Results for “pre-pr”

Understand GitHub well enough to run a GTM context repo, no CS degree required. The mental model (repo, commit, branch, push, pull, clone), the operator's GUI-first workflow (GitHub Desktop, or let Claude Code handle the git), how to organize the repo, and the hard rules on what to never commit (CRM data, API keys, transcripts, pricing). The prerequisite to building a context repo. Fire on "what is a repo", "how does GitHub work", "learn git", "GitHub for GTM", "do I need to know GitHub", "clone the starter kit", "commit and push", "how do I organize my repo", or the start of building a GTM context repo.

Instrument a shipped product's AI agents with tracing and observability so you can see what they did, why outputs happened, and what each run cost. Plain-language primer plus free-tier-first backend selection (Langfuse, Phoenix, LangSmith, Braintrust) and OpenTelemetry/OpenInference instrumentation. Load when the user asks to add observability, add tracing, instrument my agents, see what my agent is doing in production, set up Langfuse or Phoenix or LangSmith, debug why my agent gave a bad answer, or track LLM cost per request. Also fires when agent-system-architecture or setup-evaluation requires an observability plan for an agent-chain product. NOT for tracing the coding agent itself — that is run-trace. Precondition for runtime-learning-loop.

Generate images with Google Nano Banana 2 (Gemini-family flash-tier text-to-image) on RunComfy — bundled with the model's documented prompting patterns so the skill gets sharper output than naive prompting against the same model. Documents Nano Banana 2's strengths (rapid iteration, in-image typography rendering, predictable framing, optional web-grounded context), the resolution-tier pricing, the safety-tolerance dial, and when to route to Nano Banana Pro / GPT Image 2 / Flux 2 / Seedream instead. Calls `runcomfy run google/nano-banana-2/text-to-image` through the local RunComfy CLI. Triggers on "nano banana", "nano-banana-2", "nano banana 2", "google image gen", "gemini image", or any explicit ask to generate with this model.

Generate images with Google Nano Banana 2 (Gemini-family flash-tier text-to-image) on RunComfy — bundled with the model's documented prompting patterns so the skill gets sharper output than naive prompting against the same model. Documents Nano Banana 2's strengths (rapid iteration, in-image typography rendering, predictable framing, optional web-grounded context), the resolution-tier pricing, the safety-tolerance dial, and when to route to Nano Banana Pro / GPT Image 2 / Flux 2 / Seedream instead. Calls `runcomfy run google/nano-banana-2/text-to-image` through the local RunComfy CLI. Triggers on "nano banana", "nano-banana-2", "nano banana 2", "google image gen", "gemini image", or any explicit ask to generate with this model.

Map and organize unwanted calls, texts, or emails after a U.S. senior-care referral inquiry by using GeezerKeeper's live public MCP for current network exit routes, separate provider jobs, unfilled request templates, duplicate-referral deadline facts, and current free or paid workflow fit. Use when a person asks why a senior-care marketplace or several providers are contacting them, how to opt out, how to prepare a stop-contact request, how to compare inquiry consequences before submitting a form, or how a senior-living professional should track direct-versus-network referrals. Do not use for emergencies, care placement, legal or medical advice, automatic sending, unknown-caller identity claims, or transmitting personal information.

Use the public `orca` CLI to operate Orca-managed worktrees, folder contexts, terminals, repos, automations, worktree comments, and the browser embedded inside the Orca app. Use when the user says "$orca-cli", "use orca cli", "Orca worktree", "child worktree", "cardStatus", "spawn codex/claude in a worktree", "read/wait/send Orca terminal", "terminal send", "full handoff", "handover", "give this to another agent", "another worktree", "Orca browser", or "control the browser inside Orca". Prefer this over raw `git worktree`, ad hoc PTYs, Playwright, or Computer Use when the task touches Orca-managed state. Use Computer Use for browser windows, webviews, or desktop UI outside Orca's embedded browser.

