product hunt maker

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Expert guide for defending AI applications against prompt injection, jailbreak attacks, indirect injection via tool outputs, and data exfiltration through tool calls — input sanitization, output filtering, canary token detection, and layered defense architecture / Panduan ahli pertahanan aplikasi AI terhadap prompt injection, serangan jailbreak, injeksi tidak langsung via output tool, dan eksfiltrasi data melalui tool calls — sanitasi input, filtering output, deteksi canary token, dan arsitektur pertahanan berlapis.

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Learns the words a user habitually writes to their coding agent that make its answers worse ("be 100% sure", "don't assume", "full review", "are you sure?", "all fine, right?"), from what they actually typed in their recent Claude Code sessions. Shows what each one does, what went wrong after it, and what to say instead, then writes the habit words block that maps each word to the checks it should trigger, so the checks run without the words. Use when asked to learn, read, refresh or review the user's habit words or prompts, "which of my words make you worse", from setup-first-pass's profile step, or when the start-of-session check says the habit words are due.

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Index a repository (local path or git URL) into Cognee's code graph and answer exact structural queries with no LLM calls: callers, dependencies, impact of changing a symbol, call paths, endpoints, re-index changes.

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Splits a coding task into independent parallel agent workstreams with disjoint write sets, explicit inputs and outputs, dependency ordering, and a merge/review plan. Use when several subtasks can proceed without shared mutable state. Not for tightly coupled edits, one failing root cause, or parallel changes to the same files.

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Holistic cross-GDD consistency and game design review. Reads all system GDDs simultaneously and checks for contradictions between them, stale references, ownership conflicts, formula incompatibilities, and game design theory violations (dominant strategies, economic imbalance, cognitive overload, pillar drift). Run after all MVP GDDs are written, before architecture begins.

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Make a phone call to book an appointment or reservation. Checks calendar first, gets explicit consent before dialing, discloses AI identity on the call, and adds the booking to calendar when done.

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Orchestrating multi-session parallel execution via Claude Code Agent Teams API and Codex CLI Subagents — launch, manage, coordinate concurrent tasks. Use when parallel work is needed.

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Redesign a lesson to centre students' cultural backgrounds, community knowledge, and lived experience. Use when making curriculum relevant and inclusive for diverse student populations.

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Design a fact-checking protocol for AI-generated text, extending SIFT with AI-specific adaptations for hallucination detection. Use when students need to verify AI claims and citations.

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When the user wants to create or optimize in-app paywalls, upgrade screens, upsell modals, or feature gates. Also use when the user mentions "paywall," "upgrade screen," "upgrade modal," "upsell," "feature gate," "convert free to paid," "freemium conversion," "trial expiration screen," "limit reached screen," "plan upgrade prompt," "in-app pricing," "free users won't upgrade," "trial to paid conversion," or "how do I get users to pay." Use this for any in-product moment where you're asking users to upgrade. Distinct from public pricing pages (see cro) — this focuses on in-product upgrade moments where the user has already experienced value. For pricing decisions, see pricing.

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Diagnose and fix mini-program failures through reproducible, evidence-driven root-cause analysis across source, state, asynchronous timing, caches, mock data, build versions, platform behavior, devices, cloud calls, and external services. Use when users report white screens, crashes, stale or jumping values, missing data, intermittent behavior, permission failures, performance stalls, device-only defects, API or cloud-function errors, source-versus-build mismatches, or regressions whose cause is not yet established. Freezes the observation environment, ranks competing hypotheses, runs discriminating checks, creates a failing regression test before repair, covers analogous states, and never hides a known failure with longer waits, swallowed errors, forced success, or cosmetic patches.

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Implement bounded features and fixes in existing or new mini-program codebases while preserving repository rules, user-owned changes, approved behavior, assets, framework conventions, and evidence boundaries. Use when users ask to write or modify mini-program code, implement a confirmed specification or architecture, add a scoped feature, remove behavior semantically, update internal documentation required by a change, or carry out a well-defined small fix that does not require root-cause investigation. Establishes a baseline and change boundary, applies test-driven small steps, distinguishes source from generators and build artifacts, verifies the affected contract, and never reports source completion as device validation, formal acceptance, upload, or release.

日本語の概要は準備中です。原文の説明を表示しています。