Agent skill

Discovery

by aiblueprinthq in aiblueprinthq/ai-blueprint

Run an optional guided product-discovery interview and draft project-plan.md and build-plan.md only after the user is ready.

MITAuto-check passedProduct & Project Management

Install Discovery

skills CLI
$ npx skills add aiblueprinthq/ai-blueprint --skill discovery -a claude-code

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

GitHub CLI
$ gh skill install aiblueprinthq/ai-blueprint 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/aiblueprinthq/ai-blueprint.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.agents/skills/discovery .claude/skills/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
discovery
GitHub stars
463
Token cost
~2.1k tokens
SKILL.md length
1,146 words
Files
2
Skills in repo
20
Repo updated
First seen
Licence
MIT

At a glance

Run an optional guided product-discovery interview and draft project-plan.md and build-plan.md only after the user is ready.

  • Works in 5 steps: establish the starting point → run adaptive discovery → decide whether the plans are ready → …
  • Guided planning
  • SKILL.md covers Step 1 - establish the…, Step 2 - run adaptive discovery, Step 3 - decide whether the… and Step 4 - draft both planning…, plus 3 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Discovery is an agent skill from aiblueprinthq/ai-blueprint. Run an optional guided product-discovery interview and draft project-plan.md and build-plan.md only after the user is ready. Use for /discovery, guided planning, thinking through a new product, or deepening plans. Never require it before /overview.

Its SKILL.md is about 2.1k tokens, which your agent loads only when the skill is triggered. The skill folder holds 2 other files (for example `agents/openai.yaml`).

It sits in Product & Project Management, covering Project management. The repository describes itself as: A file-backed, spec-driven AI coding workflow framework for building real software while staying in control. The licence is MIT.

When your agent uses it

  • Guided planning
  • Thinking through a new product
  • Deepening plans

Example prompts

  • “/discovery”

Workflow steps

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

  1. establish the starting point
  2. run adaptive discovery
  3. decide whether the plans are ready
  4. draft both planning files
  5. write only after approval

What it can do on your machine

Read from SKILL.md and the folder at commit 96222b7. 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

Discovery loads about 2.1k tokens when it runs. Until then it costs about 65 tokens; SKILL.md has 1,146 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~65
When it runs · the whole SKILL.md, loaded when a task matches
~2.1k

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 aiblueprinthq/ai-blueprint at commit 96222b7, republished under its MIT licence (© aiblueprinthq). 1,146 words, ~2,078 tokens.

Download SKILL.mdSave it as .claude/skills/discovery/SKILL.md (or your agent's skills folder). This skill also uses 1 other file; get the full folder from GitHub.
name
discovery
description
Run an optional guided product-discovery interview and draft project-plan.md and build-plan.md only after the user is ready. Use for /discovery, guided planning, thinking through a new product, or deepening plans. Never require it before /overview.

discovery - develop the plans through a deep conversation

Context reuse: Reuse any required file already loaded in project instructions or the current session. Read it again only if absent, changed, or exact current bytes or line references are needed.

First action: Before project inspection, preflight, or any other tool call, publish running to blueprint/.state/run.json using the dashboard activity contract in AGENTS.md.

Where this can sit in the workflow:

/onboard  ->  write the plans directly  ->  /overview
          \
           ->  [discovery]  ->  review and approve plan drafts  ->  /overview

/discovery is an optional planning partner, not a required workflow gate and not a quick questionnaire. It can span as many turns as the project needs. Its job is to help the user think through the product, preserve the depth and nuance of that conversation, and draft the two user-owned planning files only when the user asks for drafts.

Running /onboard never starts this skill. Empty plans never require it. A user who writes detailed plans manually, has another AI conversation, or arrives with finished plans continues directly to /overview exactly as before.

For a focused idea or technical tradeoff without drafting plans, use /explore. Discovery develops the broader product plans.

Step 1 - establish the starting point

Read only the planning and project facts needed for the conversation:

  • blueprint/project-plan.md
  • blueprint/build-plan.md
  • the root project manifest, README, and framework configuration when they already contain relevant facts
  • blueprint/context/project-overview.md only when the user is revisiting an established project's direction

Classify each planning file as a template, partial draft, or substantive plan. Never treat existing user content as disposable. When either plan has real content, summarize what it already establishes and ask whether the user wants to deepen it, revise a specific direction, or use it unchanged as conversation context. Do not replace it with a fresh generic plan.

Start with a short working hypothesis about the project and name the most important unknown. Then ask one focused question. Do not draft either plan yet.

Step 2 - run adaptive discovery

Ask one meaningful question at a time and let each answer shape the next one. Prefer a likely interpretation the user can correct over a vague request for more detail. Explain a tradeoff when the answer would materially change scope, architecture, cost, or build order.