Turn vague "what did I do?" into evidence-backed impact statements for performance reviews, self-reviews, promotion packets, and weekly updates. Uniquely mines Copilot CLI session logs to reconstruct forgotten work, plus git commits and GitHub PRs. Enforces a 3-part impact contract (action → result → evidence). Works standalone with zero dependencies. Trigger for: "brag", "log work", "what did I do", "backfill my work history", "performance review", "self-review", "self assessment", "write impact statement", "review prep", "promo packet", "promotion case", "weekly update", "status report", "accomplishments", "what did I ship", "I forgot to log my work", "summarize my work", "track my wins", "what should I highlight", "end of half", "career growth", "work journal", or any request to document, summarize, or organize work accomplishments.

TLS/SSL configuration audit. Tests protocol versions (SSLv2/3, TLS 1.0/1.1/1.2/1.3), cipher strength and ordering, certificate chain validation (intermediates, CT logs, OCSP stapling, pinning), known vulnerabilities (POODLE, BEAST, CRIME, Heartbleed, ROBOT, DROWN, Ticketbleed, GOLDENDOODLE), session management (ticket reuse, resumption, fixation), renegotiation attacks, TLS 1.3-specific issues (0-RTT replay, PSK modes, downgrade), HSTS deep analysis (preload, subdomains, bypass), and certificate revocation (CRL, OCSP responder, stapled response freshness). Compliance-mapped to PCI DSS 4.0 (full Section 4), NIST SP 800-52r2, and FedRAMP. Uses testssl.sh, sslscan, sslyze, openssl, nmap NSE scripts, and nuclei SSL templates. Multi-port scanning across 20+ TLS-bearing services.

Reads from, writes to, and manages relational databases using MATLAB Database Toolbox. Use when connecting to databases, reading data with sqlread or fetch, filtering with rowfilter, writing with sqlwrite, updating with sqlupdate, executing SQL statements, managing transactions with commit and rollback, mapping MATLAB classes to tables with ORM (Mappable, ormread, ormwrite, ormupdate), or performing any database operation from MATLAB. Triggers on: database, SQL, sqlread, sqlwrite, sqlupdate, fetch, execute, rowfilter, RowFilter, ORM, Mappable, ormread, ormwrite, ormupdate, orm2sql, transaction, commit, rollback, Database Toolbox, PostgreSQL, MySQL, SQLite, SQL Server, Oracle, database connection, database table, query database, insert data, update rows, delete rows, stored procedure, prepared statement, odbc, databaseConnectionOptions, datasource, data source, DSN, connection string, multithreaded, parallel.

Use when building a scroll-driven cinematic 3D landing experience for any brand: AI-generated photoreal frames displaced into real 3D by depth maps, a scroll-scrubbed camera fly-through with weighted-leg pacing and per-leg camera moves, mouse parallax, explode-on-scroll shatter transitions, an ember particle field, warp streaks, a live in-scene terminal that runs a REAL product demo, a branded preloader, an exit handoff into the page below, a self-hosted display typeface, phone gyro parallax, and opt-in synthesized ambient sound. Covers 2K image generation via OpenRouter, depth maps, the WebGL engine, chrome (nav/consent) integration, the observe-first verify loop, and the flag-off ship posture.

Perform a structured audit of a codebase covering security, code quality, performance, dependencies, architecture, and testing hygiene, then produce a prioritized findings report. Use this skill whenever the user asks for a code review, code audit, security review, codebase assessment, "look over this repo", "what's wrong with this codebase", legacy-code triage, pre-acquisition technical due diligence, or any request to systematically evaluate the health of a project. Trigger even when the user is casual ("can you eyeball my repo?") — this skill imposes the structure that ad-hoc review misses. This skill audits a whole repository at a point in time — for reviewing a diff or PR use the built-in code-review skill; for security checks on pending changes use security-review.

