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Thinking Jobs To Be Done

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tjboudreaux
thinking-jobs-to-be-done

Deciding what to build or why adoption fails. Recover the progress users hire a solution for under a circumstance, then rank by outcome and competing workarounds.

Overview

Publishertjboudreaux
Repositorycc-thinking-skills
Skill namethinking-jobs-to-be-done
Stars
1.3K
Forks
158
Bundled files
Instructions only
LicenseMIT
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.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

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

Installation

Install the Thinking Jobs To Be Done 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.
  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/tjboudreaux/cc-thinking-skills.git /tmp/cc-thinking-skills
mkdir -p .claude/skills
cp -r /tmp/cc-thinking-skills/skills/thinking-jobs-to-be-done .claude/skills/thinking-jobs-to-be-done
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Thinking Jobs To Be Done 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 Thinking Jobs To Be Done 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 Thinking Jobs To Be Done 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.

Jobs to Be Done

Core rule: users hire solutions for progress in a situation. Prioritize the job, forces, outcome, and competing workaround—not the feature list.

When to Use

  • Choosing what to build, cut, or prioritize when user need is unclear
  • Explaining low adoption of a shipped feature
  • Mapping competition beyond same-category products (email, spreadsheets, manual work, non-consumption)
  • Positioning or research when the progress sought is contested

Requires at least one evidence source: PRD/spec, tickets, support/sales notes, analytics/logs, or current product behavior. If none exist, name the research gap; do not invent quotes.

When NOT to Use

  • Pure execution once the job is known (bug fix, schema, CI, performance)—implement, do not rediscover the job
  • Retro-justifying a decision already locked—framework theater
  • Infrastructure/internal work with no end-user progress decision
  • When the open question is only how to implement a settled job

Procedure

  1. Name performers and circumstance. Who hires a solution, in what trigger situation, how often, and with what stakes. Prefer primary performers with daily high-stakes jobs over rare secondary ones.
  2. State the job, not the solution. Frame: When [circumstance], I want to [progress], so I can [outcome]. Reject solution-shaped statements ("use Slack", "add a dashboard"). Capture functional, emotional, and social dimensions only if evidence supports them.
  3. Map forces and switch. From artifacts: what push made the old way fail, what pull the new progress offers, what anxiety blocks switching, what habit keeps the status quo. List what they hire today—including non-software and non-consumption.
  4. Define done and outcome metrics. How the performer knows the job is finished. List outcomes to minimize and maximize (time-to-progress, rework, confidence, surprises). Prefer frequent, poorly served jobs over rare, adequately worked-around ones.
  5. Score candidates against the job. For each feature/priority: which job step it serves, performer share, frequency, quality of alternatives. Promote high-frequency underserved dimensions; demote polished work for well-served or low-stakes jobs.
  6. Strongest countercase. State the best case that the stated job is wrong (wrong performer, vanity metric, process-is-the-job, competition is actually non-consumption). If the countercase fits evidence better, revise the job before recommending build.
  7. Stop. Stop when one primary job statement, competing set, and outcome metrics are evidence-backed enough to change a build/position decision—or when artifacts cannot answer and research is the only next step.

Output

Produce a JTBD decision artifact:

text
Job statement: When …, I want to …, so I can …
Performers: primary / secondary (frequency, stakes)
Forces: push / pull / anxiety / habit
Competition: direct | indirect | non-consumption
Outcomes: minimize […] ; maximize […]
Priority implication: build / cut / reposition — because job gap is …
Countercase checked: …
Evidence used / gaps: …

Verification

  • Falsify: If replacing the job statement with a feature name does not change the recommendation, you never left solution-space—rewrite the job from circumstance and progress.
  • Stop: Do not keep mapping job steps once the ranking decision is stable.
  • Over-application guard: Skip on pure implementation tasks and known jobs. Never fabricate user quotes or personas to fill missing evidence.

Frequently asked questions

What does the Thinking Jobs To Be Done AI skill do?

Deciding what to build or why adoption fails. Recover the progress users hire a solution for under a circumstance, then rank by outcome and competing workarounds.

Why use Thinking Jobs To Be Done on TypingMind?

Because you install it once and use it with any model. Thinking Jobs To Be Done 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 Thinking Jobs To Be Done in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/tjboudreaux/cc-thinking-skills/tree/main/skills/thinking-jobs-to-be-done. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Thinking Jobs To Be Done?

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 Thinking Jobs To Be Done?

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

Is the Thinking Jobs To Be Done AI skill free?

Yes. It is published on GitHub by tjboudreaux under the MIT 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.

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