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Agent skill

Product Deep Think

by stella in stella/stella

Deeply assess a consequential product idea using repository evidence, current market research, switching costs, international constraints, and maintainability before planning or coding.

Apache-2.0Auto-check passedMarketing & SEO

Install Product Deep Think

skills CLI
$ npx skills add stella/stella --skill product-deep-think -a claude-code

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

GitHub CLI
$ gh skill install stella/stella product-deep-think --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/stella/stella.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.agents/skills/product-deep-think .claude/skills/product-deep-think && 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
product-deep-think
GitHub stars
258
Token cost
~1.1k tokens
SKILL.md length
561 words
Files
1
Skills in repo
24
Repo updated
First seen
Licence
Apache-2.0

At a glance

Deeply assess a consequential product idea using repository evidence, current market research, switching costs, international constraints, and maintainability before planning or coding.

  • Works in 7 steps: Establish the Problem → Autopsy the Status Quo → Test Stella's Product Boundary → …
  • Tasks that involve Market research
  • SKILL.md covers Interaction Mode, 1. Establish the Problem, 2. Autopsy the Status Quo and 3. Test Stella's Product…, plus 4 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Product Deep Think is an agent skill from stella/stella. Deeply assess a consequential product idea using repository evidence, current market research, switching costs, international constraints, and maintainability before planning or coding.

Its SKILL.md is about 1.1k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Marketing & SEO, covering Market research. The repository describes itself as: Open-source legal workspace. The licence is Apache-2.0.

When your agent uses it

  • Tasks that involve Market research

Example prompts

  • “/product-deep-think”

Workflow steps

7 steps, taken from the step headings in SKILL.md.

  1. Establish the Problem
  2. Autopsy the Status Quo
  3. Test Stella's Product Boundary
  4. Research the Market and Structural Analogs
  5. Rebuild, Then Reconcile
  6. Apply the Maintenance Filter
  7. Verdict

What it can do on your machine

Read from SKILL.md and the folder at commit b225fd8. 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

Product Deep Think loads about 1.1k tokens when it runs. Until then it costs about 51 tokens; SKILL.md has 561 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~51
When it runs · the whole SKILL.md, loaded when a task matches
~1.1k

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 stella/stella at commit b225fd8, republished under its Apache-2.0 licence (© stella). 561 words, ~1,083 tokens.

Download SKILL.mdSave it as .claude/skills/product-deep-think/SKILL.md (or your agent's skills folder).
name
product-deep-think
description
Deeply assess a consequential product idea using repository evidence, current market research, switching costs, international constraints, and maintainability before planning or coding.

Product Deep Think

Assess whether and how to build a consequential feature. This is a conversational research exercise: do not edit code or create files unless the user later asks for a plan or implementation.

Interaction Mode

Default to one coherent pass after confirming the problem statement. Do not stop after every analytical lens for ceremonial approval. Pause only when the user's answer would materially change who the product serves, the product boundary, or the recommended direction. If the user explicitly asks for an interactive workshop, work through the lenses one useful question at a time.

Lead with discoveries, not phase narration. Keep intermediate updates short and translate abstractions into concrete user behavior.

1. Establish the Problem

Separate the pain from the proposed feature:

  • who experiences it, in which workflow, and how often;
  • what they do today, including manual workarounds and tolerated failure;
  • what happens if Stella does nothing;
  • what outcome would demonstrate improvement.

Confirm this framing once when it is genuinely ambiguous. Do not make the user answer questions the repository or evidence can resolve.

2. Autopsy the Status Quo

Understand why the current process persists before redesigning it. Account for regulation, liability, professional norms, migration, training, political ownership, integration, and parallel safety systems. Apply the Schuster check: is the idea better after switching costs, or merely cleaner on a blank page?

3. Test Stella's Product Boundary

Evaluate the idea for:

  • 5–50 lawyer firms without dedicated workflow administrators;
  • a credible path to 2,000–5,000+ lawyers;
  • buyer, administrator, and daily-user incentives;
  • international jurisdictions, terminology, formats, RTL, and legal-system differences;
  • privileged data, auditability, trust, and graceful failure;
  • open standards, self-hosting, and provider replaceability.

Inspect the current repository and nearby plans so the recommendation extends existing product primitives instead of inventing a parallel system.

4. Research the Market and Structural Analogs

Because product and competitor capabilities change, verify current claims with web research and cite direct, authoritative sources. Separate documented facts from inference and unknowns.

