continuous-discovery

continuous-discovery is a skill for Claude Code, Codex from lucasheriques/shipmate. It costs 90 tokens per session (1,960 once invoked), scanned A, original, MIT.

A product-discovery procedure for live products that already have users. It uses opportunity solution trees, which map user needs to possible solutions, and regular interviews to test assumptions.

In plain words
What is it for?
Use it to set a product outcome, organize user needs, prioritize opportunities, run weekly interviews, and test whether proposed solutions are worth building.
Why use it?
It helps teams decide what to build next based on a measurable product outcome and evidence from users, instead of choosing features only by opinion or backlog order.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Install

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.

agentmods
npx agentmods add skills/lucasheriques/shipmate/continuous-discovery
Any agent
npx skills add lucasheriques/shipmate --skill continuous-discovery
Clone the repo
git clone --depth 1 https://github.com/lucasheriques/shipmate

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for continuous-discovery

README.md
[![agentmods](https://agentmods.dev/badge/skills/lucasheriques/shipmate/continuous-discovery.svg)](https://agentmods.dev/skills/lucasheriques/shipmate/continuous-discovery)
Your own site
<a href="https://agentmods.dev/skills/lucasheriques/shipmate/continuous-discovery"><img src="https://agentmods.dev/badge/skills/lucasheriques/shipmate/continuous-discovery.svg" alt="Measured on agentmods" height="20"></a>
Per session 90 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,960 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00090 $0.01960
Opus 5 $0.00045 $0.00980
Sonnet 5 $0.00018 $0.00392
Haiku 4.5 $0.00009 $0.00196

Measured 5d ago against content hash f19d99bfe3f9, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-05, from the pricing page.

Security

Grade A, and why

continuous-discovery 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 5d 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.

skills/continuous-discovery/SKILL.md · 71 lines

How it starts

The opening of the file, as written. The whole thing — 71 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Continuous Discovery — post-launch discovery procedure

When to use

A live product with real users, and the question is "what should we build next?" or "will this idea work?". This is the post-launch complement to The Mom Test: Mom Test validates whether a problem/business is real before building; this skill structures the ongoing loop of choosing and de-risking what to build once you have users. For how to conduct the conversation itself (question technique, bad data, commitment), defer to the mom-test skill — it's stronger on that and this skill does not repeat it.

The procedure

1. Set one outcome

  1. Pick a product outcome: a metric the team can move directly (e.g. "increase % of new users who reach the aha moment"), not a business outcome (revenue — lagging, cross-team) and not a traction metric (usage of one feature — too narrow). Prefer leading over lagging; iterate toward faster feedback (90-day retention → 5-day is a legitimate revision).
  2. In a team: leader picks which outcome and shares the strategic why; the team commits how much movement ("+10% this quarter"). Neither side names solutions. Solo: still write the outcome down — it's the root of the tree and the referee of every later debate.
  3. One outcome at a time, kept for multiple quarters. Pair it with a health metric so you don't optimize it destructively. New territory? Set a learning goal ("find the levers") before a numeric goal.

2. Map the opportunity space (the tree)

  1. Draw an experience map of how customers do the job today. Scope it with a question calibrated to ambition: "How do people use our product to X" = optimization; "How do people X at all" = new markets. In a team, each person draws alone first, then merge (prevents groupthink). Visual — nodes (moments) and links.
  2. Build the opportunity solution tree: outcome at the root; below it, opportunities = customer needs, pain points, desires in the customer's own words; below those, solutions; below those, assumption tests.
  3. Construction rules — the tree's value is in following them strictly:
    • Child = subset of parent. Siblings = similar but distinct (you can address one without the others).
    • Top-level opportunities = distinct moments in time in the customer journey; branches must not overlap.
    • No solutions in disguise — test: "Is there more than one way to address this?" If not ("let me skip ads"), it's a solution; write the need behind it ("I don't like ads").
    • No company-perspective framings, no feelings-as-opportunities (capture the cause), no vertical single-child chains, no node that fits under two parents.
  4. Feed the tree from interviews (step 3); it's never finished.

Read the full file on GitHub · 71 lines

Changes

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.

  1. 5d ago First seen · 71 lines · 90 tokens per session scan A f19d99bfe3f9

Subscribe to this mod's changes

continuous-discovery is a skill published in the GitHub repository lucasheriques/shipmate (4 stars, last pushed 1mo ago), licensed MIT. It adds 90 tokens to every session and 1,960 once invoked, about $0.0005 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.