정보
이 스킬은 기존 경쟁사 스냅샷 대비 자동화된 델타 모니터링을 수행하며, 중대한 변경사항만 인용된 증거와 함께 보고하고 배틀카드 업데이트 플래그를 제공합니다. 예약된 시간에 무인으로 실행되도록 설계되어 노이즈를 걸러내고 실행 가능한 인텔리전스를 제공합니다. 주간 수동 리서치 재생성 없이 경쟁사 자산을 최신 상태로 유지하는 데 활용하십시오.
빠른 설치
Claude Code
추천npx skills add deanpeters/Product-Manager-Skills -a claude-code/plugin add https://github.com/deanpeters/Product-Manager-Skillsgit clone https://github.com/deanpeters/Product-Manager-Skills.git ~/.claude/skills/competitive-intel-watchClaude Code에서 이 명령을 복사하여 붙여넣어 스킬을 설치하세요
문서
Competitive Intel Watch
Purpose
Monitor a competitive landscape for material shifts since the last run. Diff the world against the previous snapshot; report only what changed, with evidence; flag which downstream artifacts need updating. This is the skill that turns competitive research from a document into a cadence — the weekly SIGINT sweep and monthly OSINT digest from the fusion cadence live here. A watch reports change, not state: regenerating the same report weekly is theater, and "no material shifts this cycle" is a valid, useful result.
Input
Works best with: the previous Competitive Research Snapshot (pasted or attached) — the baseline this run diffs against — and the competitor list (defaults to those in the snapshot). Also useful: anything specific you're watching for this cycle, and a materiality bar adjustment if the default needs tightening or loosening.
Input supplied inline with the invocation — text after the skill name, a pasted context dump, or an
appended ARGUMENTS: line — counts as answers already given. Use it against the question budget;
don't re-ask.
Arriving empty-handed? That works too. With no prior snapshot, the skill falls back to baseline
mode: it produces a first snapshot using the competitive-research-snapshot structure and stops —
the delta value starts on run two.
Example invocation: Competitive intel watch — prior snapshot pasted below; this cycle I'm specifically watching for pricing moves. [snapshot]
Key Concepts
- Governing protocol: honors the
autonomous-investigationcontract — question budget of 2 (this skill's tightest), search-plan gate, Fact/Inference/Assumption labels, Just Enough Mode, stable schema, 4-option Final Step. - Discipline mix: SIGINT first (site diffs, pricing pages, job posts — the freshest layer), with
OSINT and HUMINT signals monthly and FININT on the quarterly pass — the fusion cadence in
intelligence-collection-disciplinesis this skill's operating rhythm. - The materiality bar. Report a change only if a sales rep, pricing owner, or roadmap owner would plausibly act on it: pricing/packaging changes, launches and deprecations, positioning shifts, leadership moves, funding or M&A, major customer wins/losses, credible roadmap signals. Below the bar: cosmetic site changes, routine content marketing, minor releases. Why it matters: a watch that cries wolf gets ignored by cycle three — the bar is what keeps the audience.
- Delta discipline. Read the prior snapshot fully before searching; diff against it, never regenerate it. The empty changelog is a first-class outcome.
- Update flags close the loop. Research is only done when it names the artifact it changes — each material shift maps to the battle card, positioning, pricing, or roadmap sections now stale.
- Do-not-invent list: competitors, features, pricing, market share, customer wins, roadmap items, product claims. Every claimed change carries a URL and a date.
- When NOT to use: no baseline exists and you want the full treatment → run
competitive-research-snapshotfirst; the scope itself changed (new segment, pivot) → re-snapshot from scratch rather than diffing a stale scope.
Application
- Determine mode. Prior snapshot provided → delta mode. None → baseline mode: produce a
snapshot per the
competitive-research-snapshotschema and stop. - Credit inline context, then ask only the unanswered questions (max 2):
- Do you have the previous snapshot, or should I create a baseline?
- Anything specific you're watching for this cycle? If unanswered, proceed: baseline mode if no snapshot, default materiality bar otherwise.
- Read the prior snapshot fully before searching. The diff target is the document, not your memory of the market.
- Show the 3-bullet search plan — what you'll check per competitor, source types (company sites, pricing pages, release notes, press, investor materials, credible news, review sites, job postings), how facts will be separated from inference. Continue unless revised.
- Sweep and filter through the materiality bar. When nothing clears it, say so plainly.
- Emit the schema below exactly — runs must be diffable.
Output schema (do not reorder)
# Competitive Watch Report
## 1. Run Header
**Scope (from prior snapshot):** | **Prior snapshot date:** | **This run date:** | **Competitors checked:**
## 2. Changelog (Material Shifts Only)
For each material shift:
### [Competitor] — [4 to 8 word change summary]
- **What changed:** [1-2 bullets, labeled Fact/Inference]
- **Evidence:** [URL, date]
- **So what:** [why it clears the materiality bar]
- **Confidence:** [high / medium / low]
If nothing cleared the bar: "No material shifts this cycle." List
anything on the watchlist for next run.
## 3. Update Flags
| Downstream artifact | Sections needing update | Driven by |
|---|---|---|
| Battle card | | |
| Positioning statement | | |
| Pricing/packaging analysis | | |
| Roadmap assumptions | | |
Only rows with real updates; omit the rest.
## 4. Watchlist for Next Run
- [Signals below the bar but trending]
- [Open questions this run could not resolve]
### Assumptions to Validate
- [Assumption 1] / [Assumption 2] / [Assumption 3]
A copy/paste fill-in version of this schema, with quality checks, lives in template.md.