Compare:

  • legal-sector table stakes, strengths, complaints, and switching paths;
  • adjacent categories that solve the same structural problem;
  • interaction models and mental models worth importing, rather than cosmetic feature copying;
  • genuine differentiation Stella can sustain rather than a temporary feature gap.
Show full SKILL.md (199 more words)Show less

5. Rebuild, Then Reconcile

Describe the clean-slate ideal and its remarkable version. List inherited assumptions and distinguish real constraints from convention. Then bring back path dependence: identify which parts survive now, which require compatibility, and which should only shape the long-term architecture.

Offer at least three shapes when materially different options exist:

  • the smallest coherent version;
  • the stronger durable version;
  • what not to build yet.

Make the tradeoffs explicit and recommend one.

6. Apply the Maintenance Filter

Assess ongoing complexity, operator burden, tuning, failure behavior, likely follow-up demands, extensibility, and the ability to explain the feature in three sentences. Prefer a smaller model with truthful states over a flexible system whose edge cases require permanent manual discipline.

7. Verdict

Return Build, Reshape, Defer, or Kill, with:

  • the problem and affected user;
  • evidence from the repository and current market;
  • options considered and why the recommendation wins;
  • the smallest valuable scope and explicit non-goals;
  • three guardrails that must carry into planning;
  • failure/maintenance risks and success signals;
  • a one-sentence pitch to a skeptical managing partner.

Keep the synthesis compact enough to act on. Move to /plan only after the product boundary is coherent and the user asks for a durable plan artifact.

© stella, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in .agents/skills/product-deep-think of stella/stella.

Open the folder on GitHubat commit b225fd8

Compare with similar skills

Product Deep Think 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.

Product Deep Think compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Product Deep Think this skillstella/stella258—~1.1kAutomated safety check: PassApache-2.0
Customer ResearchNexus-JPF/note-companion8706 repos~3.2kAutomated safety check: PassMIT
Creative Directorsmixs/creative-director-skill248—~5.1kAutomated safety check: PassCC-BY-4.0
Audience ResearchScrapeCreators/social-media-research-skills3.4k—~635Automated safety check: NotesMIT
Last 30 Days Trend Researchnexu-io/open-design100k—~1.3kAutomated safety check: PassMIT
Bggg Data Redditbinggandata/bggg-skills604—~1.2kAutomated safety check: PassMIT

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Categories

Questions about Product Deep Think

What does Product Deep Think do?

Deeply assess a consequential product idea using repository evidence, current market research, switching costs, international constraints, and maintainability before planning or coding. Product Deep Think is an agent skill from stella/stella. Deeply assess a consequential product idea using repository evidence, current market research, switching costs, international constraints, and maintainability before planning or coding.

When should I use Product Deep Think?

Product Deep Think fits situations like: tasks that involve Market research.

How do I install Product Deep Think in Claude Code?

Run `npx skills add stella/stella --skill product-deep-think -a claude-code`. Or copy the skill folder (.agents/skills/product-deep-think in stella/stella) into .claude/skills/product-deep-think in your project. Claude Code loads it when a task matches its description.

How do I install Product Deep Think in Codex?

Run `npx skills add stella/stella --skill product-deep-think -a codex`. Or copy the skill folder (.agents/skills/product-deep-think in stella/stella) into .agents/skills/product-deep-think in your project. Codex loads it when a task matches its description.

Can I use Product Deep Think 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 stella/stella --skill product-deep-think -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/product-deep-think, .gemini/skills/product-deep-think, .github/skills/product-deep-think and .opencode/skills/product-deep-think in your project.

What does Product Deep Think need to run?

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

Does Product Deep Think 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 Product Deep Think 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 Product Deep Think use?

Product Deep Think is published under the Apache-2.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Product Deep Think use?

About 1.1k tokens (SKILL.md is roughly 4.3k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Product Deep Think?

Skills that share tags, products or a category with Product Deep Think: Customer Research (Nexus-JPF/note-companion, 870 stars), Creative Director (smixs/creative-director-skill, 248 stars), Audience Research (ScrapeCreators/social-media-research-skills, 3.4k stars) and Last 30 Days Trend Research (nexu-io/open-design, 100k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Product Deep Think?

stella (a GitHub organization) maintains it in stella/stella, which has 258 GitHub stars. The repository holds 24 skills in this directory. The repository was last updated on October 9, 2026.

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