Cover the areas that matter to this project, not a fixed questionnaire:

  • problem, desired outcome, and why the project should exist
  • target users, their context, and their primary workflows
  • MVP capabilities, explicit non-goals, and later possibilities
  • business rules, data, integrations, permissions, and important edge cases
  • stack choices, constraints, dependencies, and technical unknowns
  • UI/UX direction, accessibility needs, and useful references
  • monetization or business model when relevant
  • deployment shape, environments, background work, storage, and operations
  • risks, assumptions, unresolved decisions, and how success will be judged
  • feature boundaries, dependencies, and a sensible build order

Depth is the goal. Follow a consequential answer until its implications are clear instead of racing to the next category. Do not ask the user to repeat facts already established in the conversation or repository. Do not force irrelevant topics merely to complete a checklist.

Follow the proportional-engineering contract in AGENTS.md: ask about optional usage or trust constraints only when they materially change the solution, record established non-requirements, and leave unknowns blank.

Periodically return a compact discovery snapshot with:

  • confirmed decisions
  • working assumptions that still need confirmation
  • open questions or conflicts
  • ideas explicitly deferred or excluded

The snapshot keeps a long conversation coherent. It is not permission to write the plans.

Step 3 - decide whether the plans are ready

Do not end discovery because a preset number of questions has been reached. It is ready to draft when:

  • the problem, users, and core workflows are concrete
  • MVP scope and non-goals are distinguishable
  • data and technical choices are detailed enough to expose major dependencies
  • the build order can be expressed as feature-sized outcomes
  • important contradictions are resolved
  • remaining unknowns are either safe to defer or explicitly accepted as TODOs
  • the user says they are ready for the plans to be drafted

If the user asks for drafts while a material gap remains, name the gap and ask whether to continue discovery or preserve it as an explicit TODO. Respect the choice. The user may also stop at any time and write the plans manually.

Show full SKILL.md (441 more words)Show less

Step 4 - draft both planning files

When the user asks for drafts, produce complete proposed contents for both files without writing them yet.

For blueprint/project-plan.md:

  • keep the template's main subject areas, adding useful sections when the conversation requires them
  • preserve rationale, examples, tradeoffs, constraints, edge cases, and exclusions that will matter during later feature work
  • be as detailed as the project needs; never compress a rich discovery into a line or two per section
  • distinguish confirmed decisions from assumptions and TODOs

For blueprint/build-plan.md:

  • use numbered checkboxes and optional milestone headings
  • keep each item a high-level, feature-sized outcome with a concise description
  • order items by dependency and the earliest useful vertical slice
  • keep implementation detail in later /feature specs rather than turning the roadmap into a task dump
  • include only agreed scope; place deferred ideas outside the MVP or omit them as the user directed

If substantive plans already exist, preserve their information and completed build-plan numbering. Clearly identify proposed additions, removals, or changed decisions.

End by asking the user to review the full drafts. Do not write either file in the same response that first presents them.

Step 5 - write only after approval

Write the approved drafts only after the user explicitly approves them. If the user requests changes, revise the drafts and show the affected sections again before writing.

After writing:

  • report which files changed
  • list any retained TODOs or unresolved decisions
  • remind the user that both files remain theirs to edit and deepen directly
  • stop before generating blueprint/context/project-overview.md
  • point to /overview or $overview as the next optional command when the user is satisfied with the plans

Rules

  • This skill is always optional. Never make it a prerequisite for /overview, /feature, or any other Blueprint command.
  • Never start it automatically from /onboard, because planning files are empty, or because a project is new.
  • Never imply that plans created manually or through another conversation are inferior or incomplete merely because this skill was not used.
  • Never overwrite substantive planning content without showing the replacement and receiving explicit approval.
  • Never write plans during the interview or after a vague signal such as "looks good." The user must explicitly approve the proposed file contents.
  • Never scaffold the app, edit product code, generate the overview, create a feature spec, commit, merge, push, or deploy.
  • Preserve detailed project reasoning in project-plan.md, while keeping build-plan.md high-level and trackable.
  • Keep the conversation adaptive. Depth comes from relevant follow-up questions, not from mechanically asking every possible question.

Formatting

Follow blueprint/context/ai-interaction.md. During discovery, ask one focused question per turn. For snapshots and draft reviews, use concise headings and lists so confirmed decisions and remaining gaps are easy to inspect.

© aiblueprinthq, 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 1 other file in .agents/skills/discovery of aiblueprinthq/ai-blueprint.

  • SKILL.md
  • agents/openai.yaml

Open the folder on GitHubat commit 96222b7

Compare with similar skills

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.

Discovery compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Discovery this skillaiblueprinthq/ai-blueprint463—~2.1kAutomated safety check: PassMIT
CCPM Project Managementautomazeio/ccpm8.4k—~1.1kAutomated safety check: PassMIT
Uvastral-sh/claude-code-plugins3132 repos~980Automated safety check: PassApache-2.0
Project Managementkunchenguid/firstmate7.8k—~2.1kAutomated safety check: PassMIT
Hivemind Goalsactiveloopai/hivemind1.6k—~1.7kAutomated safety check: NotesApache-2.0
Ichartjswanghetommy/ichartjs352—~4.3kAutomated safety check: PassApache-2.0

Similar skills

  • Runs a spec-driven workflow from PRD to epic to GitHub issues to parallel agents, with status, standup and blocked-work reports from bundled scripts.

