Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add Doris-Labs/sales-skills --skill discovery-planninggit clone --depth 1 https://github.com/Doris-Labs/sales-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/doris-labs/sales-skills/discovery-planning)<a href="https://agentmods.dev/skills/doris-labs/sales-skills/discovery-planning"><img src="https://agentmods.dev/badge/skills/doris-labs/sales-skills/discovery-planning/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/doris-labs/sales-skills/discovery-planning"><img src="https://agentmods.dev/badge/skills/doris-labs/sales-skills/discovery-planning.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00085 | $0.01651 |
| Opus 5 | $0.00043 | $0.00826 |
| Sonnet 5 | $0.00017 | $0.00330 |
| Haiku 4.5 | $0.00009 | $0.00165 |
Grade A, and why
discovery-planning scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 11d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 141 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 11d ago First seen · 141 lines · 85 tokens per session scan A cfc314c57dd3
discovery-planning is a skill published in the GitHub repository Doris-Labs/sales-skills (3 stars, last pushed 3mo ago), licensed MIT. It adds 85 tokens to every session and 1,651 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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