Agent skill

Continuous Discovery

by RefoundAI in RefoundAI/lenny-skills

Help users build a sustainable habit of regular customer interaction to ensure product development is driven by real-world needs rather than internal assumptions.

MITAuto-check passedProduct & Project Management

Install Continuous Discovery

skills CLI
$ npx skills add RefoundAI/lenny-skills --skill continuous-discovery -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install RefoundAI/lenny-skills continuous-discovery --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/RefoundAI/lenny-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/continuous-discovery .claude/skills/continuous-discovery && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
continuous-discovery
GitHub stars
1.4k
Token cost
~2k tokens
SKILL.md length
1,163 words
Files
3 (incl. references)
Skills in repo
76
Repo updated
First seen
Licence
MIT

At a glance

Help users build a sustainable habit of regular customer interaction to ensure product development is driven by real-world needs rather than internal assumptions.

  • Works in 4 steps: Audit current proximity - Evaluate the… → Define discovery rituals - Help set up… → Structure the opportunity space - Guide… → …
  • Product & Project Management work in your project
  • SKILL.md covers How to Help, Core Principles, Templates & Frameworks and Questions to Help Users, plus 3 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Continuous Discovery is an agent skill from RefoundAI/lenny-skills. Help users build a sustainable habit of regular customer interaction to ensure product development is driven by real-world needs rather than internal assumptions.

Its SKILL.md is about 2k tokens, which your agent loads only when the skill is triggered. The skill folder holds 3 other files, including reference files (for example `references/artifacts.md` and `references/guest-insights.md`).

It sits in Product & Project Management. The repository describes itself as: 86 product management skills from Lenny's Podcast for Claude Code and AI agents. Hiring, user research, strategy, shipping, and more. The licence is MIT.

When your agent uses it

  • Product & Project Management work in your project

Example prompts

  • “/continuous-discovery”

Workflow steps

4 steps, taken from the first numbered list in SKILL.md.

  1. Audit current proximity - Evaluate the frequency and quality of direct team-to-user interactions and identify existing gatekeepers.
  2. Define discovery rituals - Help set up recurring cadences for interviews, demo days, and support shifts that involve the entire product…
  3. Structure the opportunity space - Guide the translation of raw feedback into a visual map of unmet needs, pain points, and desires.
  4. Accelerate evidence gathering - Recommend lightweight methods for testing assumptions through prototypes and behavior mapping before…

What it can do on your machine

Read from SKILL.md and the folder at commit 13598cc. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md.

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Continuous Discovery loads about 2k tokens when it runs, and up to ~15k if it reads all its reference files. Until then it costs about 46 tokens; SKILL.md has 1,163 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~46
When it runs · the whole SKILL.md, loaded when a task matches
~2k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~15k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from RefoundAI/lenny-skills at commit 13598cc, republished under its MIT licence (© RefoundAI). 1,163 words, ~2,022 tokens.

Download SKILL.mdSave it as .claude/skills/continuous-discovery/SKILL.md (or your agent's skills folder). This skill also uses 2 other files; get the full folder from GitHub.
name
continuous-discovery
description
Help users build a sustainable habit of regular customer interaction to ensure product development is driven by real-world needs rather than internal assumptions.

Continuous Product Discovery

Turn customer feedback from a periodic chore into a high-frequency engine for product decisions.

Help the user with continuous product discovery using insights from 23 guests and posts across Lenny's Podcast and Newsletter.

How to Help

  1. Audit current proximity - Evaluate the frequency and quality of direct team-to-user interactions and identify existing gatekeepers.
  2. Define discovery rituals - Help set up recurring cadences for interviews, demo days, and support shifts that involve the entire product trio.
  3. Structure the opportunity space - Guide the translation of raw feedback into a visual map of unmet needs, pain points, and desires.
  4. Accelerate evidence gathering - Recommend lightweight methods for testing assumptions through prototypes and behavior mapping before committing to full builds.

Core Principles

Distinguish needs from solutions

Teresa Torres: "I can tell you that opportunity is an unmet need pain point or desire, and that's great. But I can tell you that 98% of people that write opportunities write them as solutions. So we tend to just really struggle with this distinction between the problem space and the solution space."

True discovery requires defining every opportunity strictly as an unmet customer need rather than a pre-conceived feature idea.

