'opa' 검색 결과

Review a Go design spec before implementation begins - dispatch a subagent that checks a design doc for completeness, consistency, and idiomatic Go (simplicity, small consumer-defined interfaces, explicit wrapped errors, context propagation). Use when a user has a Go spec to review, asks "is this spec ready?", or is about to implement from a written design. Distilled from spf13/go-skills and adapted to this repo's conventions.

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Cuts the next release of the unified Prisma CLI: bumps the root package.json version (on the Prisma 8 RC line: 8.0.0-rc.N → rc.N+1), propagates it to every lockstep workspace package, and opens a PR titled "chore(release): bump to <next-version>". When the maintainer merges the PR, the `Publish to npm` workflow ships @prisma/cli-engine and @prisma/cli under dist-tag `latest` (RC versions get a pre-release GitHub Release). Use when a maintainer asks to "cut the next RC", "cut the next release", "bump to the next version", "open a release PR", or "prepare a publish PR".

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Wire Prisma Next into the project's build system with the right build-tool plugin — Vite today via @prisma-next/vite-plugin-contract-emit (Vite 7 / 8); Next.js / Webpack / esbuild / Rollup / Turbopack are named as gaps rather than fabricated. Always offers the Vite plugin proactively when the project is using Vite. Use for vite plugin, vite-plugin, vite.config.ts, prismaVitePlugin, contract emit on save, HMR, hot reload contract, dev server, Next.js plugin, next plugin, withPrismaNext, webpack plugin, esbuild plugin, rollup plugin, build integration, dev server plugin, vite 7, vite 8.

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Code obfuscation analysis and deobfuscation playbook. Use when reversing binaries protected by junk code, opaque predicates, self-modifying code, control flow flattening, VM protection, or string encryption.

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Systematically extract hidden system prompts, core directives, and invisible context intentionally concealed within Large Language Model (LLM) applications. This skill utilizes targeted linguistic engineering and boundary manipulation to bypass prompt opacity.

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Classify a proposed code change as Leaf Node or Core Code to decide how much AI usage and review rigor is appropriate. Use this skill whenever the user asks "is this leaf or core", "what kind of change is this", "should I vibe code this", "can AI write this", "how rigorous should the review be", or before starting any non-trivial change. Also useful as a sub-step inside other workflows (planning, PR prep, code review) when classification matters. Asks 6 diagnostic questions about propagation range, change frequency, tech-debt tolerance, examples, failure cost, and review intensity, then outputs a clear classification with the recommended approach. Trigger even when the user describes a change without explicitly asking for classification — the answer changes how they should proceed.

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Uses symbolic execution (angr) to solve constraint-based malware problems — finding inputs that reach a target branch, recovering keys/passwords from a check routine, and brute-forcing opaque predicates — by generating a ready-to-edit angr harness. Activates for requests to use symbolic execution, solve a malware unlock check with angr, or recover an input that reaches a code path.

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Use when a task needs the judgment of a Data Engineer — designing an idempotent pipeline operation so retries and backfills don't corrupt data, choosing event-time over processing-time partitioning for time-series aggregation, enforcing schema compatibility rules before an upstream change propagates downstream, adding automated data quality checks to a pipeline, or deciding between batch and streaming architecture for a specific freshness requirement.

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Use when a task needs the judgment of a Chemical Engineer — closing a mass/energy balance around a reactor or separation unit, evaluating scale-up risk from bench to pilot to plant, reviewing a HAZOP finding or LOPA scenario for a covered process, sizing relief for a runaway-reaction or fire case, or checking a process safety information (PSI) package against OSHA 1910.119 before startup.

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When a GDD is revised, scans all ADRs and the traceability index to identify which architectural decisions are now potentially stale. Produces a change impact report and guides the user through resolution.

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Use when setting up, customizing, or troubleshooting the Salesforce Path component on Opportunity, Lead, Case, or custom objects. Triggers: 'add guidance to stages', 'key fields on path', 'celebrate closed won', 'path not showing', 'configure path steps', 'confetti on stage change'. NOT for creating the stage picklist — use admin/opportunity-management. NOT for in-app prompts — use admin/in-app-guidance-and-walkthroughs. Metadata: PathAssistant, PathAssistantStep, PathAssistantSettings, pathAssistantEnabled, canOverrideAutoPathCollapseWithUserPref, runtime_sales_pathassistant:pathAssistant, picklistValueName, recordTypeName, BusinessProcess.

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Email consent management in Marketing Cloud: configuring Subscription Centers, building Preference Centers on CloudPages, enforcing CAN-SPAM and GDPR requirements, and handling opt-out propagation. Trigger keywords: unsubscribe handling, CAN-SPAM compliance, GDPR consent tracking, publication lists, subscription center, preference center, opt-out, double opt-in, Privacy Center, MC consent. NOT for subscriber key and suppression lists — use data/subscriber-data-management. NOT for the CRM consent data model or MC-to-CRM sync — use architect/marketing-consent-architecture.

