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 agentmods add skills/lucasheriques/shipmate/continuous-discoverynpx skills add lucasheriques/shipmate --skill continuous-discoverygit clone --depth 1 https://github.com/lucasheriques/shipmateWrote 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/lucasheriques/shipmate/continuous-discovery)<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>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 | $0.00090 | $0.01960 |
| Opus 5 | $0.00045 | $0.00980 |
| Sonnet 5 | $0.00018 | $0.00392 |
| Haiku 4.5 | $0.00009 | $0.00196 |
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.
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
- 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).
- 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.
- 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)
- 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.
- 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.
- 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.
- Feed the tree from interviews (step 3); it's never finished.
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.
- 5d ago First seen · 71 lines · 90 tokens per session scan A f19d99bfe3f9
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.
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