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/avyayalaya/agent-prime/problem-framingnpx skills add Avyayalaya/agent-prime --skill problem-framinggit clone --depth 1 https://github.com/Avyayalaya/agent-primeWrote 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/avyayalaya/agent-prime/problem-framing)<a href="https://agentmods.dev/skills/avyayalaya/agent-prime/problem-framing"><img src="https://agentmods.dev/badge/skills/avyayalaya/agent-prime/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 | $0.00103 | $0.18577 |
| Opus 5 | $0.00051 | $0.09288 |
| Sonnet 5 | $0.00021 | $0.03715 |
| Haiku 4.5 | $0.00010 | $0.01858 |
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 5d 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- problem-framing — 97% identical, 113 lines differ
How it starts
The opening of the file, as written. The whole thing — 1,100 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Purpose
Produce a Problem Definition Document — a structured artifact that decomposes a vague or assumed problem into an evidence-graded problem statement with identified stakeholders, quantified opportunity size, mapped constraints, and prioritized sub-problems. This is the upstream skill that feeds every downstream PM activity: Discovery, Competitive Analysis, Spec Writing, and Metric Design. The output is not a brief or a pitch — it is a rigorous decomposition that forces the question "What exactly are we solving and for whom?" to be answered with evidence, not assumption.
When to Use / When NOT to Use
Use this skill when:
- A stakeholder says "we should build X" and you need to determine whether X solves a real problem
- You have a vague problem area ("user retention is bad") that needs decomposition into solvable sub-problems
- Multiple teams disagree on what the real problem is — you need a shared, evidence-graded definition
- You are starting a new product initiative and need to define the problem space before Discovery or Competitive Analysis
- A feature request arrives disguised as a problem statement and you need to peel back to the actual user pain
- You need to prioritize across multiple candidate problems with limited resources
- An existing product is underperforming and nobody agrees on why
Do NOT use this skill when:
- The problem is already well-defined and validated — you need a spec, not more framing (use Spec Writing)
- You need to understand the competitive landscape (use Competitive & Market Analysis — that skill consumes this one's output)
- You need to design metrics for an already-defined feature (use Metric Design & Experimentation)
- You need to run user research — this skill identifies what research to do, not how to do it (use Discovery & Research)
- The "problem" is a technical bug with a known root cause (use your bug tracker)
Anti-inputs (what this skill does NOT handle):
- Solution design or feature specification (-> Spec Writing skill)
- User research methodology and interview design (-> Discovery & Research skill)
- Competitive positioning (-> Competitive & Market Analysis skill)
- Metric hierarchy and experiment design (-> Metric Design & Experimentation skill)
- Organizational change management (this is product problem framing, not org design)
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.
- 5d ago First seen · 1,100 lines · 103 tokens per session scan A 4648ccc2da85
problem-framing is a skill published in the GitHub repository Avyayalaya/agent-prime (8 stars, last pushed 3mo ago), licensed MIT. It adds 103 tokens to every session and 18,577 once invoked, about $0.0005 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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