Results for “conversation”

Use to check that required disclosures, consents and notices were actually given in support conversations — the right wording, at the right point, through a channel the customer could act on. Trigger for "did we give the required disclosure", "are we capturing consent properly", "recording notification audit", "check our mandatory notices", disclosure compliance, or a complaint that a customer was not told something.

Use to find cancellation, downgrade and switching intent in support conversations, and to be honest about how much of your churn support can actually see. Trigger for "which customers are at risk of leaving", "find cancellation intent", "customers asking how to export their data", "top reasons customers asked to close their account", competitor mentions, or building a churn early-warning signal from support data.

Turns a raw idea, brain dump, or conversation into a complete PRD with mandatory acceptance criteria and measurable success metrics. Extracts the problem, target users, and jobs-to-be-done, runs a quick competitive scan (web if available, logged as a gap if not), drafts the full PRD (problem, personas, user stories.

Transform diverse inputs (YouTube videos/audio, existing Suno songs, raw lyrics, audio files, or conversational ideas) into highly optimized Suno V5 custom song generation prompts with intelligent character optimization (5000 lyrics, 1000 style limits), proven metatag reliability, V5-enhanced emotion tags, and template-based best practices. Use when user wants to create music with Suno, provides content for song generation, or needs help crafting effective V5 prompts.

Use to verify that the chain from conversation to evaluation to decision is complete and reconstructible, so a QA score or a customer outcome can be explained months later. Trigger for "can we prove why this decision was made", "is our audit trail complete", "reconstruct how this score was produced", evidencing an AI-assisted decision, or an auditor asking how a conclusion was reached.

Use to assemble a fair, evidence-backed coaching pack for a support agent's one-to-one from QA evaluations and conversation history. Trigger for "prepare a coaching session for X", "what areas does X need to improve", "areas of markdown for this agent", "what coaching opportunities stand out", "build a coaching agenda from these tickets", or preparing a weekly or monthly agent review.

Use when a local Bot/MetaBot should publish/register one paid MetaWeb service, choose a provider MetaBot and publishable skill, gather service fields conversationally, or list/modify/revoke/manage one of its discoverable services. Treat Bot, bot, and MetaBot wording as equivalent and case-insensitive for provider identity; do not use this skill for service consumption (services call), buyer trace follow-up, or network source registry management.

Use to scan support conversations for a harm nobody has categorised yet — a new failure mode, a scam pattern, or a product change hurting a group of customers before it shows in any metric. Trigger for "is anything new going wrong", "scan for emerging issues", "early warning from support", horizon scanning, a new scam pattern, or preparing for a question about what you might be missing.

Codify the most recent successful /scrape flow into a permanent browser-skill on disk. Future /scrape calls with the same intent run the codified script in ~200ms instead of re-driving the page. Walks back through the conversation, synthesizes script.ts + script.test.ts + fixture, runs the test in a temp dir, and asks before committing. Use when asked to "skillify", "codify", "save this scrape", or "make this permanent". (gstack)

Giskard RAGET (RAG Evaluation Toolkit): automatic testset generation (simple / complex / distracting / conversational), component-level scoring (retriever / generator / rewriter), hallucination and bias tests, CI integration. Compared to RAGAS and DeepEval. USE WHEN: user mentions "Giskard", "RAGET", "Giskard RAG toolkit", "automatic testset generation", "component-level RAG scoring", "hallucination test Giskard" DO NOT USE FOR: general RAGAS usage - use `rag-evaluation`; Stanford ARES - use `ares-framework`; CI/CD wiring - use `continuous-evaluation`

Use to analyse a support backlog and find the conversations actually at risk — never-touched tickets, work stalled on your side, customers stranded with a bot, and unassigned open work. Trigger for "report on our open tickets", "daily outstanding tickets report", "how many tickets have we not responded to", "find tickets waiting on us", "customers stuck with a bot and no human", aging backlog, or a backlog that keeps growing.

Use to check whether a conversation export, dataset or AI pipeline is leaking personal data that should have been redacted, and to measure how well the redaction actually works. Trigger for "is this export safe to share", "check our redaction", "can we use support transcripts for training", sending transcripts to a vendor or model, "is there PII in this dataset", or before opening support data to a wider audience.