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Prepares Go applications to work effectively with AI coding agents. Use when setting up a new Go project or retrofitting an existing one to ensure: structured JSON logging (slog/Zap/ZeroLog/Logrus), machine-readable command output, thorough golangci-lint configuration, non-interactive CLI design with --yes flags, structured error handling with meaningful exit codes, proper context.Context propagation, graceful shutdown, health check endpoints, and a standardized Makefile. Triggers when a user asks to make their Go app "agent-ready," "AI-friendly," wants to improve agent tooling/observability in a Go project, or needs to audit an existing Go project against agent-readiness best practices.

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Plan a feature or system before implementation. Use when the user says "plan this", "I want to build X", "how do I implement Y", or when /ck:brainstorm produces a spec.md. Always run before /ck:cook. Modes (pick one): --fast (simple, single-file), --hard (research + red-team + validate). Composable flag (combine with any mode): --tdd — propagates into the cook pipeline. Every phase gets Design Constraints and Quality/Testing State; ck:cook confirms optional test and quality checks per phase.

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Plan a feature or system before implementation. Use when the user says "plan this", "I want to build X", "how do I implement Y", or when /ck:brainstorm produces a spec.md. Always run before /ck:cook. Modes (pick one): --fast (simple, single-file), --hard (research + red-team + validate). Composable flag (combine with any mode): --tdd — propagates into the cook pipeline. Every phase gets Design Constraints and Quality/Testing State; ck:cook confirms optional test and quality checks per phase.

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Audit and curate the `learnings` memory namespace — the one namespace nothing re-derives, so the only one that rots. Runs moflo's mechanical audit to nominate stale, unused, and near-duplicate entries, then decides entry by entry whether to keep, retire, compress, or merge, and propagates the result to the shared artifact. Use when memory search returns stale or duplicated hits, after retiring a big chunk of work whose supporting entries went stale with it, or as a periodic pass once the namespace passes a few hundred entries.

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Use when code changes should be scanned and reflected back into living specs, docs, or architecture notes without changing product intent.

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ADHD-friendly life management assistant for OpenClaw. Helps with daily planning, task breakdown, time management, prioritization, body doubling, dopamine regulation, and maintaining routines. Use when the user asks for help organizing their life, staying on top of tasks, beating procrastination, planning their day/week, managing overwhelm, or mentions ADHD-related challenges like time blindness, forgetfulness, difficulty starting tasks, or emotional dysregulation.

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Time-blind friendly planning, executive function support, and daily structure for ADHD brains. Specializes in realistic time estimation, dopamine-aware task design, and building systems that actually work for neurodivergent minds.

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This skill should be used when the user asks for body doubling, ADHD focus sessions, accountability while working, help getting started on a task, pomodoro-style work sessions, or says things like 'I can't focus', 'I'm stuck', 'help me start', 'I need accountability', 'body double with me', 'I keep procrastinating', 'I can't get started', or 'focus session'. Provides punk-style ADHD body doubling with micro-step protocols, frequent check-ins, dopamine resets, and session history tracking. Part of the ADHD-founder.com ecosystem.

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Performs root cause analysis on DAG execution failures. Traces failure propagation, identifies systemic issues, and generates actionable remediation guidance. Activate on 'failure analysis', 'root cause', 'why did it fail', 'debug failure', 'error investigation'. NOT for execution tracing (use dag-execution-tracer) or performance issues (use dag-performance-profiler).

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Building resilient distributed systems with circuit breakers, retries with full-jitter exponential backoff, retry budgets (per-request 3-attempt + per-client 10% ratio per Google SRE), deadline propagation, and the cascading-failure math (4 layers × 3 retries = 64x amplification). Grounded in Resilience4j, Microsoft Cloud Patterns, AWS Architecture Blog (Marc Brooker), and Google SRE Book.

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Time-blind friendly planning, executive function support, and daily structure for ADHD brains. Specializes in realistic time estimation, dopamine-aware task design, and building systems that actually work for neurodivergent minds.

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Quality control for pooled CRISPR screens covering library representation, Gini index, log-skew, replicate Pearson and Spearman concordance, essentialome precision-recall AUC against CEGv2 (Hart 2017), Cas9 cut-toxicity diagnostics, copy-number amplicon detection (Aguirre 2016 / Munoz 2016), bottleneck propagation through plasmid pool, infection, selection, and endpoint stages, MOI verification, and DepMap-style screen-quality scoring. Use when assessing screen quality before hit calling, deciding whether to repeat or rescue a screen, diagnosing low-confidence hits, choosing between MAGeCK / BAGEL2 / Chronos based on quality grade, picking a normalization strategy from QC signatures, or evaluating whether an in-vivo screen retained adequate library complexity.

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