Remove layers between builders and users

Brian Tolkin: "Talking to customers every single day like one-on-one onboarding drivers responding to support tickets, there's no centralized support team, there was no closer to the customer, right? And so I think that foundation actually for really understanding what moves the business and being super close to the customer actually is a pretty good foundation for them going on to say, okay, what do we actually want to build in a more scalable technology way?"

Deep empathy is built by eliminating centralized filters and having product teams engage directly in onboarding and support.

Integrate discovery into execution

Itamar Gilad: "Google, was what I call an evidence guided company. So essentially it put a high premium on focusing on customers, coming up with a lot of ideas on looking at the data, looking at how these ideas actually worked out. They weren't shy about launching betas and things that were very rough and incomplete and learning from that and then they expected people to take action based on the results."

Launch rough, incomplete versions to gather real-world data that dictates whether to pivot or proceed with the engineering task.

Prioritize high-signal informal contact

Jeff Weinstein: "The moment the customer felt compelled enough to go out of their way to talk about some problem, that's a unbelievable gift. I will leave a meeting to just get one message back to them. If you're text message friendly with five or 10 of those, you are going to have so much direct signal that is infectious."

Direct, informal communication with motivated users, such as text messaging, often provides higher quality signals than structured research.

Maintain research as a continuous partner

Judd Antin: "Well, the solution is simple but not easy to me. It's that we need to restructure the way we make products in a way which integrates research much more fully. It looks like consistent relationships in which researchers, and the work, and the insights they provide are a part of the process from beginning to end."

Research should be an integrated partner throughout the entire development process rather than a reactive service used at the start.

Identify friction through direct observation

From "The unconventional Palantir principles that catalyzed a generation of startups": "You have to be the user to unlock this concept. I don’t mean that spiritually as in “think like the user”; I mean literally do their same job with your product as an extended member of their team and see what you learn."

Perform the customer's actual work alongside them to reveal the operational friction that interviews alone cannot uncover.

Embed technical staff in customer workspaces

Nabeel S. Qureshi: "There was a different type of engineer which you sent into the field. You would spend maybe Monday to Thursday and you would actually go into the building where the customer worked and you would work alongside them. You would literally get a desk there and so, that engineer became known as a forward deployed engineer."

High-value enterprise discovery is best achieved by having engineers work on-site at customer offices to identify tactical bottlenecks.

Show full SKILL.md (446 more words)Show less

Templates & Frameworks

  • Continuous Discovery vs. Project-Based Research (Teresa Torres) - A reframing of how discovery fits into product work: not as a phase before delivery, but as a parallel continuous habit that runs alongside delivery
  • Opportunity Solution Tree Framework (The Best of Lenny’s Newsletter 2023) - Teresa Torres's framework for continuous product discovery, connecting outcomes to opportunities to solutions to experiments.
  • Product Scrapbook (Notion Database) (Product manager is an unfair role. So work unfairly.) - A lightweight Notion database for collecting customer insights, feedback, and evidence organized by strategic swim lanes for faster discovery and planning.
  • Forward Deployed Engineering (FDE) Model (The unconventional Palantir principles that catalyzed a generation of startups) - A customer discovery and product development approach where engineers physically embed inside customer environments for extended periods (months, not hours), do
  • Exposure Hours (Guillermo Rauch) - An internal operating principle at Vercel for developing product taste and empathy.
  • Product Trio Model (Teresa Torres) - A collaborative working model where the product manager, designer, and software engineer make discovery and product decisions together as equals, rather than th
  • Customer Support as Everyone's Job — Implementation Rhythm (What working at Figma taught me about customer obsession) - The three-step rhythm Figma used when handling any customer interaction, from support tickets to sales calls to random encounters
  • Go to the Source Checklist (First-principles thinking) - Five methods for getting to primary information rather than relying on secondhand assumptions

See references/artifacts.md for the full list with details.

Questions to Help Users

  • "How many hours did your team spend observing customers doing their work last week?"
  • "What is the most recent customer insight that caused you to kill or pivot a feature?"
  • "Which members of your engineering team have spoken directly to a user in the last 14 days?"
  • "Are you tracking unmet customer needs separately from your list of feature requests?"
  • "What internal gatekeepers or centralized teams currently sit between your builders and your users?"
  • "How do you currently store and organize customer evidence so it is accessible during planning?"