Voice agents represent the frontier of AI interaction - humans speaking naturally with AI systems. The challenge isn't just speech recognition and synthesis, it's achieving natural conversation flow with sub-800ms latency while handling interruptions, background noise, and emotional nuance. This skill covers two architectures: speech-to-speech (OpenAI Realtime API, lowest latency, most natural) and pipeline (STT→LLM→TTS, more control, easier to debug). Key insight: latency is the constraint. Hu

Voice agents represent the frontier of AI interaction - humans speaking naturally with AI systems. The challenge isn't just speech recognition and synthesis, it's achieving natural conversation flow with sub-800ms latency while handling interruptions, background noise, and emotional nuance. This skill covers two architectures: speech-to-speech (OpenAI Realtime API, lowest latency, most natural) and pipeline (STT→LLM→TTS, more control, easier to debug). Key insight: latency is the constraint. Hu

Voice agents represent the frontier of AI interaction - humans speaking naturally with AI systems. The challenge isn't just speech recognition and synthesis, it's achieving natural conversation flow with sub-800ms latency while handling interruptions, background noise, and emotional nuance. This skill covers two architectures: speech-to-speech (OpenAI Realtime API, lowest latency, most natural) and pipeline (STT→LLM→TTS, more control, easier to debug). Key insight: latency is the constraint. Hu

Use to push back-office work into Rulebase over the REST API so it can be evaluated like a conversation, with a dry-run plan, idempotent external ids and an audit log. Trigger for "send our back-office work to Rulebase", "push work items", "QA our operations team", "get case work into Rulebase", disputes or KYC queues with no customer on the line, or duplicate work items appearing after a re-run.

Turn a cold dial into a conversation. A permission-based opener plus ready objection turns for "not interested," "no time," "just send me info," and "we already use X," tuned to one persona. Built for B2B SDRs and full-cycle reps, customizable to your persona, offer, and dialer. Trigger on "write a cold call opener", "give me objection responses", "handle 'not interested'", "cold call script for this persona", or any live-dial prep.

Complete meeting lifecycle skill — preparation before and follow-up after any professional or personal meeting. Use this skill whenever the user mentions an upcoming meeting, wants to prepare for a meeting, needs an agenda, wants to brief themselves before talking to someone, needs to write up meeting notes, wants to send a follow-up email after a meeting, needs to capture action items, or says anything like "I have a meeting with...", "I just got out of a meeting", "help me prepare for...", "I need to follow up on...", or "what should I ask in this meeting." Also trigger for specific meeting types: job interviews, performance reviews, 1:1s, sales or client calls, and difficult conversations. Both modes are covered: PRE-meeting preparation and POST-meeting follow-up. Trigger proactively even when the user doesn't use the word "meeting" — if they describe a situation where they're about to talk to someone important, or just finished such a conversation and need to capture it, this skill applies.

Use when querying a Rulebase workspace with SQL through the MCP server's query tool — writing queries that finish inside the statement timeout, and avoiding the join fan-outs that silently inflate QA evaluation counts. Trigger for "query my Rulebase data", "the query timed out", "statement timeout", "canceling statement due to statement timeout", counts that don't reconcile between two Rulebase queries, criterion counts exceeding team counts, or any multi-step analysis over Rulebase conversations and evaluations.

Orchestrator for the learn-from suite - auto-detects source type (academic paper, GitHub repo, blog/web article, or in-conversation learnings) and routes to the correct sub-skill for credibility check, security scan, insight extraction, and application. Load when the user says "learn from", "learn from this", "extract insights from", "apply learnings from", "what can we learn from", or provides a URL, file path, or pasted content that should be ingested as knowledge. Single entry point for all learning workflows.

Interact with DeerFlow AI agent platform via its HTTP API. Use this skill when the user wants to send messages or questions to DeerFlow for research/analysis, start a DeerFlow conversation thread, check DeerFlow status or health, list available models/skills/agents in DeerFlow, manage DeerFlow memory, upload files to DeerFlow threads, or delegate complex research tasks to DeerFlow. Also use when the user mentions deerflow, deer flow, or wants to run a deep research task that DeerFlow can handle.

