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 Adelie-Squad/solosquad --skill opportunity-treegit clone --depth 1 https://github.com/Adelie-Squad/solosquadWrote 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/adelie-squad/solosquad/opportunity-tree)<a href="https://agentmods.dev/skills/adelie-squad/solosquad/opportunity-tree"><img src="https://agentmods.dev/badge/skills/adelie-squad/solosquad/opportunity-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/adelie-squad/solosquad/opportunity-tree"><img src="https://agentmods.dev/badge/skills/adelie-squad/solosquad/opportunity-tree.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00045 | $0.00815 |
| Opus 5 | $0.00023 | $0.00407 |
| Sonnet 5 | $0.00009 | $0.00163 |
| Haiku 4.5 | $0.00005 | $0.00081 |
Grade A, and why
opportunity-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 9d 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.
What it actually says
Opportunity Tree Skill
Teresa Torres CDH (Continuous Discovery Habits) 의 OST 자체 실행. Outcome 에서 시작해 opportunity, solution, experiment 까지 트리 형태로 분해.
구조
Outcome (team OKR 의 KR)
└─ Opportunity 1 (사용자 unmet need)
├─ Solution 1.1
│ └─ Experiment 1.1.a
└─ Solution 1.2
└─ Experiment 1.2.a
└─ Opportunity 2
├─ Solution 2.1
└─ Solution 2.2
입력
- team OKR (Layer 4a)
discovery-synthesis출력 (JTBD signals)
Six Forcing Questions (gstack 차용) — 자가검증 체크리스트
각 opportunity / solution 에 대해 PM 이 스스로 답해본다. 답할 수 없으면 → open_questions[]:
- Demand Reality: "사용자가 진짜 원하는가, 흥미만 표현하나?"
- Interest is not demand.
- Status Quo: "현재 사용자가 이미 어떻게 해결하나?"
- Status quo is your real competitor.
- Desperate Specificity: "Y segment 가 X 를 지금 절박하게 원하나?"
- Narrowest Wedge: "가장 좁고 깊은 진입점은?"
- Observation & Surprise: "예상 못한 패턴 발견했나?"
- Future-Fit: "이 솔루션이 6개월 뒤에도 유효한가?"
출력
{
"outcome": "OKR KR 인용",
"tree": [
{
"opportunity": "...",
"jtbd_ref": "...",
"six_questions": {
"demand_reality": "...",
"status_quo": "...",
"desperate_specificity": "...",
"narrowest_wedge": "...",
"observation": "...",
"future_fit": "..."
},
"solutions": [
{
"title": "...",
"approach": "...",
"experiments": [
{ "spec": "...", "metric": "...", "expected": "..." }
]
}
]
}
],
"open_questions": [...]
}
HARD GATE
- Six Forcing Questions 6 항목 모두 자가응답 또는 open_question
- Opportunity 당 Solution ≥2 (gstack rule)
- Solution 당 Experiment ≥1
Anti-Sycophancy
- ❌ "이 opportunity 가 흥미롭습니다"
- ✅ "이 opportunity 는 demand 신호 N건, status quo 분석 Y. Z 가 사실로 드러나면 우선순위 낮춤."
Reference
- Teresa Torres "Continuous Discovery Habits"
- gstack
/office-hoursSix Forcing Questions - phuryn/pm-skills/pm-product-discovery/opportunity-solution-tree
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.
- 9d ago First seen · 107 lines · 45 tokens per session scan A b6eb828a6b88
opportunity-tree is a skill published in the GitHub repository Adelie-Squad/solosquad (19 stars, last pushed 19d ago), licensed MIT. It adds 45 tokens to every session and 815 once invoked, about $0.0002 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 2026-08-30.
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