compete

Researching competitors, analyzing differentiation, and shaping strategic positioning. Covers feature matrices, SWOT, benchmarking, positioning maps, battle cards, win/loss, and LLM brand visibility. Research only — no code. Use when scoping competitive landscape, building positioning artifacts, or assessing LLM brand visibility.

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Repository: seaworld008/Commonly-used-high-value-skills
Last source activity: July 27, 2026 at 07:13
Detected SKILL.md language: English
Stars: 64
Forks: 10

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name	compete
description	Researching competitors, analyzing differentiation, and shaping strategic positioning. Covers feature matrices, SWOT, benchmarking, positioning maps, battle cards, win/loss, and LLM brand visibility. Research only — no code. Use when scoping competitive landscape, building positioning artifacts, or assessing LLM brand visibility.
zh_description	竞品研究、差异化定位、矩阵对比和竞争战卡。
version	1.0.1
author	seaworld008
source	github:simota/agent-skills
source_url	https://github.com/simota/agent-skills/tree/main/compete
license	MIT
tags	["compete", "growth", "marketing"]
created_at	2026-07-27
updated_at	2026-07-27
quality	5
complexity	advanced

Compete

Strategic competitive analyst. Research only.

Trigger Guidance

Use Compete when the task needs:

competitor discovery, profiling, or tiering
feature, pricing, UX, SEO, or tech-stack comparison
SWOT, positioning, benchmarking, or differentiation strategy
competitive alert triage, battle cards, or response planning
win/loss analysis tied to product, sales, or market strategy
moat, category, PLG, pricing, or DX-based market interpretation
LLM brand visibility, AI share of voice, or GEO metrics analysis
deep OSINT: job posting signals, patent/IP tracking, SEC filing narrative analysis, GitHub/OSS intelligence
market sizing: TAM/SAM/SOM/PAM estimation and competitive market share
ecosystem mapping: platform dynamics, network effects, partnership landscape, adjacent market threats
competitive wargaming: red/blue team simulation, competitor response prediction, pre-mortem analysis

Route elsewhere when the task is primarily:

general product feature proposal (not competition-driven): Spark
business strategy simulation or scenario planning: Helm
market metrics and KPI tracking: Pulse
user feedback analysis without competitive context: Voice
visual diagram creation (not competitive analysis): Canvas
code implementation: Builder

Read only the references needed for the current analysis shape.

Core Contract

Always use WebSearch to collect the latest data before analysis. Never rely solely on training knowledge — real-time web research is mandatory for every task.
Cite sources for every claim. Every finding, data point, and comparison must include a source URL or attribution. Unsourced claims are not permitted in deliverables.
Produce intelligence, not monitoring. Monitoring shows what happened; intelligence explains why and what's coming next. Every deliverable must include forward-looking implications, not just current-state observations.
Treat CI as a continuous capability, not an event. One-off competitive reports decay within weeks. Embed CI as a standing process with regular collection cycles, living battle cards, and automated change detection.
Prefer customer value over competitor imitation.
Distinguish direct competitors, indirect competitors, and substitutes.

Phase	Required action	Key rule	Read
`MAP`	Define 5-10 Key Intelligence Questions (KIQs) — the questions whose answers would materially change competitive positioning. Run WebSearch for each competitor and market segment. Actively track `3-5` primary competitors (identified from CRM win/loss data); passively monitor `10-15` via automated alerts. Collect pricing pages, changelogs, press releases, and review sites	KIQs before collection; WebSearch first, then source list before analysis	`reference/intelligence-gathering.md`
`ANALYZE`	Extract patterns, gaps, threats, and substitutes	Evidence-backed findings	`reference/analysis-templates.md`
`DIFFERENTIATE`	Turn findings into strategic choices and downstream actions	Actionable, not exhaustive	`reference/playbooks.md`

Shape	Use when	Default reference
Landscape	Map players, segments, or category boundaries	`reference/intelligence-gathering.md`
Benchmark	Compare features, pricing, UX, performance, SEO, or stack	`reference/analysis-templates.md`
Response	React to competitor moves, build battle cards, or set alert actions	`reference/playbooks.md`
Win/Loss	Explain why deals were won or lost	`reference/modern-win-loss-analysis.md`
Strategy	Define moats, positioning, category moves, or pricing posture	`reference/competitive-moats-category-design.md`
Calibration	Validate predictions and tune source confidence	`reference/intelligence-calibration.md`
LLM Visibility	Analyze how AI models reference and recommend brands in the competitive set	`reference/intelligence-gathering.md`
Deep Dive	Extract strategic intent from structured public data (jobs, patents, SEC, GitHub, reviews)	`reference/deep-osint-signals.md`
Market Sizing	Estimate TAM/SAM/SOM/PAM with top-down and bottom-up cross-verification	`reference/market-sizing.md`
Ecosystem	Map platform ecosystems, network effects, partnerships, and adjacent market threats	`reference/ecosystem-mapping.md`
Wargame	Simulate competitor responses to strategic moves via red/blue team exercises	`reference/competitive-wargaming.md`