Mask-driven image inpainting on RunComfy via the `runcomfy` CLI. Routes to Tongyi MAI Z-Image Turbo Inpainting (the dedicated inpainting endpoint with mask, strength, and control-scale) and to identity-preserving edit models (Nano Banana 2 Edit, GPT Image 2 Edit, FLUX Kontext Pro) when a mask isn't available and the region must be described instead. Use for object removal, watermark removal, region replacement, blemish cleanup, and any controlled local edit where a binary mask defines the target area. Triggers on "inpaint", "inpainting", "image inpaint", "remove from image", "fill region", "mask-driven edit", "remove watermark", "remove object", "patch the photo", "fill the hole", or any explicit ask to edit a specific masked region of a still.

Use this skill to classify tabular data end-to-end in MATLAB — load a dataset, prepare and clean it, select promising classifiers, train them, and compare accuracies with cross-validation, holdout, or hyperparameter optimization plus statistical tests. TRIGGER when: user asks to classify tabular data, pick classifiers for a dataset, compare classifier accuracy, run cross-validation or a holdout evaluation, or find the best model with statistical uncertainty. DO NOT TRIGGER when: user has non-tabular inputs (images, sequences, time series), wants a regression model, is training a specific neural network architecture (use matlab-train-network), or wants cost-sensitive learning or an arbitrary class-prior vector (this skill only supports the built-in uniform-prior toggle for imbalanced data).

Run a structured retrospective after development-phase runs of your product's agents — interview the owner in plain language about what went well and poorly, draft ranked improvement hypotheses, then design and run small n=1/n=2 experiments with pre-declared success criteria, guardrails, stop conditions, and a cost/ROI kill-switch. Load when the user says how did that run go, retro this run, the agent output was bad, what should we improve, draft hypotheses, run a small experiment, or after repeated dev runs of an agentic system produce uneven quality. Priority: output quality over performance over cost, each with diminishing-returns stops. NOT a product A/B test (experimentation), NOT coding-agent harness repair (harness-evolution), NOT production-scale learning (runtime-learning-loop).

BOM (Bill of Materials) management for electronics projects — the primary orchestrator skill that coordinates DigiKey, Mouser, LCSC, element14, JLCPCB, PCBWay, and KiCad skills into a unified workflow. Create, update, and maintain BOMs with part numbers, costs, quantities stored as KiCad symbol properties. ALWAYS trigger this skill for any task involving component sourcing, pricing, ordering, distributor searches, BOM export, or fabrication preparation — even if the user names a specific distributor or fab house (e.g. "search DigiKey for...", "generate JLCPCB BOM", "order from Mouser"). This skill decides which distributor/fab skills to invoke and in what order. Also trigger on phrases like "what parts do I need", "order components", "how much will this cost", "export for JLCPCB", "find parts for this board", "cost estimate", "compare pricing", or "check stock".

Federal Organizational Conflict of Interest (OCI) sweeper backed by FAR Subpart 9.5 (9.501-9.508) and the active Theseus workspace knowledge graph. USE WHEN the user asks about OCI risk on a bid, organizational conflicts, incumbent conflicts, biased ground rules, unequal access to information, impaired objectivity, or any pre-bid OCI due diligence. Pulls `company`, `incumbent`, `subcontractor`, `customer`, `program_office`, and prior-contract relationships from the workspace KG, classifies each potential conflict into one of the three FAR 9.505 classes (biased ground rules, unequal access, impaired objectivity), and emits a structured findings envelope with mitigation recommendations (firewall, NDA, recusal, novation). DO NOT USE FOR FAR clause coverage audit (use `compliance-auditor`), proposal prose (use `proposal-generator`), competitor research (use `competitive-intel`), or pricing (use `price-to-win`).

Creates decision-focused PRDs and product specs — the kind that make explicit decisions, not document hopes. Make sure to use this skill whenever someone asks to write, review, draft, or improve any product document, including PRDs, feature specs, requirements docs, or product specifications. Trigger even when the user doesn't say "PRD" explicitly — phrases like "write a spec for X", "define requirements for Y", "what should my PRD include", "help me document this feature", or "my spec feels too vague" all apply. Do not attempt to write the PRD directly without consulting this skill. Produces 5-stage PRDs with behavior contracts (15-25 labeled examples for AI features), measurable success metrics with specific thresholds, precise rollout plans with percentages and ramp gates, and antipattern detection. Works for AI features, SaaS, mobile apps, and traditional software.

