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 skills add stanislavnianko/product-discovery-claude-skills --skill problem-framinggit clone --depth 1 https://github.com/stanislavnianko/product-discovery-claude-skillsWrote 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/stanislavnianko/product-discovery-claude-skills/problem-framing)<a href="https://agentmods.dev/skills/stanislavnianko/product-discovery-claude-skills/problem-framing"><img src="https://agentmods.dev/badge/skills/stanislavnianko/product-discovery-claude-skills/problem-framing/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/stanislavnianko/product-discovery-claude-skills/problem-framing"><img src="https://agentmods.dev/badge/skills/stanislavnianko/product-discovery-claude-skills/problem-framing.svg" alt="Reviewed on agentmods" width="80" 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 5.1 | $0.00061 | $0.01086 |
| Opus 5 | $0.00030 | $0.00543 |
| Sonnet 5 | $0.00012 | $0.00217 |
| Haiku 4.5 | $0.00006 | $0.00109 |
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 12d 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 — 78 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Problem Framing
Part of the discovery-phase skill pack ·
discoverygroup · readsdiscovery-context.md(runprofile-builderfirst if missing).
Turns whatever the client said into a testable, falsifiable hypothesis. If the client already proposed a solution (very common in outsourcing), this skill un-pitches it back into a problem statement before anything downstream runs.
Step 1 — Read discovery context
Read discovery-context.md (sections 1. Client, 2. Product / Initiative). If section 2 says the client already proposed a solution, flag it — this skill will need to extract the underlying problem from that solution rather than starting clean.
If discovery-context.md is missing, ask the BA inline: "(a) client name + sector; (b) what's being explored in one line; (c) did the client propose the solution or did the agency?" — tag any unverified canvas field as [ASSUMED]. Never block; recommend profile-builder for high-stakes work.
Step 2 — Forbid solution-speak (mid-skill rule)
Apply this rule throughout: any phrasing like "build X", "add Y feature", "implement Z" is intercepted and reframed as "what outcome would X produce, and why is that outcome missing today?"
This applies even to the client's own framing. If the discovery-context says they want "an AI assistant", the canvas asks: "What job is the AI assistant supposed to do, and what's broken about how that job gets done today?"
Step 3 — Fill the canvas
Walk the BA through the canvas. Each section is 1-3 sentences max.
- Problem statement — who has the problem, in what context, what outcome they're not getting
- Why now — what changed in the world / market / client's business that makes this worth solving this quarter
- Target user — role, segment, size; if B2B, name the buyer AND the end user if different (often same in SMB, different in enterprise)
- Current workaround — how the user solves this today (Excel, manual ops, a competitor, contractors, doing nothing)
- Success signal — metric + direction + rough magnitude (NOT a feature; a behavior or outcome)
- Out of scope — explicit exclusions
- Open questions — 3-5 items research must answer
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 12d ago First seen · 78 lines · 61 tokens per session scan A 514b19d33c91
problem-framing is a skill published in the GitHub repository stanislavnianko/product-discovery-claude-skills (1 stars, last pushed 4mo ago), licensed MIT. It adds 61 tokens to every session and 1,086 once invoked, about $0.0003 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 2026-08-31.
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