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 aleksander-dytko/ai-pm-workspace --skill opportunity-solution-treegit clone --depth 1 https://github.com/aleksander-dytko/ai-pm-workspaceWrote 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/aleksander-dytko/ai-pm-workspace/opportunity-solution-tree)<a href="https://agentmods.dev/skills/aleksander-dytko/ai-pm-workspace/opportunity-solution-tree"><img src="https://agentmods.dev/badge/skills/aleksander-dytko/ai-pm-workspace/opportunity-solution-tree/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/aleksander-dytko/ai-pm-workspace/opportunity-solution-tree"><img src="https://agentmods.dev/badge/skills/aleksander-dytko/ai-pm-workspace/opportunity-solution-tree.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 | $0.00024 | $0.00950 |
| Opus 5.5 | $0.00010 | $0.00380 |
| Sonnet 5.5 | $0.00005 | $0.00190 |
| Haiku 4.5 | $0.00002 | $0.00095 |
Grade A, and why
opportunity-solution-tree 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 1mo 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 — 73 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Opportunity Solution Tree (OST)
A visual framework for structuring continuous product discovery. Connects a desired outcome to customer opportunities, possible solutions, and experiments to validate them.
Domain Context
The Opportunity Solution Tree (Teresa Torres, Continuous Discovery Habits) is the backbone of modern product discovery. It prevents teams from jumping to solutions by forcing them to first map the opportunity space.
Structure (4 levels):
-
Desired Outcome (top) - The measurable business or product outcome the team is pursuing. Should be a single, clear metric (e.g., "increase 7-day retention to 40%"). This comes from your OKRs or product strategy.
-
Opportunities (second level) - Customer needs, pain points, or desires discovered through research. These are problems worth solving - not features. Frame them from the customer's perspective: "I struggle to..." or "I wish I could..." Prioritize using Opportunity Score: Importance * (1 - Satisfaction) (Dan Olsen, The Lean Product Playbook). Normalize Importance and Satisfaction to 0-1.
-
Solutions (third level) - Possible ways to address each opportunity. Generate multiple solutions per opportunity - don't commit to the first idea. The Product Trio (PM + Designer + Engineer) should ideate together. "Best ideas often come from engineers."
-
Experiments (bottom) - Fast, cheap tests to validate whether a solution actually addresses the opportunity. Use assumption testing (Value, Usability, Viability, Feasibility risks). Prefer experiments with "skin-in-the-game" (Alberto Savoia) over opinion-based validation.
Key principles:
- One outcome at a time. Don't try to solve everything. Focus the tree on a single desired outcome.
- Opportunities, not features. "Never allow customers to design solutions. Prioritize opportunities (problems), not features."
- Compare and contrast. Always generate at least 3 solutions per opportunity before choosing. Avoid the "first idea" trap.
- Discovery is not linear. Loop back if experiments fail. Kill solutions that don't validate. Explore new branches.
- Continuous, not periodic. Update the tree weekly as you learn from interviews, analytics, and experiments.
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.
- 1mo ago First seen · 73 lines · 24 tokens per session scan A 288643974285
opportunity-solution-tree is a skill published in the GitHub repository aleksander-dytko/ai-pm-workspace (34 stars, last pushed 6mo ago), licensed MIT. It adds 24 tokens to every session and 950 once invoked, about $0.0001 per session on Opus 5.5. 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.
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