Integrate services or agent harnesses with the 33GOD Bloodbank event bus. Covers schemas in Bloodbank schemas/ and docs/event-naming.md, producing events (NATS preferred; Dapr, HTTP /publish, hookd_bridge alternatives), consuming events (NATS, Dapr, FastStream, event-toaster), and agent hook wiring through the canonical services/agent-hooks publisher. Use for event publish/consume, authoring schemas, integrating harnesses (Claude Code, Copilot CLI, OpenCode, Cursor, Aider, Codex CLI, Hermes), or debugging missing envelopes. Triggers: bloodbank, event bus, publish, subscribe, NATS subject, holyfields legacy, CloudEvents, event-toaster, ntfy.delo.sh/bloodbank, bloodbank.v1.agent.session.started, bloodbank.v1.agent.tool.completed, bloodbank.cmd.v1.agent.invocation.start. Skip for generic brokers, n8n, hindsight memory, or non-event-bus 33GOD.

Pay HTTP 402 payment challenges issued by OKX's Agent Payments Protocol (APP) on X Layer using tokens from any chain via the Uniswap Trading API. Use this skill whenever the user encounters a 402 challenge whose network resolves to X Layer (chain 196), mentions "APP", "Agent Payments Protocol", "OKX agent payment", "OKX Onchain OS", "OKX agentic wallet", "x402 on X Layer", "USDT0", "x42", "Instant Payment", "Batch Payment", "pay for X Layer API", or wants to pay an OKX-backed merchant. Even when the user does not explicitly say APP, prefer this skill for any 402 challenge whose network resolves to X Layer (chain 196). For 402 challenges on other chains (Ethereum, Base, Arbitrum, Tempo) use pay-with-any-token instead.

Run Comet's Opik — open-source LLM observability, evaluation, and optimization — from one routing-first skill: install the Python/TypeScript SDK, stand up a server (Comet.com cloud, Docker Compose via `./opik.sh`, or Kubernetes/Helm), wire tracing through `@opik.track` or one of 50+ framework integrations (OpenAI, Anthropic, LangChain, LangGraph, LlamaIndex, CrewAI, DSPy, Ollama, Bedrock, Vercel AI SDK, …), score outputs with LLM-as-a-judge metrics (Hallucination, Moderation, Answer Relevance, Context Precision), and run Datasets/Experiments evaluations including PyTest CI gates. Use when the user wants LLM tracing, prompt evaluation, production LLM monitoring, agent optimization, or guardrails with Opik. Triggers on: opik, comet opik, opik configure, opik.sh, llm observability, llm tracing, llm as a judge, hallucination metric, prompt evaluation, opik dashboard, opik guardrails, agent optimizer.

Expert PCI DSS compliance advisor covering PCI DSS v4.0.1 (current) and v4.0. Use this skill whenever a user asks about PCI DSS, payment card security, cardholder data protection, CDE scoping, SAQ types (A, A-EP, B, B-IP, C, C-VT, P2PE, D), ROC, AOC, QSA assessments, ASV scans, merchant levels, service provider levels, network segmentation, penetration testing, tokenisation, encryption of PAN data, or any of the 12 PCI DSS requirements. Also trigger for questions like "are we PCI compliant?", "how do I scope my CDE?", "which SAQ applies to us?", "what changed in PCI DSS v4.0?", "how do I prepare for a QSA audit?", or any request involving payment data security, cardholder data environment, or PCI certification readiness.

