continuous-discovery-habits

continuous-discovery-habits is a skill for Claude Code, Codex from marcos-sponton/frameworks-as-skills. It costs 265 tokens per session (3,099 once invoked), scanned A, original, MIT.

A product-discovery method based on regular customer interviews, an Opportunity Solution Tree, and small tests of assumptions. Product discovery is the work of learning what customers need before deciding what to build.

In plain words
What is it for?
Use it to plan a weekly interview practice, organize customer problems and possible solutions, identify assumptions, and design tests for product decisions.
Why use it?
It helps teams keep customer learning continuous instead of treating research as a one-time project. Small tests reduce the risk of committing to an unproven solution.

Skill for Claude CodeCodex

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

Good fit Use it to plan a weekly interview practice, organize customer problems and possible solutions, identify assumptions, and design tests for product decisions.

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Install with agentmods
npx agentmods add skills/marcos-sponton/frameworks-as-skills/continuous-discovery-habits
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.

Any agent
npx skills add marcos-sponton/frameworks-as-skills --skill continuous-discovery-habits
Clone the repo
git clone --depth 1 https://github.com/marcos-sponton/frameworks-as-skills

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-habits

README.md
[![agentmods](https://agentmods.dev/badge/skills/marcos-sponton/frameworks-as-skills/continuous-discovery-habits/github.svg)](https://agentmods.dev/skills/marcos-sponton/frameworks-as-skills/continuous-discovery-habits)
Your own site
<a href="https://agentmods.dev/skills/marcos-sponton/frameworks-as-skills/continuous-discovery-habits"><img src="https://agentmods.dev/badge/skills/marcos-sponton/frameworks-as-skills/continuous-discovery-habits/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.

agentmods 80×15 button for continuous-discovery-habits

Your own site · 80×15
<a href="https://agentmods.dev/skills/marcos-sponton/frameworks-as-skills/continuous-discovery-habits"><img src="https://agentmods.dev/badge/skills/marcos-sponton/frameworks-as-skills/continuous-discovery-habits.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 265 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 3,099 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
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 $0.00265 $0.03099
Opus 5 $0.00133 $0.01550
Sonnet 5 $0.00053 $0.00620
Haiku 4.5 $0.00026 $0.00310

Measured 11d ago against content hash ac127536f6fd, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-11, from the pricing page.

Security

Grade A, and why

continuous-discovery-habits 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.

skills/continuous-discovery-habits/SKILL.md · 100 lines

How it starts

The opening of the file, as written. The whole thing — 100 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Continuous Discovery Habits

Teresa Torres's practitioner method for product discovery — distilled from Continuous Discovery Habits (Product Talk, 2021), the ongoing Product Talk essay archive (2013–present, still active weekly-ish through 2026), the All Things Product podcast (co-hosted with Petra Wille, 2025+), the Product Talk Academy (16,000+ students across 102 countries), her post-book refinements (adding "ethical" as the 5th assumption category, the Ladder of Evidence, the Interview Coach AI she built in 2025), and her position on the Product Operating Model + AI + Discovery debates through 2026.

This skill helps the assistant think in Torres's method, not just recite the tree diagram. Torres's method is a set of habits — the weekly cadence is non-negotiable, the trio is non-negotiable, the story-based interview rules are non-negotiable. Softening any of those collapses the method into generic user-research advice. Applying her frame means holding those constraints and coaching the user to build the habit, not just draw a tree.

When this skill activates

Use this skill when the user is:

  • Setting up (or trying to sustain) a weekly customer interview cadence with their product team.
  • Building, updating, or debugging an Opportunity Solution Tree — Outcome → Opportunities → Solutions → Assumption Tests.
  • Running story-based customer interviews and wanting help with the technique (opening question, timeline walk, redirecting generalizations).
  • Framing a product outcome (vs a business outcome vs an output) for a team.
  • Mapping assumptions across desirability, viability, feasibility, usability, and ethical categories.
  • Designing small assumption tests for a solution — instead of jumping to a full A/B experiment.
  • Forming or fixing a product trio (PM + designer + engineer) so discovery is team-based, not PM-solo.
  • Diagnosing why "we already talk to users" isn't producing new insight (usually project-based, not continuous).
  • Reframing "opportunities" that turn out to be features in disguise.
  • Auditing whether a discovery practice is actually continuous or is one-and-done research relabeled.
  • Deciding how AI tools fit into a discovery cadence without replacing customer contact.

Read the full file on GitHub · 100 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. 11d ago First seen · 100 lines · 265 tokens per session scan A ac127536f6fd

Subscribe to this mod's changes

continuous-discovery-habits is a skill published in the GitHub repository marcos-sponton/frameworks-as-skills (1 stars, last pushed 1mo ago), licensed MIT. It adds 265 tokens to every session and 3,099 once invoked, about $0.0013 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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