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/deciqai/knowledge-skills/continuous-discoverynpx skills add deciqAI/knowledge-skills --skill continuous-discoverygit clone --depth 1 https://github.com/deciqAI/knowledge-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/deciqai/knowledge-skills/continuous-discovery)<a href="https://agentmods.dev/skills/deciqai/knowledge-skills/continuous-discovery"><img src="https://agentmods.dev/badge/skills/deciqai/knowledge-skills/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 | $0.00082 | $0.00616 |
| Opus 5 | $0.00041 | $0.00308 |
| Sonnet 5 | $0.00016 | $0.00123 |
| Haiku 4.5 | $0.00008 | $0.00062 |
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 6d 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 — 44 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Continuous Discovery — Weekly Contact, Opportunity Trees
Overview
Continuous discovery (Teresa Torres, Continuous Discovery Habits, 2021) replaces one-off research with a weekly cadence of customer touchpoints by the team building the product, structured around an opportunity solution tree: one outcome → the opportunities (unmet needs) that drive it → competing solutions → assumption tests. It keeps roadmaps anchored to real needs instead of the loudest stakeholder.
When to Use
- A product with users but no steady learning loop
- Roadmap fights decided by seniority, not evidence
- Turning a fuzzy outcome (e.g. "increase activation") into shippable bets
The Process
- Pick one clear outcome (a behavior/metric, not a feature). Gate: if the target is a feature, back up to the outcome it serves.
- Interview weekly — the trio (PM/design/eng), small and continuous, not a quarterly study.
- Map opportunities as a tree under the outcome; keep them as customer needs, not solutions in disguise.
- Diverge on solutions per opportunity (≥3), then converge.
- Test the riskiest assumption cheaply before building (desirability, viability, feasibility, usability).
- Prune to the next bet. Gate: no assumption test run = you're shipping opinion → stop and test.
Applying It Well
- Automate recruiting so weekly interviews actually happen (the habit dies on scheduling friction).
- One opportunity tree per outcome; don't boil the ocean.
- Small continuous samples beat big infrequent ones.
Red Flags
- Discovery done by a research silo, not the builders.
- Opportunities written as features.
- Interviews stop the moment things get busy.
Verification
- Single outcome defined (behavioral)
- Weekly interview cadence in place
- Opportunity tree maps needs, not solutions
- Riskiest assumption tested before build
Part of deciqAI Knowledge Skills — 237 open-source thinking skills that make rigor executable for AI agents. The same skills power every deciqAI agent, which runs them autonomously to operate your company. See it run → https://www.deciqai.com/s/continuous-discovery · Built by deciqAI · github.com/deciqAI · Contributions welcome.
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.
- 6d ago First seen · 44 lines · 82 tokens per session scan A d1f285511368
continuous-discovery is a skill published in the GitHub repository deciqAI/knowledge-skills (10 stars, last pushed 4d ago), licensed MIT. It adds 82 tokens to every session and 616 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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