Common Mistakes to Flag

  • Treating discovery as a phase - Discovery should be a parallel continuous habit rather than a one-time project that happens before delivery starts.
  • Listening to stated positions over incentives - Users often provide playbooks or demands that mask the underlying incentives driving their actual behavior.
  • Focusing on solutions during interviews - Early discovery should focus on deeply understanding the underlying user needs rather than pitching specific feature ideas.
  • Building for vocal minorities - Over-indexing on feedback from the loudest users can lead to a product that fails to meet the needs of the broader market.

Deep Dive

For all 27 sourced insights from 23 guests, see references/guest-insights.md

  • Customer Interviews
  • Idea Validation
  • Product Experiments
  • Defining Icp

© RefoundAI, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 2 other files (references) in skills/continuous-discovery of RefoundAI/lenny-skills.

  • SKILL.md
  • references/artifacts.md
  • references/guest-insights.md

Open the folder on GitHubat commit 13598cc

Compare with similar skills

Continuous Discovery next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Continuous Discovery compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Continuous Discovery this skillRefoundAI/lenny-skills1.4k—~2kAutomated safety check: PassMIT
User Story Writerdeanpeters/Product-Manager-Skills7.2k2 repos~2.9kAutomated safety check: PassCustom licence
Game Changing FeaturesopenstatusHQ/data-table-filters2.3k3 repos~2.1kAutomated safety check: PassMIT
CCPM Project Managementautomazeio/ccpm8.4k—~1.1kAutomated safety check: PassMIT
Convex Create Componentspokvulcan/poker-planning1148 repos~2.6kAutomated safety check: PassMIT
Self Improving Agentfarm-fe/farm5.6k2 repos~3.3kAutomated safety check: NotesMIT

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Questions about Continuous Discovery

What does Continuous Discovery do?

Help users build a sustainable habit of regular customer interaction to ensure product development is driven by real-world needs rather than internal assumptions. Continuous Discovery is an agent skill from RefoundAI/lenny-skills. Help users build a sustainable habit of regular customer interaction to ensure product development is driven by real-world needs rather than internal assumptions.

When should I use Continuous Discovery?

Continuous Discovery fits situations like: product & Project Management work in your project.

How do I install Continuous Discovery in Claude Code?

Run `npx skills add RefoundAI/lenny-skills --skill continuous-discovery -a claude-code`. Or copy the skill folder (skills/continuous-discovery in RefoundAI/lenny-skills) into .claude/skills/continuous-discovery in your project. Claude Code loads it when a task matches its description.

How do I install Continuous Discovery in Codex?

Run `npx skills add RefoundAI/lenny-skills --skill continuous-discovery -a codex`. Or copy the skill folder (skills/continuous-discovery in RefoundAI/lenny-skills) into .agents/skills/continuous-discovery in your project. Codex loads it when a task matches its description.

Can I use Continuous Discovery in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add RefoundAI/lenny-skills --skill continuous-discovery -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/continuous-discovery, .gemini/skills/continuous-discovery, .github/skills/continuous-discovery and .opencode/skills/continuous-discovery in your project.

What does Continuous Discovery need to run?

SKILL.md names no scripts, command-line tools or credentials: Continuous Discovery is instructions for the agent only.

Does Continuous Discovery access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Continuous Discovery safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Continuous Discovery use?

Continuous Discovery is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Continuous Discovery use?

About 2k tokens (SKILL.md is roughly 8.1k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 13k tokens, read only when the agent opens those files.

What are the alternatives to Continuous Discovery?

Skills that share tags, products or a category with Continuous Discovery: User Story Writer (deanpeters/Product-Manager-Skills, 7.2k stars), Game Changing Features (openstatusHQ/data-table-filters, 2.3k stars), CCPM Project Management (automazeio/ccpm, 8.4k stars) and Convex Create Component (spokvulcan/poker-planning, 114 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Continuous Discovery?

RefoundAI (a GitHub organization) maintains it in RefoundAI/lenny-skills, which has 1,377 GitHub stars. The repository holds 76 skills in this directory. The repository was last updated on July 16, 2026.

Source: RefoundAI/lenny-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.