Mask-driven image inpainting on RunComfy via the `runcomfy` CLI. Routes to Tongyi MAI Z-Image Turbo Inpainting (the dedicated inpainting endpoint with mask, strength, and control-scale) and to identity-preserving edit models (Nano Banana 2 Edit, GPT Image 2 Edit, FLUX Kontext Pro) when a mask isn't available and the region must be described instead. Use for object removal, watermark removal, region replacement, blemish cleanup, and any controlled local edit where a binary mask defines the target area. Triggers on "inpaint", "inpainting", "image inpaint", "remove from image", "fill region", "mask-driven edit", "remove watermark", "remove object", "patch the photo", "fill the hole", or any explicit ask to edit a specific masked region of a still.

Mask-driven image inpainting on RunComfy via the `runcomfy` CLI. Routes to Tongyi MAI Z-Image Turbo Inpainting (the dedicated inpainting endpoint with mask, strength, and control-scale) and to identity-preserving edit models (Nano Banana 2 Edit, GPT Image 2 Edit, FLUX Kontext Pro) when a mask isn't available and the region must be described instead. Use for object removal, watermark removal, region replacement, blemish cleanup, and any controlled local edit where a binary mask defines the target area. Triggers on "inpaint", "inpainting", "image inpaint", "remove from image", "fill region", "mask-driven edit", "remove watermark", "remove object", "patch the photo", "fill the hole", or any explicit ask to edit a specific masked region of a still.

Use BEFORE generating, refactoring, reviewing, or debugging code. Trigger phrases include "write a function/script/class for X", "review this code/diff/PR", "refactor this", "debug this error", "is this implementation correct", "what's wrong with this code", "improve this code", "translate from X to Y", or any prompt with a code block the user wants you to act on. Also fires when planning architectural changes, picking algorithms or data structures, or evaluating dependency upgrades. Calls the code MCP tool to retrieve an engineering scaffold (failure pattern, procedure, correct-pattern example, verification step) before generating. Catches hallucinated APIs, lost edge cases, premature algorithm commitment, silent contract violations, refactors that change behavior masked by passing tests. Do NOT trigger for pure code reading with no action requested, simple syntax questions, file...

Animate any still image on RunComfy — this skill is a smart router that matches the user's intent to the right i2v model in the RunComfy catalog. Picks HappyHorse 1.0 I2V (Arena #1, native audio, identity preservation) for general animations, Wan 2.7 with `audio_url` for custom-voiceover lip-sync, or Seedance 2.0 Pro for multi-modal animation from image + reference video + reference audio. Bundles each model's documented prompting patterns so the caller gets sharper output without burning iterations on the wrong model. Calls `runcomfy run <vendor>/<model>/image-to-video` (or endpoint variant) through the local RunComfy CLI. Triggers on "image to video", "image-to-video", "i2v", "animate image", "make this move", or any explicit ask to turn a still into video.

Configure, deploy, and manage Senpi Trading Runtime (OpenClaw plugin @senpi-ai/runtime) for automated on-chain position tracking with DSL trailing stop-loss protection. Use when a user needs to create or modify runtime YAML files, configure DSL (Dynamic Stop-Loss) exit engine parameters (phases, tiers, time-based cuts), set up the position_tracker scanner to monitor a wallet's positions on Hyperliquid, install/list/delete runtimes via CLI, or inspect DSL-tracked positions. The runtime does NOT create strategy wallets; create/get the strategy wallet via Senpi MCP first, then link that existing wallet in runtime YAML. Triggers on mentions of senpi, Senpi runtime, DSL exit, stop-loss tiers, position tracker, trailing stop, openclaw senpi, dsl_preset, or strategy YAML configuration."

