Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/wellapp-ai/well/problem-framingnpx skills add WellApp-ai/Well --skill problem-framinggit clone --depth 1 https://github.com/WellApp-ai/WellWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/wellapp-ai/well/problem-framing)<a href="https://agentmods.dev/skills/wellapp-ai/well/problem-framing"><img src="https://agentmods.dev/badge/skills/wellapp-ai/well/problem-framing.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00019 | $0.00697 |
| Opus 5 | $0.00010 | $0.00349 |
| Sonnet 5 | $0.00004 | $0.00139 |
| Haiku 4.5 | $0.00002 | $0.00070 |
Grade A, and why
problem-framing scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 4d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 127 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Problem Framing Skill
Frame problems effectively using Jobs-to-be-Done, How Might We questions, and persona validation from Notion.
When to Use
- At the start of DIVERGE loop (Ask mode)
- When exploring a new feature or problem space
- Before ideation to ensure clear problem definition
Instructions
Phase 1: Job Story Definition
Create a Job Story in this format:
When [situation/context],
I want to [motivation/action],
So I can [expected outcome/benefit].
Example:
When I'm managing multiple client workspaces,
I want to switch between them quickly,
So I can respond to urgent requests without losing context.
Phase 2: How Might We (HMW) Question
Reframe the problem as an opportunity question:
How might we [opportunity that addresses the job story]?
Guidelines:
- Start broad, then narrow if needed
- Avoid suggesting solutions in the question
- Focus on the user's goal, not the feature
Example:
How might we help users navigate between workspaces seamlessly?
Phase 3: Persona Lookup (Notion MCP)
Fetch relevant personas from Notion database:
-
Search for personas database:
API-post-search with query "Personas" or "User Personas" -
Query the database:
API-query-data-source with database_id from search results -
Get persona details:
API-retrieve-a-page + API-get-block-children for each relevant persona
Extract these fields:
- Name
- Role / Job Title
- Goals (what they want to achieve)
- Pain Points (what frustrates them)
- Context (environment, constraints)
Phase 4: Three Dimensions Check
Validate the problem addresses all three job dimensions:
| Dimension | Question | Example |
|---|---|---|
| Functional | What task are they completing? | "Switch between workspaces" |
| Emotional | How do they want to feel? | "In control, not overwhelmed" |
| Social | How do they want to be perceived? | "Responsive, professional" |
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 4d ago First seen · 127 lines · 19 tokens per session scan A c52ec36a59cd
problem-framing is a skill published in the GitHub repository WellApp-ai/Well (340 stars, last pushed 27d ago), licensed MIT. It adds 19 tokens to every session and 697 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
auto-reader-ocr
Extract text from images/PDFs (Arabic-first, manga-aware Japanese, 13+ languages auto-detected), translate between 100+ languages, and pull structured fields from invoices/receipts/IDs via the Auto-Reader OCR API. Use when the user shares a document image or PDF to read, translate, or extract data from — or asks for…
nextjs-pages-router
Set up tRPC in Next.js Pages Router with createNextApiHandler, createTRPCNext, withTRPC HOC, SSR via ssr option and ssrPrepass, SSG via createServerSideHelpers with getStaticProps, and server-side helpers for getServerSideProps prefetching.
react-query-setup
Set up @trpc/tanstack-react-query with createTRPCContext(), TRPCProvider, useTRPC() hook, queryOptions/mutationOptions factories, query invalidation via queryClient.invalidateQueries with queryFilter, and type inference with inferInput/inferOutput.
non-json-content-types
Handle FormData, file uploads, Blob, Uint8Array, and ReadableStream inputs in tRPC mutations. Use octetInputParser from @trpc/server/http for binary data. Route non-JSON requests with splitLink and isNonJsonSerializable() from @trpc/client. FormData and binary inputs only work with mutations (POST).
openapi-glossary
Use consistent OpenAPI terminology and definitions when writing documentation, educational material, and tooling guidance.
scalar-docs
Skill for writing and updating scalar.config.json — Scalar Docs configuration reference for users and LLMs.