Discovery Research Stinger logo

Discovery Research Stinger

Organization
legioncodeinc
discovery-research-stinger

Run pre-build discovery: interviews, assumption tests, demand evidence, continue/pivot/stop decisions, and first-version scope. Read README.md for guides.

Overview

Publisherlegioncodeinc
Repositoryvibe-coding-tools
Skill namediscovery-research-stinger
Stars
94
Forks
44
Bundled files
23
LicenseAGPL-3.0-or-later
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 23 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by legioncodeinc on GitHub. Read the source before you install it.

Installation

Install the Discovery Research Stinger AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/legioncodeinc/vibe-coding-tools/tree/main/plugins/business-and-product-tools/skills/discovery-research-stinger
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/legioncodeinc/vibe-coding-tools.git /tmp/vibe-coding-tools
mkdir -p .claude/skills
cp -r /tmp/vibe-coding-tools/plugins/business-and-product-tools/skills/discovery-research-stinger .claude/skills/discovery-research-stinger
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Discovery Research Stinger in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Discovery Research Stinger on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Discovery Research Stinger is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

discovery-research Stinger

Procedural arsenal for discovery-research-wasp-drone, the Legion Army's continuous-discovery coach. This stinger encodes Teresa Torres' continuous-discovery framework, Opportunity Solution Trees (OST), Jobs-to-be-Done (JTBD) interview methodology, assumption mapping, and lightweight prototype experiment design.

The stinger's job is to give the Angel specific, opinionated playbooks - not generic UX theory. Every guide cites primary research. Every template is ready to fill in. Every example shows a real output shape.


When this stinger applies

Load this stinger when discovery-research-wasp-drone is invoked. Typical triggers:

  • "Help me figure out what to build next."
  • "Create an opportunity solution tree for [product/team]."
  • "Write a user interview script for [opportunity]."
  • "Map the assumptions behind [solution]."
  • "Design an experiment to test [assumption]."
  • "We need to do customer discovery before writing the PRD."
  • "Run continuous discovery for [desired outcome]."

Do NOT load for:

  • Usability testing of shipped features → quality-wasp-drone.
  • Authoring a PRD or feature spec → library-wasp-drone.
  • UI/UX design decisions → ux-ui-wasp-drone.
  • Shipped-product analytics, statistical significance, or causal experiment interpretation; route to a qualified analytics owner. Narrow pre-build synthesis against declared assumptions uses guide 08.

First action when this stinger is loaded

Read these in order before doing anything else:

  1. guides/00-principles.md - the continuous-discovery philosophy, the three tenets, the "build less, learn more" manifesto, and the critical directives.
  2. guides/01-desired-outcome.md - how to scope a desired outcome before any other work begins.
  3. guides/02-opportunity-solution-tree.md - OST node taxonomy, snapshot rules, common failure modes.

Then load domain-specific guides as the session requires:

  • guides/03-interview-cadence.md - when to run interviews and how to recruit.
  • guides/04-jtbd-interview.md - the Five-Act interview structure.
  • guides/05-assumption-mapping.md - DVFU 2×2 and Kill Zone protocol.
  • guides/06-experiment-design.md - prototype archetypes and success criteria.
  • guides/07-test-demand-before-building.md - declaration, participant/channel provenance, proxies, counterevidence, feasibility, and criteria changes.
  • guides/08-continue-pivot-or-stop.md - one scoped advisory decision from actual pre-build evidence.
  • guides/09-scope-first-version.md - evidence-linked first-version discovery handoff, without PRD authorship.

Procedure (how the Angel uses this stinger)

1. Anchor to a desired outcome

If no outcome is stated, run the outcome-scoping interview from guides/01-desired-outcome.md. Nothing else starts until a single measurable outcome is defined.

2. Build or update the OST

Read or create library/discovery/opportunity-solution-tree.md using the structure from guides/02-opportunity-solution-tree.md and templates/opportunity-solution-tree.md.

3. Generate an interview script

For a target opportunity node, produce a JTBD-style script using guides/04-jtbd-interview.md and templates/interview-script.md. Write to library/discovery/interview-scripts/<YYYY-MM-DD>-<opportunity-slug>.md.

4. Map assumptions

For a chosen solution, run the DVFU 2×2 protocol from guides/05-assumption-mapping.md using templates/assumption-map.md. Write to library/discovery/assumption-maps/<solution-slug>.md.

5. Design an experiment

For the highest-risk assumption, design the smallest invalidating experiment using guides/06-experiment-design.md and templates/experiment-plan.md. Write to library/discovery/experiments/<YYYY-MM-DD>-<experiment-slug>.md.

6. Test demand and make one scoped next decision

Use guides 07 and 08 with templates/demand-test-record.json, references/evidence-grades.md, and templates/discovery-decision.md. Declare the assumption, exposure, metric, proxy limitation, threshold, sample conditions, source/channel, and consent procedure before an authorized test. Preserve counterevidence and criteria changes. Choose one scoped CONTINUE, PIVOT, or STOP recommendation; CONTINUE discovery is not permission to build. Planned tests remain unexecuted. The checker validates bookkeeping only, never demand or conclusion correctness.

7. Scope the first version for handoff

Use guide 09 and templates/first-version-discovery-brief.md for one customer job, essential/manual/deferred/excluded items, constraints, unresolved assumptions, and acceptance observations. Preserve Accepted requirements. Hand formal requirements to library-wasp-drone after owner scope acceptance and the required tested assumption evidence; otherwise label the handoff unresolved discovery.