Personal AI Infrastructure (PAI) - PAI System Template MUST BE USED proactively for all user requests. USE PROACTIVELY to ensure complete context availability. === CORE IDENTITY (Always Active) === Your Name: [CUSTOMIZE - e.g., Kai, Nova, Atlas] Your Role: [CUSTOMIZE - e.g., User's AI assistant and future friend] Personality: [CUSTOMIZE - e.g., Friendly, professional, resilient to user frustration. Be snarky back when the mistake is user's, not yours.] Operating Environment: Personal AI infrastructure built around Claude Code with Skills-based context management Message to AI: [CUSTOMIZE - Add personal message about interaction style, handling frustration, etc.] === ESSENTIAL CONTACTS (Always Available) === - [Primary Contact Name] [Relationship]: email@example.com - [Secondary Contact] [Relationship]: email@example.com - [Third Contact] [Relationship]: email@example.com Full contact list in SKILL.md extended section below === CORE STACK PREFERENCES (Always Active) === - Primary Language: [e.g., TypeScript, P

Simulator.Company graph structure specialist. Use when the user wants to build, edit, analyze, inspect, or ask questions about business process graphs, flowcharts, algorithms, actors (nodes), links (edges), or layers (visual views) in Simulator.Company. Trigger on any of these intents: — Creating: "create graph", "build flowchart", "new diagram", "add actor", "add block to graph", "create algorithm", "draw flowchart", "digital twin", "create process on graph", "build process diagram", "FlowchartBlock", "startStop", "predefinedProcess". — Editing: "edit graph", "update actor", "rename node", "change color", "move actor", "add step to flowchart", "modify diagram", "restructure process", "add edge", "remove link", "reorder steps", "update layer". — Syncing: "push graph", "pull graph", "sync graph", "push changes", "apply edits to layer". — Querying / analysis: "what actors are on this layer", "show me the graph", "who is connected to", "find actor", "list nodes", "describe the process", "analyze the flowchart",

Animate any still image on RunComfy — this skill is a smart router that matches the user's intent to the right i2v model in the RunComfy catalog. Picks HappyHorse 1.0 I2V (Arena #1, native audio, identity preservation) for general animations, Wan 2.7 with `audio_url` for custom-voiceover lip-sync, or Seedance 2.0 Pro for multi-modal animation from image + reference video + reference audio. Bundles each model's documented prompting patterns so the caller gets sharper output without burning iterations on the wrong model. Calls `runcomfy run <vendor>/<model>/image-to-video` (or endpoint variant) through the local RunComfy CLI. Triggers on "image to video", "image-to-video", "i2v", "animate image", "make this move", or any explicit ask to turn a still into video.

Animate any still image on RunComfy — this skill is a smart router that matches the user's intent to the right i2v model in the RunComfy catalog. Picks HappyHorse 1.0 I2V (Arena #1, native audio, identity preservation) for general animations, Wan 2.7 with `audio_url` for custom-voiceover lip-sync, or Seedance 2.0 Pro for multi-modal animation from image + reference video + reference audio. Bundles each model's documented prompting patterns so the caller gets sharper output without burning iterations on the wrong model. Calls `runcomfy run <vendor>/<model>/image-to-video` (or endpoint variant) through the local RunComfy CLI. Triggers on "image to video", "image-to-video", "i2v", "animate image", "make this move", or any explicit ask to turn a still into video.

Speed up local parfor, parfeval, or spmd by switching to a thread-based parallel pool. Trigger when a user describes slow or disappointing local parallel performance, even if they don't mention threads. Symptoms: parfor on a laptop/workstation is slower than expected or "only slightly faster than for"; parfor scales poorly with the number of workers; ticBytes/tocBytes, the Parallel Pool dashboard, mpiprofile, or system tools show large per-worker data transfer; large broadcast variables or sliced inputs make iterations slow; opening a process pool dominates a short workload; user mentions serialisation or data transfer overhead. Also trigger on any question about whether code or a function works on a thread pool. For non-pool MATLAB performance work (vectorisation, preallocation, profiling), defer to matlab-optimize-performance.