Walk through the manual-capture.md checklist for a slug, driving Chrome via the Claude in Chrome MCP to capture each visual that needs more than a static URL — multi-step flows, conversation states, settings panels, age gates on third-party sites. Runs equally well locally (your desktop Chrome) or on a VPS with always-on Chrome + the extension installed. Defaults to unattended mode when `BLOG_AGENT_AUTONOMOUS=1` (which forces `UNATTENDED=1`). Use after /generate-visuals has flagged action-shot or failed-screenshot entries.

Turn "block until condition X is met" into an agent wake-up via the bash_async tool. Use when you need to wait for a one-shot event without burning context — a webhook arriving, a CI pipeline finishing, a file appearing, a long shell command completing — AND you want this conversation to resume with full context when the event fires. Distinct from pollers (recurring) and the synchronous Bash tool (blocks the turn). The completion event lands as a fresh turn on the spawning channel; no polling needed.

Opens a conversational planning session for a feature, bug, or ticket. Investigates the repo, discusses the approach with the user until a solution is agreed, and only then writes the plan to docs/plans/. Use whenever the user invokes it, describes a problem to solve, pastes a ticket, mentions an incident, or asks to plan a non-trivial change. Also on "plan this", "analyze this first", "what would we need to touch", "let's start with this" — or in Spanish "planeá", "analizá esto antes", "qué habría que tocar", "arranquemos con esto".

Match a user request to the right skill using trigger matching, project context, and conversation history. Score ambiguity 1–10 and resolve it using context signals or a single clarifying question. Load when project-orchestrator needs to route a request, or when multiple skills could match and the right one is unclear. Also triggers on "which skill should handle this", "route this request", "I'm not sure which skill to use", "disambiguate this", "skill routing". Called by project-orchestrator for request routing. For finding whether a skill exists for a capability, use skill-finder instead.

Invoke a registered skill from the current skill listing. BLOCKING REQUIREMENT: when a skill from the listing matches the user's request, you MUST call this tool (not free-form text). Do not re-invoke a skill to repeat work already done: if a `<skill-loaded>` block for it with the same `args` is already present in the conversation, follow those instructions directly instead of calling the tool again. Do call the tool again when you need the skill with different arguments — the loaded block was expanded with the earlier `args` and will not reflect new inputs.

Use when a user asks to use, run, delegate, or fulfill a task through a capability that may exist as an online Bot/MetaBot skill-service, especially when no local specialized skill matches. Treat Bot, bot, and MetaBot wording as equivalent and case-insensitive for remote service requests; select from the local cached online service list first, then call the remote service and continue through trace get/watch, optional conversation view opening, and rating closure. Do not use for browse-only service discovery, network source registry management, identity creation/switching, or private chat-only requests.

Builds a feature implementation plan from an existing feature specification (or equivalent context) through a facilitated team conversation. Use when the user wants to plan how to implement, build, deliver, or ship a feature that has already been specified. Does not specify what the feature should do — use plan-a-feature first. Does not design the contract for an interface — use design-an-api. Does not refine or stress-test an already-written plan — use iterative-plan-review. Runs its resolution rounds to completion and holds its questions until after they finish; to review each round as it lands, use pairing.

Generate and conversationally edit short videos with Google Gemini Omni Flash (`gemini-omni-flash-preview`). Use when: (1) iterating on a clip with natural-language edits instead of regenerating ("make the phone invisible, keep everything else the same"), (2) generating 3-10s 720p clips with synthesized audio, rendered on-screen text, or timecoded beats, (3) binding reference images to roles with <FIRST_FRAME>/<IMAGE_REF_N> prompt tags, (4) editing an existing uploaded video. Accessed via the `gemini_omni_video` tool using the project's GEMINI_API_KEY/GOOGLE_API_KEY — the same key as Imagen and Google TTS.

Plan a field event, dinner, or conference presence that actually produces pipeline, not just badges scanned. It builds the right guest list, designs a draw worth showing up for, plans the on-site motion so conversations happen, and writes the follow-up that converts, because the pipeline is made after the event, not at it. Built for B2B field marketing and sales teams, customizable to your CRM and your event motion. Trigger on "plan a field event", "customer dinner plan", "conference presence", "how do I get pipeline from this event", "event guest list", "event follow-up plan", or any field-event or in-person pipeline request.

