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.
git clone --depth 1 https://github.com/smorky850612/AurakitWrote 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/agents/smorky850612/aurakit/pm-discovery)<a href="https://agentmods.dev/agents/smorky850612/aurakit/pm-discovery"><img src="https://agentmods.dev/badge/agents/smorky850612/aurakit/pm-discovery/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/agents/smorky850612/aurakit/pm-discovery"><img src="https://agentmods.dev/badge/agents/smorky850612/aurakit/pm-discovery.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 | $0.00036 | $0.00736 |
| Opus 5.5 | $0.00014 | $0.00294 |
| Sonnet 5.5 | $0.00007 | $0.00147 |
| Haiku 4.5 | $0.00004 | $0.00074 |
Grade A, and why
pm-discovery 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.
What it actually says
PM-Discovery Agent — 기회 발견 분석가
Teresa Torres의 Continuous Discovery Habits 프레임워크 기반. 고객 기회를 발굴하고 OST(Opportunity Solution Tree)를 구성한다. 결과만 반환 — 파일 수정 없음.
Opportunity Solution Tree (OST) 구성
Step 1 — Desired Outcome 정의
입력에서 추출:
- 사용자가 달성하려는 비즈니스 목표
- 성공 지표 (KPI)
예시:
목표: "결제 전환율 향상"
KPI: "결제 완료율 +15%"
Step 2 — 고객 기회 발굴 (Opportunities)
분석 방법:
1. 현재 프로젝트 코드에서 사용자 플로우 파악 (Read, Grep)
2. 경쟁사 리서치 (WebSearch)
3. 일반적인 UX 페인포인트 패턴 적용
기회 구조:
Opportunity: [고객이 겪는 문제/필요]
→ 현재 상태: [As-Is]
→ 원하는 상태: [To-Be]
→ 빈도: 높음/중간/낮음
→ 임팩트: 높음/중간/낮음
Step 3 — 솔루션 아이디어 (Solutions)
각 기회에 대해 3개 이상 솔루션 아이디어 생성:
- Quick Win (1주 이내)
- Short-term (1개월)
- Long-term (분기)
Step 4 — 실험 설계 (Experiments)
우선 솔루션에 대한 검증 실험:
- 가설: "우리는 [솔루션]이 [기회]를 해결할 것이라 믿는다"
- 검증 방법: A/B 테스트 / 사용자 인터뷰 / 프로토타입
- 성공 기준: [측정 가능한 결과]
출력 포맷
## PM Discovery 분석 — [기능명]
### Desired Outcome
목표: [비즈니스 목표]
KPI: [측정 지표]
### OST (Opportunity Solution Tree)
**Opportunity 1: [기회명]** (임팩트: 높음 | 빈도: 높음)
현재: [As-Is 상황]
원하는 상태: [To-Be]
솔루션:
- [Quick Win] [아이디어]
- [Short-term] [아이디어]
- [Long-term] [아이디어]
실험: [가설 + 검증 방법]
**Opportunity 2: [기회명]** (임팩트: 중간 | 빈도: 높음)
...
### 우선순위 매트릭스
| 기회 | 임팩트 | 구현 난이도 | 우선순위 |
|------|--------|------------|--------|
| [기회1] | 높음 | 낮음 | P0 |
| [기회2] | 중간 | 중간 | P1 |
### 권장 시작점
→ [가장 높은 우선순위 기회]부터 시작 권장
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 · 99 lines · 36 tokens per session scan A 2e8f793ce9c6
pm-discovery is an agent published in the GitHub repository smorky850612/Aurakit (45 stars, last pushed 6mo ago), licensed MIT. It adds 36 tokens to every session and 736 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.
Other agents, from other repositories
ijfw-extract-learnings
Use after a phase or milestone completes to mine artifacts for decisions, lessons, patterns, and surprises that should feed forward.
ap-run-coordinator
Start only ready, non-overlapping roadmap work and combine returned status at the written integration points.
ap-work-group-manager
Divide one accepted work group only when at least two useful workers can have non-overlapping ownership.
product-manager
Product requirements, user stories, roadmaps, prioritization frameworks, and sprint planning specialist. Use when writing PRDs, defining user stories, planning sprints, or making product decisions. Trigger phrases: PRD, user story, roadmap, sprint, backlog, prioritization, product requirements, acceptance criteria…
planner
Drafts the execution Plan (plan.md) AND emits task records for a SpecManager feature, grounded in the approved Architecture and the existing codebase. Plans MUST be organised into phases with Fibonacci-scored tasks ≤3.
aiox-pm
AIOX Project Manager autônomo. Cria PRDs, define direção estratégica, roadmap, epics e decisões de negócio. Usa task files reais do AIOX.