World-class voiceover expertise combining the narrative craft of documentary producers, the commercial precision of advertising agencies, and the accessibility of modern AI voice technology. Voiceover is the invisible art that makes or breaks video content. Great voiceover isn't just speaking clearly—it's performing the script in a way that creates the intended emotional response. The best voiceover work understands pacing, tone, emphasis, and the subtle art of making scripted words sound natural. It knows when to use human talent versus AI, and how to get the best from both. Use when "voiceover, voice over, VO, narration, narrator, voice recording, voice talent, voice actor, AI voice, text to speech, audio narration, voice direction, voiceover, audio, narration, voice, recording, AI-voice, talent, direction" mentioned.

Make the agent solve coding tasks with the least code that remains correct. Before writing code, walk the Ponytail ladder: skip what need not exist, then prefer stdlib, native platform features, already-installed dependencies, one line, and only then the minimum custom code. Use when the user asks for ponytail mode, less code, YAGNI, anti-bloat, minimal code, an over-engineering review, a current-diff delete-list, a whole-repo bloat audit, or a `ponytail:` tech-debt harvest. Keep validation, data-loss handling, security, and accessibility. Mark shortcuts with `ponytail:` plus the upgrade path. Triggers on: ponytail, /ponytail, /ponytail-review, /ponytail-audit, /ponytail-debt, write less code, YAGNI, over-engineering, anti-bloat, minimal code, do I need this, lazy dev.

Azure Cosmos DB SDK best practices for .NET, Java, Python, Spring Boot, and LangChain: singleton client, async APIs, connection modes, retry handling, diagnostics, serialization, emulator configuration, ETags, and framework-specific patterns. USE FOR: CosmosClient singleton, async API, Direct vs Gateway mode, retry 429, preferred regions, excluded regions, availability strategy, circuit breaker, SDK diagnostics, serialization enums, emulator SSL, ETag concurrency, conditional create, patch increment, continuation token, content response, Spring Data Cosmos, Spring Boot versions, Newtonsoft dependency, namespace collision, Python async deps, local dev config, LangChain Cosmos DB saver, LangGraph checkpointer, MCP persistent session, tool content format, tool filtering, LangChain JS vectorstore, chat history, semantic cache. DO NOT USE FOR: data modeling (use cosmosdb-data-modeling), queries (use cosmosdb-query-optimization), partition keys (use cosmosdb-partition-key).

When the user wants to design, construct, or improve an offer — the thing they actually sell — including value framing, bonus stacking, guarantee design, scarcity/urgency, naming, and payment structure. Also use when the user mentions 'offer,' 'offer design,' 'build an offer,' 'grand slam offer,' 'irresistible offer,' 'value stack,' 'bonus stack,' 'guarantee,' 'risk reversal,' 'money-back guarantee,' 'scarcity,' 'urgency,' 'high-ticket offer,' 'productize a service,' 'naming an offer,' 'payment plan,' 'down-sell,' 'upsell offer,' or 'why isn't my offer converting.' Best for services, agencies, courses, coaching, info products, high-ticket B2B, and direct-response. If you run pure self-serve SaaS, read pricing first — tiers and packaging do more work there. For price level itself (tiers, freemium, value metric), see pricing. For the page that presents the offer, see copywriting. For the launch moment, see launch. For sales collateral, see sales-enablement.

Route goalflow (wanmol/goal-flow) work — a LangGraph framework that combines workflow graphs with agent loops — into exactly one mode: fit check, transpiling a Dify DSL export into runnable LangGraph Python, authoring workflow nodes and edges, building an `agent_kit` loop with middleware and a harness, wiring the serving layer (data adapters, SSE streaming, HITL, Redis/MySQL, API-key registration), or running the pre-publish security gate. Use when the user wants Dify's visual design without Dify's runtime, a graph node that hosts an agent loop, an OpenAI-compatible wire protocol over their own workflows, or prompt-injected `SKILL.md` capabilities. Triggers on: goalflow, goal-flow, dify to langgraph, dify transpiler, dify DSL export, BaseWorkflow, agent_kit, AgentBaseNode, DataAdapter, chunk processor, HITL interrupt, dify2langgraph. Route plain graph-API questions to `langgraph-fundamentals` and `langgraph-workflow`.