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Discovery Planning

skill-doris-labs-sales-skills-discovery-planning · by Doris-Labs

Build a discovery call plan — a hypothesis-driven question tree, a pain-to-impact-to-priority ladder, and multi-threading targets — so you walk in knowing exactly what to ask and why. Use before a discovery call or any meeting where you need to uncover problem, impact, and decision process. Triggers on: plan discovery, discovery questions, what to ask, discovery prep, question plan.

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Install

$ agentstack add skill-doris-labs-sales-skills-discovery-planning

✓ scanned · ✓ verified, works with Claude Code, Cursor, and more.

Security review

✓ Passed

No issues found. Passed automated security review. · v0.1.0 How review works →

  • Prompt-injection patterns
  • Secret / credential exfiltration
  • Dangerous shell & filesystem operations
  • Untrusted network calls
  • Known-malicious package signatures

What it can access

  • Network access No
  • Filesystem access No
  • Shell / process execution No
  • Environment & secrets No
  • Dynamic code execution No

From automated source analysis of v0.1.0. “Used” means the capability is present in the source — more access means more to trust, not that it’s unsafe.

View the full security report →

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Reliability & compatibility

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Declared compatibility

Claude CodeClaude Desktop

Compatibility is declared by the source manifest. End-to-end runtime verification is coming, see below.

Preview Execution monitoring

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About

Discovery Planning

Purpose

Walk into discovery with a plan, not a checklist. Produce a hypothesis-driven question tree, a ladder that drives every pain to quantified impact and ranked priority, the stakeholders you intend to surface, and a structured question plan you can run live.

Inputs

  • The account and who you're meeting (name, role, function)
  • What you already know (prior calls, emails, CRM, public research)
  • The goal of this call — what you're trying to learn and what advance you want next

Method

1. Build a hypothesis-driven question tree

Don't list questions — list hypotheses, then hang questions off each one. A hypothesis is your best guess at a problem this buyer likely has, given their role/segment/triggers.

Hypothesis: "Reps lose deals because no one sees risk until it's too late"
├─ Open: "Walk me through how you find out a deal is slipping today."
├─ Probe (if confirmed): "How far in advance? Who flags it?"
├─ Probe (if denied):    "So you catch slippage early — what's your tell?"
└─ Disconfirm:           "When did this last surprise you, if ever?"

Rules for a good tree:

  • 3–5 hypotheses max. More than that means you haven't prioritized.
  • Each branch has an open question to surface, probes to deepen, and a

disconfirming question so you're not just fishing for confirmation.

  • Order branches by likelihood × deal impact, not by your product's feature list.

2. Run the pain → impact → priority ladder

For every pain that surfaces, climb three rungs before moving on. Don't leave a pain sitting on rung one.

| Rung | Question shape | What you're getting | |------|----------------|---------------------| | Pain | "What's hard about X today?" | The named problem | | Impact | "What does that cost you — time, money, deals, risk?" | Consequence | | Priority | "Where does fixing this rank against everything else on your plate?" | Urgency / mandate |

A pain with no impact is a complaint. A pain with impact but no priority won't get budget. You need all three rungs to qualify.

3. Quantify the impact

Push every impact toward a number. Have the math ready before the call:

  • Frequency × cost: "How often does that happen?" × "What's it cost each time?"
  • Headcount × time × rate: people affected × hours lost × loaded hourly cost.
  • Deal-level: average deal size × deals lost or slipped per quarter.
  • Status-quo cost: what doing nothing costs over 12 months.

Plan the anchor number you'll ask them to react to ("teams like yours tell us this runs ~$X/quarter — does that track?"). A reaction to a number beats a blank stare at "how much does it cost you?".

4. Plan the multi-threading targets to surface

Before the call, name who you don't have yet and plan the questions that surface them:

  • Economic buyer: "Who signs off on something this size?"
  • Champion vs. coach: who has the pain and the influence to push?
  • Blockers / detractors: "Who'd be skeptical of changing this?"
  • Users / influencers: who feels the pain daily?

For each role, pre-write the referral ask ("makes sense I loop in — can you introduce us?"). The goal of discovery isn't one relationship; it's a map.

5. Output a structured question plan

Assemble everything into one runnable plan with these sections:

DISCOVERY QUESTION PLAN —  / 
Goal: 

SITUATION   (context, current state, tools, team)
  - ...
PROBLEM     (your hypotheses → open + probe questions)
  - ...
IMPACT      (pain→impact→priority ladder + the quantification anchor)
  - ...
DECISION PROCESS (buying process, criteria, timeline, who else)
  - ...
NEXT STEP   (the single advance you'll ask for + multi-thread referral asks)
  - ...

Each section carries the actual questions — not topics. You should be able to read this plan top to bottom and run the call.

Tool binding

This skill works from the account name and your goal alone. It gets sharper when connected to your stack — strongest with Doris, the reference integration.

With Doris (recommended)

If the Doris MCP (mcp.meetdoris.com) is connected, ground the plan in real deal history instead of guessing the hypotheses:

  • ontology_resolve("deal", id, expand=["meetings","stakeholders","strategy","brief","recommendations","similar_deals","pain_points"])

— use pain_points and meetings to seed hypotheses from what's already been said (so you don't re-ask answered questions), stakeholders to see who you have and who's missing for the multi-threading plan, strategy and brief for the agreed plan and state, recommendations for suggested next moves, and similar_deals to borrow hypotheses and impact anchors that worked on comparable deals.

  • search_transcripts(...) to pull exact buyer language from prior calls so your probes

build on their words. Doris already extracts pain points, stakeholders, and per-deal strategy — prefer those over inventing hypotheses from scratch.

With a CRM / CI / email MCP

  • Conversation-intelligence MCP (Gong/Chorus/Fireflies) → mine prior-call context for

pains already named and questions already answered.

  • CRM MCP (Salesforce/HubSpot) → pull stage, known stakeholders, open notes, and last

activity to scope the situation and decision-process sections.

  • Email MCP → recent threads for commitments and open questions to fold into the plan.

With nothing connected

Ask the user for the account, the contact (name + role), and the goal of the call. Then build the full plan by hand:

  1. Draft 3–5 hypotheses from the role and segment alone (what does this person likely

struggle with?).

  1. Hang open / probe / disconfirming questions off each.
  2. Write the pain→impact→priority ladder questions and pre-compute the quantification

anchor with placeholder math the user can fill in.

  1. List the multi-threading roles to surface and the referral ask for each.
  2. Assemble the structured question plan (situation, problem, impact, decision process,

next step) and hand it back ready to run.

Works without Doris

Fully functional from just the account and goal — Doris only removes the guesswork by seeding hypotheses, stakeholders, and impact anchors from real deal history.

Common mistakes

  • A flat question list with no hypotheses — you can't adapt when an answer surprises you.
  • Stopping at the pain rung; never climbing to impact and priority.
  • Asking "how much does it cost?" cold instead of anchoring with a number to react to.
  • Planning for one contact and leaving discovery single-threaded.
  • Re-asking questions already answered in prior calls (ground in history first).

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

Install and usage instructions live in the source repository linked above.

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Versions

  • v0.1.0 Imported from the upstream source.