    8.4k GitHub stars~1.1k tokensUpdated 6 mo ago
    Product & Project ManagementAuto-check passed
  • Uv

    astral-sh/claude-code-plugins

    Official

    Guide for using uv, the Python package and project manager. An agent skill from astral-sh/claude-code-plugins.

    313 GitHub starsUsed in 2 repos~980 tokens
    Product & Project ManagementAuto-check passed
  • Project Management

    kunchenguid/firstmate

    Agent-only procedure for Firstmate project management. An agent skill from kunchenguid/firstmate.

    7.8k GitHub stars~2.1k tokensUpdated yesterday
    Product & Project ManagementAuto-check passed
  • Hivemind Goals

    activeloopai/hivemind

    Create, track and update team goals via the Deeplake virtual filesystem at memory/goal/.

    1.6k GitHub stars~1.7k tokensUpdated 12 days ago
    Product & Project ManagementAuto-check: notes
  • Ichartjs

    wanghetommy/ichartjs

    Plan, validate, render, explain, and safely edit iChart.js visualizations from tabular, project, or diagram data.

    352 GitHub stars~4.3k tokensUpdated yesterday
    Product & Project ManagementAuto-check passed
  • Hivemind Goals

    activeloopai/hivemind

    Create, track and update team goals in Hivemind via the hivemind CLI.

    1.6k GitHub stars~814 tokensUpdated 12 days ago
    Product & Project ManagementAuto-check passed

More from aiblueprinthq/ai-blueprint

All 20 skills in this repo
  • Adopt

    aiblueprinthq/ai-blueprint

    Adopt Blueprint into an existing brownfield codebase by surveying shipped behavior and generating plans, standards, commands, adapter choices, and visibility setup.

    463 GitHub stars~2.7k tokensUpdated 2 days ago
    Auto-check passed
  • CI

    aiblueprinthq/ai-blueprint

    Set up or normalize one project Verify command and matching GitHub Actions checks while preserving existing CI, with an optional local pre-push hook.

    463 GitHub stars~2.2k tokensUpdated 2 days ago
    Auto-check passed
  • Doctor

    aiblueprinthq/ai-blueprint

    Run a Blueprint health and context check covering setup, adapters, commands, visibility, plans, overview freshness, configuration, dashboard state, and workflow drift.

    463 GitHub stars~4.5k tokensUpdated 2 days ago
    Auto-check: notes
  • Feature

    aiblueprinthq/ai-blueprint

    Turn the next, named, or numbered build-plan feature into a buildable current-feature.md spec with small steps and done-when criteria.

    463 GitHub stars~2.8k tokensUpdated 2 days ago
    Auto-check passed
  • Onboard

    aiblueprinthq/ai-blueprint

    Onboard a fresh or early scaffold after Blueprint is overlaid by tuning commands, standards, adapters, visibility, and context loading.

    463 GitHub stars~4.5k tokensUpdated 2 days ago
    Auto-check passed
  • Overview

    aiblueprinthq/ai-blueprint

    Validate and normalize project-plan.md and build-plan.md, then generate the durable project-overview.md used by agents.

    463 GitHub stars~3.8k tokensUpdated 2 days ago
    Auto-check passed

Questions about Discovery

What does Discovery do?

Run an optional guided product-discovery interview and draft project-plan.md and build-plan.md only after the user is ready. Discovery is an agent skill from aiblueprinthq/ai-blueprint.md only after the user is ready.

When should I use Discovery?

Discovery fits situations like: guided planning; thinking through a new product; deepening plans.

How do I install Discovery in Claude Code?

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

How do I install Discovery in Codex?

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

Can I use 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 aiblueprinthq/ai-blueprint --skill 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/discovery, .gemini/skills/discovery, .github/skills/discovery and .opencode/skills/discovery in your project.

What does Discovery need to run?

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

Does 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 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 Discovery use?

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 Discovery use?

About 2.1k tokens (SKILL.md is roughly 8.3k 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 Discovery?

Skills that share tags, products or a category with Discovery: CCPM Project Management (automazeio/ccpm, 8.4k stars), Uv (astral-sh/claude-code-plugins, 313 stars), Project Management (kunchenguid/firstmate, 7.8k stars) and Hivemind Goals (activeloopai/hivemind, 1.6k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Discovery?

aiblueprinthq (a GitHub organization) maintains it in aiblueprinthq/ai-blueprint, which has 463 GitHub stars. The repository holds 20 skills in this directory. The repository was last updated on October 8, 2026.

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