/deepline-start

name: deepline-start
description: 'Interview a user to discover a concrete GTM problem or manual task, recommend the right installed Deepline skill, prebuilt Play, recipe, or tool, confirm the plan and org, then start the chosen workflow. Use only when the user explicitly invokes deepline-start.'
disable-model-invocation: true

Discover a Deepline GTM workflow

Quick Start

npm install -g deepline
deepline preflight
deepline -h

Use a one-question-at-a-time interview to turn an unclear GTM need into a specific, executable Deepline plan. This skill adapts the `grilling` interview pattern; it does not require that skill to be installed.

Route first

Start this interview only when the user explicitly invokes `deepline-start`. Do not invoke it automatically for unclear GTM requests or from another skill.

When the user invokes this skill, keep the discovery and confirmation flow. If the outcome, inputs, and scope are already clear, skip redundant questions and propose the plan for confirmation. For a specific execution request that did not invoke this skill or ask for discovery, use its owning skill directly. `deepline-gtm` is the generalist for GTM implementation. A specialist leads when its narrower job is the user's actual goal; it may route back to `deepline-gtm` for execution.

| Emerging need | Likely owner | What to verify before recommending | | ------------------------------------------------------------------------------ | ---------------------------- | --------------------------------------------------------------------- | | Find accounts or people, enrich a list, write outreach, or automate row work | `deepline-gtm` | Input identifiers, target criteria, useful output, and batch size | | Build a TAM, source a portfolio, map an account, or find qualified roles | `deepline-gtm` recipe | Which established recipe matches the target and available identifiers | | Watch for new hiring, funding, or other provider events | `deepline-gtm` monitor route | Event definition, cadence, downstream action, and access | | Discover public/private sources, current market language, or provider coverage | `deepline-research` | Research question, evidence needed, recency, and source access | | Learn ICP signals, audit won/lost evidence, or score accounts/leads | `deepline-scoring` | Decision, population, labels or approved rules, and evaluation goal | | Answer a funnel, pipeline, or RevOps metric question | `deepline-analytics` | Metric, period, grain, and governed data source | | Build or upload an ads audience | `deepline-ads-audiences` | Platform, audience, suppression, data rights, and destination | | Turn a defined state machine into a durable Play | `deepline-engine` | States, transitions, inputs, and outputs | | Review and improve existing Play results with a human | `deepline-plays-review` | Play/run, review surface, and acceptance criteria |

These are starting points, not a complete inventory. Inspect the skills available in the current agent environment before naming an owner, and read the candidate's `SKILL.md` once the job is clear. Read its guidance without invoking its workflow during the interview: an early handoff can replace the one-question interview with the specialist's intake or execution. Do not claim an uninstalled skill is available. If the specialist is unavailable, route through the installed `deepline-gtm` guidance where it covers the task; otherwise state the gap and give the user a usable plan.

Interview

Ask one concise question per turn, wait for the answer, and give your recommended answer or a short set of choices with a recommendation. Follow the branch the answer opens; do not send a questionnaire. Look up facts available from supplied files, the current environment, or read-only catalog calls instead of asking the user to recall them. Decisions about priorities, acceptable output, data use, spend, and external actions belong to the user. When you cannot recommend the business answer yet, recommend how to decide between the choices rather than guessing the user's preference. Include that recommendation even when asking about files or systems: recommend an input format or inspection approach without inventing the missing fact.

Start with the result they want or the recurring work that consumes time. If they cannot name a problem, offer concrete starting points: building target lists, filling missing contact data, researching accounts or buyer language, ranking accounts, personalizing outreach, preparing ad audiences, and measuring pipeline. Ask which is most costly or blocked today.

For example: “Which recurring task costs your team the most time right now: finding target accounts, filling missing contact data, researching accounts, ranking leads, or preparing outreach? I recommend starting with the one you can show me a recent example of.” Wait for that answer before drilling down.

As the branch becomes clearer, resolve only uncertainties that change the workflow:

• What happens now, with one recent example and the manual steps?
• Who or what is processed, and what identifiers or private sources already exist?
• What decision or deliverable would make the work useful, and how will the user judge success?
• How many items and how often, and where should the result go?
• Which constraints matter: geography, freshness, quality, budget, data rights,

or permission for outreach, CRM writes, recurring work, and paid calls?

Do not demand every answer before proposing a plan. Mark a none