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 | $0.00018 | $0.00368 |
| Sonnet 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 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
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.
- 9d ago First seen · 99 lines · 36 tokens per session scan A 2e8f793ce9c6
pm-discovery is an agent published in the GitHub repository smorky850612/Aurakit (41 stars, last pushed 4mo ago), licensed MIT. It adds 36 tokens to every session and 736 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.
Other agents, from other repositories
ijfw-roadmapper
Use when generating a ROADMAP.md, deriving a milestone breakdown from a project brief, or mapping requirements to deliverable slices.
ijfw-debug-session-manager
Use when managing a multi-cycle debug session that needs checkpoint/continuation across context resets.
ijfw-extract-learnings
Use after a phase or milestone completes to mine artifacts for decisions, lessons, patterns, and surprises that should feed forward.
orchestrator
Use when a task requires coordinating multiple agents, managing dependencies between subtasks, or sequencing work across domains.
autopilot
Use when you want to run iterative autonomous development with PRD and progress tracking, fresh context per iteration to avoid context rot.
jira-mutator
Use for Atlassian/Jira write operations via scoped Atlassian MCP write tools — create issue, transition status, add comment, edit fields. Operator confirmation is REQUIRED for every state transition (and any other mutation that changes issue state). Jira access uses runtime-discovered Atlassian MCP write tools; the…