Recipe	Subcommand	Default?	When to Use	Read First
Competitor Matrix	`matrix`	✓	Competitor map, feature comparison matrix, tiering	`reference/analysis-templates.md`
SWOT Analysis	`swot`		SWOT, positioning, differentiation strategy	`reference/competitive-moats-category-design.md`
Positioning Map	`positioning`		Positioning map, category design, moat evaluation	`reference/competitive-moats-category-design.md`
LLM Visibility	`llm-visibility`		LLM brand presence, AI share of voice measurement	`reference/intelligence-gathering.md`
Battle Card	`battle`		One-pager sales enablement, objection-handling pairs, freshness governance, GTM distribution	`reference/battle-card.md`
Win/Loss Analysis	`winloss`		Post-decision interviews, segmentation, theme extraction, cadence design, CRM integration	`reference/winloss-analysis.md`
Moat (7 Powers)	`moat`		Helmer 7 Powers assessment, durability scoring, anti-moat detection	`reference/moat-7-powers.md`
Multi-Engine	`multi`		Tri-engine coverage (Codex + agy + Claude parallel) leveraging non-overlapping priors. Artifact-driven merge with `engine_concurrence` tags + mandatory "Uncommon Competitors (Verified-Divergent)" callout patching single-engine blind-spots.	`reference/tri-engine-compete.md`, `reference/multi-engine-mode.md`

Keyword cues	Shape
`competitor`, `landscape`, `market map`, `players`, unclear	Landscape
`feature comparison`, `pricing`, `benchmark`, `UX compare`	Benchmark
`SWOT`, `positioning`, `differentiation`, `moat`, `category`, `PLG`, `DX advantage`	Strategy
`battle card`, `alert`, `competitor move`, `response`	Response
`win/loss`, `deal analysis`, `lost deal`	Win/Loss
`calibrate`, `prediction`, `source confidence`	Calibration
`LLM visibility`, `AI share of voice`, `GEO metrics`, `AI brand monitoring`	LLM Visibility
`deep dive`, `OSINT`, `job postings`, `patents`, `SEC filings`, `hiring signals`	Deep Dive
`TAM`, `SAM`, `SOM`, `market size`, `addressable market`	Market Sizing
`ecosystem`, `platform`, `network effects`, `partnerships`, `integrations`, `adjacent market`	Ecosystem
`wargame`, `red team`, `blue team`, `competitor response`, `pre-mortem`, `what if we`	Wargame
`multi-engine`, `tri-engine`, `cross-engine compete`, `parallel competitor research`, `uncommon competitors`, `blind-spot competitors`	`multi` Recipe

Topic	Rule
Limited data	State gaps, lower confidence, avoid decisive strategic claims
Alert urgency	`High = immediate`, `Medium = weekly`, `Low = monthly`. `10%+` price cut = `High`
Prediction accuracy	`> 0.80 maintain`, `0.60-0.80 improve`, `< 0.60 review method`
Calibration	`3+` data points before reweighting; max `+/-0.15` per cycle; `10%` quarterly decay
Indirect competition	Include substitutes when the customer job can be solved without direct competitors
Response default	Prefer differentiation/value framing over feature-copy recommendations
Battle card freshness	Manual cycle `14-21 days`; AI-enabled `< 24h`. Weekly updates `→ +15%` win-rate vs monthly
Battlecard adoption	`< 40%` = quality problem; `60-70%` healthy; `> 80%` excellent
Win/loss program ROI	`15-30%` win-rate lift — establish formal program above `20` competitive deals/quarter
Pricing verification	Verify before every competitive deal — pages change without announcement
Competitive deal prevalence	~`68%` of deals are head-to-head — assume competitive context unless proven otherwise
GEO monitoring	Quarterly minimum per AI platform; citations vs mentions tracked separately; AI-referred traffic `+527%` YoY 2024-2025
Executive sponsorship	CI programs with sponsor show `76%` higher effectiveness — prerequisite for L2+ maturity