Final Step (offer exactly 4 options)
- Update the battle card sections flagged above (
battle-card-builder) - Deep-dive the most significant change
- Produce the refreshed full snapshot (new baseline)
- Adjust the materiality bar or competitor list for next run
Accept 1, 2, 3, 4, 1 and 2, Verbose Mode, or a custom path. On a scheduled, unattended
run, file the report and stop — the options wait for a human.
Examples
A changelog entry that clears the bar (fictional):
Ledgerline — mid-tier plan removed from pricing page
- What changed: the $49 "Team" tier no longer appears; feature list redistributed upward — Fact (pricing page vs. archived version, Jul 2 vs. Jun 1)
- Evidence: URL + archive diff, dated
- So what: entry price effectively doubled; our "cheaper to start" talking point is now stronger, and their SMB churn may spike — clears the bar for both sales and pricing owners
- Confidence: high
The empty changelog done right:
No material shifts this cycle. Below-the-bar activity logged for trend: [Competitor B] published three thought-leadership posts on compliance automation (watchlist: possible positioning shift if their product pages follow), and two senior-engineer job posts mention a language we haven't seen in their stack before (watchlist: TECHINT corroboration needed before this means anything).
See examples/sample.md for a complete worked run (fictional FSM-software
market) that diffs against the competitive-research-snapshot example's baseline — including an
assumption from that baseline getting confirmed by the diff. examples/sample-industrial.md
shows the quarterly-cadence industrial version, where a top risk gets demoted and that's
reported as material.
Common Pitfalls
- Regeneration theater. Producing a fresh full report each run and calling it a watch. The reader's question is "what changed?" — answer only that.
- Materiality inflation. Reporting blog posts and minor releases to seem productive. Every below-bar item reported costs credibility the real alerts will need later.
- Fear of the empty changelog. Padding a quiet cycle with noise. "No material change" backed by a real sweep is exactly what a healthy watch produces most cycles.
- Undated evidence. A change claim without both URL and date can't be verified or diffed next run. The date is half the evidence.
- Diffing a stale scope. The market pivoted, you entered a new segment — and the watch keeps diffing the old frame. Re-baseline when the scope changes; say so in the run header.
- Orphaned intelligence. A changelog with no update flags. If no artifact needs updating, the shift probably didn't clear the bar — flags are how research becomes action.
References
autonomous-investigation(Workflow) — the governing protocolintelligence-collection-disciplines(Component) — the fusion cadence this watch runs oncompetitive-research-snapshot(Workflow) — produces the baseline this skill diffs againstbattle-card-builder(Workflow) — consumes the update flagspestel-analysis(Component) — the macro-environment sibling of this competitor-level watch- Adapted from
market-intelligence/competitive-intel-watch-prompt.mdin thehttps://github.com/deanpeters/product-manager-promptsrepo.
GitHub 저장소
자주 묻는 질문
competitive-intel-watch Skill이란 무엇인가요?
competitive-intel-watch은(는) deanpeters이(가) 만든 Claude Skill입니다. Skill은 Claude가 필요할 때 불러오는 지침과 리소스를 묶어 추가 프롬프트 없이 competitive-intel-watch 관련 작업을 수행할 수 있게 합니다.
competitive-intel-watch은(는) 어떻게 설치하나요?
이 페이지의 설치 명령을 사용하세요. competitive-intel-watch을(를) Claude Code 플러그인으로 추가하거나 저장소를 skills 디렉터리에 복제한 다음 Claude를 다시 시작해 Skill을 불러옵니다.
competitive-intel-watch은(는) 어떤 카테고리에 속하나요?
competitive-intel-watch은(는) 기타 카테고리에 속합니다.
competitive-intel-watch은(는) 무료로 사용할 수 있나요?
네. competitive-intel-watch은(는) AIMCP에 등록되어 있으며 무료로 설치할 수 있습니다.
연관 스킬
LlamaGuard는 폭력 및 혐오 발언 등 6가지 안전 범주에서 LLM 입력과 출력을 조정하기 위한 Meta의 70-80억 파라미터 모델입니다. 94-95% 정확도를 제공하며 vLLM, Hugging Face 또는 Amazon SageMaker를 사용해 배포할 수 있습니다. 이 기술을 사용하여 AI 애플리케이션에 콘텐츠 필터링 및 안전 가드레일을 손쉽게 통합하세요.
이 Claude Skill은 리소스 적정화, 태깅 전략, 지출 분석을 통해 개발자들이 클라우드 비용을 최적화할 수 있도록 지원합니다. AWS, Azure, GCP에서 클라우드 비용을 절감하고 비용 거버넌스를 구현하기 위한 프레임워크를 제공합니다. 인프라 비용을 분석하거나, 리소스를 적정화하거나, 예산 제약을 충족해야 할 때 사용하세요.
이 Claude Skill은 스프레드, 오버/언더, 프로프 베트를 포함한 스포츠 베팅 시장을 분석합니다. 역사적 추이와 상황별 통계를 검토하여 가치 베트를 발견하고, 교육적 목적으로 실행 가능한 권장 사항이 담긴 구조화된 마크다운 결과를 제공합니다. 개발자는 이 기능을 스포츠 베팅 분석 도구에 활용할 수 있으며, 단순히 엔터테인먼트/교육 목적으로만 설계되었음을 유의해야 합니다.
이 스킬은 bitsandbytes를 사용하여 LLM을 8비트 또는 4비트 정밀도로 양자화하며, 최소한의 정확도 손실로 50-75%의 메모리 감소를 달성합니다. 제한된 GPU 메모리에서 더 큰 모델을 실행하거나 추론을 가속화하는 데 이상적이며, INT8, NF4, FP4와 같은 형식을 지원합니다. 이 스킬은 HuggingFace Transformers와 통합되어 QLoRA 학습 및 8비트 옵티마이저를 가능하게 합니다.