Run Mom Test–style customer-discovery interviews to validate or kill an unbuilt idea — generate a non-leading interview guide, conduct or coach the conversations, and synthesize signal vs compliments. Load when the user asks to do customer discovery, run problem interviews, validate an idea with users, run a Mom Test, design an interview guide, or says "talk to customers", "validate the problem", "interview users", "Mom Test this", "did real users want it", "synthesize my interviews", "I just talked to N people". Sub-skill of `venture-exploration`. Hard-bans "would you use this?", solution-pitching, friend/family-only ICP, and treating compliments as validation. Calls `secure-*` before synthesizing any pasted external transcripts.

World-class copywriting expertise combining the persuasive clarity of David Ogilvy, the conversational warmth of Ann Handley, and the conversion science of modern growth marketers. Copywriting is where brand voice becomes words that move people. Great copy doesn't just communicate—it persuades, connects, and converts. The best copywriters understand that every word earns its place, that benefits beat features, that clarity trumps cleverness, and that the reader's problem is the only starting point that matters. Use when "copywriting, write copy, headlines, taglines, email copy, ad copy, landing page copy, product copy, UX writing, CTAs, value proposition, microcopy, sales copy, conversion copy, writing, copy, headlines, conversion, persuasion, messaging, UX-writing, emails" mentioned.

Self-tuning question sensitivity + developer psychographic for gstack (v1: observational). Review which AskUserQuestion prompts fire across gstack skills, set per-question preferences (never-ask / always-ask / ask-only-for-one-way), inspect the dual-track profile (what you declared vs what your behavior suggests), and enable/disable question tuning. Conversational interface — no CLI syntax required. Use when asked to "tune questions", "stop asking me that", "too many questions", "show my profile", "what questions have I been asked", "show my vibe", "developer profile", or "turn off question tuning". (gstack) Proactively suggest when the user says the same gstack question has come up before, or when they explicitly override a recommendation for the Nth time.

Multi-source recency research skill that takes the pulse of any topic across Reddit, Hacker News, the open web, and optionally X/Twitter within a configurable recent window (default 30 days). Forcing intake clarifies topic specificity, angle (trend/sentiment/problems/opportunities/comparison), time window, and platform scope before searching. Returns a synthesized briefing with citations, engagement metrics, and cross-platform pattern analysis. Triggers: 'pulse on [topic]', 'what's happening with [topic]', 'what are people saying about [topic]', 'current conversation about [topic]', 'take the pulse of [topic]', 'trending: [topic]', 'find me info on [topic]', or any variation requesting multi-source recency intelligence on a topic. Also use for competitor research, trend discovery, tool comparisons, and audience sentiment analysis.

Multi-source recency research skill that takes the pulse of any topic across Reddit, Hacker News, the open web, and optionally X/Twitter within a configurable recent window (default 30 days). Forcing intake clarifies topic specificity, angle (trend/sentiment/problems/opportunities/comparison), time window, and platform scope before searching. Returns a synthesized briefing with citations, engagement metrics, and cross-platform pattern analysis. Use when the user requests multi-source recency intelligence on a topic (e.g., 'pulse on [topic]', 'what's happening with [topic]', 'what are people saying about [topic]', 'current conversation about [topic]', 'take the pulse of [topic]', 'trending: [topic]', 'find me info on [topic]'), and for competitor research, trend discovery, tool comparisons, and audience sentiment analysis.

Use this skill when creating a new Claude skill from scratch, editing or improving an existing skill, or measuring skill performance with evals and benchmarks. Invoke whenever the user says things like 'make a skill for X', 'turn this workflow into a skill', 'test my skill', 'improve my skill', 'run evals', 'benchmark this', or 'optimize my skill description'. Also use proactively when the conversation has produced a repeatable workflow that would benefit from being captured as a skill. Covers the full lifecycle: capture intent, draft SKILL.md, run evals, review with user, iterate, optimize description, package. NOT for general coding help, debugging runtime errors, building MCP servers, writing Claude hooks, or creating plugins - use domain-specific skills for those.