compete

Install options

Review the source files

Compete

Trigger Guidance

Core Contract

Compete

Trigger Guidance

Core Contract

Boundaries

Always

Ask First

Never

Workflow

Analysis Shapes

Recipes

Subcommand Dispatch

Output Routing

Multi-Engine Mode

SHARPEN Post-Analysis

Critical Decision Rules

Output Requirements

Collaboration

Routing And Handoffs

Reference Map

Operational

AUTORUN Support

Nexus Hub Mode

Usage Notes

Common Patterns

Boundaries

Direction	Token	Use when
`Voice -> Compete`	`VOICE_TO_COMPETE`	Customer feedback must be compared against competitors
`Pulse -> Compete`	`PULSE_TO_COMPETE`	Product or market metrics must be benchmarked
`Compete -> Spark`	`COMPETE_TO_SPARK`	Competitive gaps should become feature ideas
`Compete -> Growth`	`COMPETE_TO_GROWTH`	Positioning or SEO gaps need growth strategy
`Compete -> Canvas`	`COMPETE_TO_CANVAS`	Analysis needs visual maps or matrices
`Compete -> Helm`	`COMPETE_TO_HELM`	Strategic simulation or scenario planning is required
`Compete -> Lore`	`COMPETE_TO_LORE`	Validated recurring patterns should become shared knowledge
`Compete -> Oracle`	`COMPETE_TO_ORACLE`	LLM brand visibility analysis requires AI/ML domain expertise
`Compete -> Field`	`COMPETE_TO_RESEARCHER`	Interview design suggestions from win/loss analysis

Reference	Read when
`reference/intelligence-gathering.md`	Collecting public sources, price intel, reviews, stack data, SEO signals
`reference/analysis-templates.md`	Building competitor profiles, matrices, SWOTs, positioning maps, benchmarks
`reference/playbooks.md`	Producing battle cards, alert responses, structured competitive response plans
`reference/intelligence-calibration.md`	Validating predictions, adjusting source reliability, emitting `EVOLUTION_SIGNAL`
`reference/ci-anti-patterns-biases.md`	Analysis quality threatened by bias, copycat thinking, weak framing
`reference/ai-powered-ci-platforms.md`	CI maturity, tooling, automation, real-time monitoring strategy
`reference/modern-win-loss-analysis.md`	Analyzing why deals were won/lost, feeding back into strategy
`reference/competitive-moats-category-design.md`	Evaluating moats, category design, PLG, pricing posture, DX advantage
`reference/deep-osint-signals.md`	Extracting strategic intent from jobs, patents, SEC, GitHub, app reviews
`reference/market-sizing.md`	Estimating TAM/SAM/SOM/PAM, market share, adjacent market size
`reference/ecosystem-mapping.md`	Platform ecosystems, network effects, partnerships, adjacency threats
`reference/competitive-wargaming.md`	Simulating competitor responses, red/blue team, pre-mortem
`reference/battle-card.md`	Designing battle card, freshness governance, GTM distribution, win-rate lift
`reference/winloss-analysis.md`	Post-decision interviews, segmentation, theme coding, cadence, CRM integration
`reference/moat-7-powers.md`	Helmer 7 Powers scoring, durability, Counter-Positioning vs differentiation, anti-moats
`reference/brand-equity.md`	Measuring brand strength via Keller's CBBE pyramid (salience→resonance), brand-equity metrics, brand-as-moat diagnosis vs competitors
`reference/multi-engine-mode.md`	`multi` Recipe operational detail — engine-bias rationale, scoring semantics, degraded-mode matrix
`reference/tri-engine-compete.md`	`multi` algorithm, JSON schema, CLUSTER identity rules, per-artifact SYNTHESIZE patterns, subagent prompts
`reference/benchmarks-thresholds.md`	Full numeric thresholds — calibration, battlecard adoption, win-rate, GEO, seller-adoption baselines
`_common/SUBAGENT.md`	Base MULTI_ENGINE protocol — engine dispatch, loose prompts, Agent fan-out, fallbacks
`_common/MULTI_ENGINE_RECIPE.md`	Cross-skill `multi` protocol — Pattern D/C/H rationale, PREFLIGHT, FAN-OUT, attribution tags, degraded modes
`_common/OPUS_5_AUTHORING.md`	Report sizing, adaptive thinking depth at SHARPEN, INTAKE front-loading. Critical: P3, P5
`_common/GROWTH_BRAND_PROOF.md`	Market Proof `cannibalization_proof` (Phase 2-3) + `distinctiveness_proof` (Phase 1 B.hard, G12 Diversity Floor, competitor embedding distance). Quarterly G12 Distinctive Asset Audit; G14 Regulatory Horizon Scan
`reference/autorun-schema.md`	You are emitting the AUTORUN `_STEP_COMPLETE` block — Compete-specific Output/Next schema.

compete

Source facts

Install options

Review the source files

More from this repository

Compete

Trigger Guidance

Core Contract

Compete

Trigger Guidance

Core Contract

Boundaries

Always

Ask First

Never

Workflow

Analysis Shapes

Recipes

Subcommand Dispatch

Output Routing

Multi-Engine Mode

SHARPEN Post-Analysis

Critical Decision Rules

Output Requirements

Collaboration

Routing And Handoffs

Reference Map

Operational

AUTORUN Support

Nexus Hub Mode

Usage Notes

Common Patterns

Boundaries

More from this repository