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/bmbouter/redhat-agents/market-problem-definitionnpx skills add bmbouter/redhat-agents --skill market-problem-definitiongit clone --depth 1 https://github.com/bmbouter/redhat-agentsWrote 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/bmbouter/redhat-agents/market-problem-definition)<a href="https://agentmods.dev/skills/bmbouter/redhat-agents/market-problem-definition"><img src="https://agentmods.dev/badge/skills/bmbouter/redhat-agents/market-problem-definition.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 | $0.00025 | $0.00648 |
| Opus 5 | $0.00013 | $0.00324 |
| Sonnet 5 | $0.00005 | $0.00130 |
| Haiku 4.5 | $0.00003 | $0.00065 |
Grade A, and why
market-problem-definition 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.
How it starts
The opening of the file, as written. The whole thing — 92 lines — stays where its author put it; the contents beside it link to each section on GitHub.
When to Use
When the PM is exploring a new product direction, evaluating a feature area, or needs to articulate the market problem behind a set of customer requests.
Instructions
1. Start with the raw signal
Ask the PM what triggered this analysis:
- Customer feedback or requests?
- Competitive pressure?
- Strategic initiative from leadership?
- Emerging market trend?
2. Interview to build the problem definition
Walk through each dimension. Ask questions conversationally — don't dump a form.
Customer segments:
- Who experiences this problem? Be specific — not "developers" but "platform engineers managing 50+ microservices."
- Are there distinct segments with different versions of the problem?
- Which segment is most underserved?
Pain points:
- What specifically is painful today? How do they work around it?
- What triggers the pain? (frequency, severity, context)
- What does it cost them? (time, money, reliability, developer experience)
Current alternatives:
- How do they solve this today? (our product, competitor, manual process, nothing)
- What's good and bad about each alternative?
- Why haven't existing alternatives fully solved it?
Desired outcomes:
- What would "solved" look like from the customer's perspective?
- What would they be able to do that they can't do now?
- How would they measure success?
Opportunity sizing:
- How many customers/teams face this problem?
- Is the affected population growing or shrinking?
- Is this a blocker (must-have) or a nice-to-have?
- Any evidence from Jira? (query for related issues to quantify)
3. Produce the problem definition document
## Market Problem Definition: [Problem Name]
### Problem Statement
[One paragraph: who has this problem, what the problem is, and why it matters]
### Customer Segments
| Segment | Description | Severity | Size |
|---------|-------------|----------|------|
| [Segment 1] | ... | High/Med/Low | ... |
### Pain Points
1. [Pain point] — triggered by [context], costs [impact]
2. ...
### Current Alternatives
| Alternative | Strengths | Weaknesses |
|-------------|-----------|------------|
| [Current product capability] | ... | ... |
| [Competitor X] | ... | ... |
| [Manual workaround] | ... | ... |
### Desired Outcomes
- [Outcome 1 — measurable]
- [Outcome 2]
### Opportunity Assessment
- **Affected customers**: [estimate with evidence source]
- **Trend**: Growing / Stable / Declining
- **Blocker vs nice-to-have**: ...
- **Jira evidence**: X related issues from Y reporters (see customer-signal-aggregator output if available)
### Strategic Alignment
[How this connects to broader product/company strategy]
### Open Questions
- [What we don't know yet and how to find out]
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 · 92 lines · 25 tokens per session scan A dbd5df5e0618
market-problem-definition is a skill published in the GitHub repository bmbouter/redhat-agents (5 stars, last pushed 2mo ago), licensed Apache-2.0. It adds 25 tokens to every session and 648 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 2026-08-31.
Other skills, from other repositories
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
local-ai-agents
Build local-first AI agents that run entirely on a developer workstation with Microsoft Foundry Local and Qwen function-calling models. Covers Small Language Models (SLMs), the OpenAI-compatible local endpoint, sandboxed local tools, local RAG with Chroma, local MCP servers, hybrid cloud/local routing, and the…
chronicle
Analyze Copilot session history for standup reports, usage tips, session search, and session reindexing. Use when the user asks for a standup, daily summary, usage tips, workflow recommendations, wants to search or find past sessions by keyword/file/PR, wants to reindex their session store, or asks about deleting…
imagegen
Generate or edit raster images when the task benefits from AI-created bitmap visuals such as photos, illustrations, textures, sprites, mockups, or transparent-background cutouts. Use when Codex should create a brand-new image, transform an existing image, or derive visual variants from references, and the output…
chat-pet-sprite-creation
Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.
cpu-profile-analysis
Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…