8. Summarize for stakeholders (optional)

On demand, produce a one-page weekly discovery summary using templates/weekly-summary.md.


Critical directives

  • Never recommend building without at least one validated assumption test. Why: the "build less, learn more" loop exists to prevent building on wrong assumptions; skipping it is the failure mode continuous discovery is designed to catch. (Source: research/external/2026-05-20-torres-2026-roadmap-ai-discovery.md [source-repository research, not shipped with the plugin])
  • Always anchor work to a single desired outcome. Why: OSTs without a defined outcome become wish lists. (Source: research/external/2026-05-20-opportunity-solution-tree-guide-2026.md [source-repository research, not shipped with the plugin])
  • Distinguish opportunities (problems/desires) from solutions (product ideas). Why: conflating the two is the most common discovery anti-pattern. (Source: research/external/2026-05-20-opportunity-solution-tree-guide-2026.md [source-repository research, not shipped with the plugin])
  • Use Torres' weekly cadence as the default structure. Why: continuous discovery requires rhythm; ad-hoc interviews generate anecdotes, not patterns. (Source: research/external/2026-05-20-continuous-discovery-habits-operationalized-2026.md [source-repository research, not shipped with the plugin])
  • Ask "what's the story?" before coding any interview insight. Why: JTBD is story-based; jumping to themes before hearing the full narrative misses the motivation structure. (Source: research/external/2026-05-20-jtbd-switch-interview-moesta-method.md [source-repository research, not shipped with the plugin])
  • Do not produce a PRD or implementation plan. Why: that is library-wasp-drone's job; hand off a validated opportunity + winning solution, not a spec.

Additive evidence and qualification

The 2026-10-07 living-primary refresh is recorded in references/research/demand-decision/ (manifest and cited distillation). That folder is source-repository research that is not shipped with the plugin, so do not require it; guides/07-test-demand-before-building.md and research/research-summary.md carry the shipped conclusions. Preserve the original research/ archive. examples/demand-decision-cases.md contains labeled synthetic weak-evidence, counterevidence, missing-data, small-test, and economic-stop cases. Run python <checked-skill-root>/scripts/check-demand-record.py <approved-record-path> for bounded bookkeeping; the unfilled template intentionally requires completion. scripts/test-demand-record.py verifies malformed and boundary input handling. Neither script authenticates sources or proves an experiment ran.

Folder layout

discovery-research-stinger/
+- SKILL.md                          (this file)
+- README.md                         (one-page human overview)
+- guides/
|  +- 00-principles.md               (philosophy, three tenets, critical directives)
|  +- 01-desired-outcome.md          (outcome scoping, three-part test)
|  +- 02-opportunity-solution-tree.md (OST taxonomy, snapshot protocol, failure modes)
|  +- 03-interview-cadence.md        (weekly cadence, recruiting, structure)
|  +- 04-jtbd-interview.md           (Five-Act structure, forces diagram)
|  +- 05-assumption-mapping.md       (DVFU 2x2, Kill Zone protocol)
|  +- 06-experiment-design.md        (four archetypes, success criteria)
+- examples/
|  +- happy-path-saas-onboarding.md  (worked OST + interview + experiment for SaaS)
|  +- edge-case-b2b-stakeholders.md  (discovery in a complex B2B buying environment)
+- templates/
|  +- opportunity-solution-tree.md   (OST skeleton)
|  +- interview-script.md            (Five-Act script scaffold)
|  +- assumption-map.md              (DVFU 2x2 table)
|  +- experiment-plan.md             (experiment brief skeleton)
|  +- weekly-summary.md              (stakeholder summary one-pager)
+- reports/
|  +- README.md                      (describes what past-run summaries look like)
+- research/                         (populated by scripture-historian; DO NOT MODIFY)
   +- research-plan.md
   +- research-summary.md
   +- index.md
   +- internal/
   +- external/

Refresh cadence

  • The procedural guides (00- through 06-) align with Teresa Torres' continuous discovery framework (stable since 2021 Continuous Discovery Habits). Refresh when Torres publishes a major framework update or when a new OST tooling standard emerges.
  • The research folder covers the Nov 2025 - May 2026 window. Re-run scripture-historian at normal tier if a significant new source (e.g., a new Torres book/course, a major JTBD update from Moesta) emerges.
  • Templates are stable; no cadence refresh expected.

Procedure: skill procedure Part of the Legion Army forged by Mario Aldayuz a.k.a @thenotoriousllama.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Discovery Research Stinger AI skill do?

Run pre-build discovery: interviews, assumption tests, demand evidence, continue/pivot/stop decisions, and first-version scope. Read README.md for guides.

Why use Discovery Research Stinger on TypingMind?

Because you install it once and use it with any model. Discovery Research Stinger is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Discovery Research Stinger in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/legioncodeinc/vibe-coding-tools/tree/main/plugins/business-and-product-tools/skills/discovery-research-stinger. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Discovery Research Stinger?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Discovery Research Stinger?

As many as you like. As long as a model supports skills, you can use Discovery Research Stinger with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Discovery Research Stinger AI skill free?

Yes. It is published on GitHub by legioncodeinc under the AGPL-3.0-or-later license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

View all

Set up your own AI workspace now

Get notified about new features and future giveaways by subscribing to our newsletter 👇