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 itseffi/agentic-os --skill opportunity-solution-treegit clone --depth 1 https://github.com/itseffi/agentic-osWrote 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/itseffi/agentic-os/opportunity-solution-tree)<a href="https://agentmods.dev/skills/itseffi/agentic-os/opportunity-solution-tree"><img src="https://agentmods.dev/badge/skills/itseffi/agentic-os/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/itseffi/agentic-os/opportunity-solution-tree"><img src="https://agentmods.dev/badge/skills/itseffi/agentic-os/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 | $0.00028 | $0.00483 |
| Opus 5.5 | $0.00011 | $0.00193 |
| Sonnet 5.5 | $0.00006 | $0.00097 |
| Haiku 4.5 | $0.00003 | $0.00048 |
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 — 88 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Opportunity Solution Tree (OST)
Transform interview data into a structured Opportunity Solution Tree following Teresa Torres's method.
When to Use
When synthesizing user research into a structured view of opportunities.
Key Concepts
Opportunity: A customer need, pain, or desire - phrased from user perspective, not a solution.
Moment in time: A distinct point in the customer journey.
Tests:
- Distinctness (siblings): Can we pursue one without addressing the other? If yes, they're distinct.
- Parent-child: Does solving the child partially solve the parent? If not, reframe.
The Process
1. Extract Opportunities
From interviews, capture:
- Verbatim quotes
- Reframed user-need statement
- Which journey moment it belongs to
2. Organize by Journey Moments
Group opportunities under their primary moment in time.
3. Structure Within Each Moment
- Cluster similar opportunities
- Create parent nodes where needed
- Run distinctness checks across siblings
- Run parent-child checks down branches
- Remove generic parents with single children
- Combine near-duplicates
4. Track Evidence
For each opportunity, capture:
- Representative quotes
- Frequency count
- Confidence level (low/med/high)
5. Prioritize Leaves
Score leaf opportunities:
- Impact (1-5)
- Frequency (1-5)
- Alignment to outcome (1-5)
- Priority = Impact x Frequency x Alignment
Output Format
Opportunity Inventory Table: | ID | Moment | Opportunity | Parent ID | Quotes | Frequency | Confidence |
Tree View (per moment):
## [Moment Name]
- [Parent opportunity]
- [Child opportunity]
- [Grandchild]
Prioritized Leaf Backlog: Ranked list with scores and rationale.
Rules
- All opportunities from user perspective
- No solutions in the tree
- Keep distinct across siblings
- Prefer specific over generic
- Preserve verbatim quotes for evidence
When Not to Use
Do not use this skill when the request is unrelated, low-stakes, or better handled by a simpler direct response.
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 · 88 lines · 28 tokens per session scan A 0d8526be18a8
opportunity-solution-tree is a skill published in the GitHub repository itseffi/agentic-os (117 stars, last pushed 15d ago), licensed MIT. It adds 28 tokens to every session and 483 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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