Prompts · Shell · v0.1.0
Map evidence-backed growth options across the Ansoff Matrix with risk-rated sequencing. Use when the question is where the next tranche of growth comes from, and at what risk.
#ai-agents#ai-product-management#claude-skills
/Catalogue/Prompt/deanpeters/deanpeters-product-manager-skills-competitive-research-snapshot
Origin: githubResearch a competitive landscape with cited snapshots, a comparison matrix, and so-what implications. Use when a product decision needs competitive grounding, not a market report.
by deanpeters · updated 23d ago · imported from GitHub
Research a company's competitive landscape using a workflow, not a one-shot answer: search plan → competitor selection → just-enough research → fact/inference labels → real URL citations → next-step options. The output is a decision-support snapshot, not a market report — and because its schema is stable, downstream skills (battle cards, delta monitors) can consume it and diff it. Because it proceeds on labeled assumptions when questions go unanswered, it can run as an agent task or on a schedule; re-run it and diff against the prior snapshot.
Works best with: the company, product, or segment to research, and the decision this research
should support (positioning, roadmap bet, deal support, board prep) — the decision determines what
"just enough" means.
Also useful: known competitors (or explicit permission to identify them), and any prior snapshot
or market-landscape-scan output in session — the skill builds on evidence already gathered rather
than re-researching it.
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. The skill opens with at most 3 questions (subject, decision, competitors) and proceeds on labeled assumptions if they go unanswered.
Example invocation: Competitive research snapshot on our expense-automation product — decision: which roadmap bet wins Q1. Competitors: [Competitor A], [Competitor B]; find a third if one matters.
autonomous-investigation
contract in full — question budget of 3, search-plan gate, Fact/Inference/Assumption labels, Just
Enough Mode, stable schema, 4-option Final Step.intelligence-collection-disciplines.competitive-intel-watch
diffs the world against this document. Section order never changes.tam-sam-som-calculator;
you need deep intel on one company's strategy and executives →
company-research / company-intel;
the facts are already gathered → go straight to the battle card.# Competitive Research Snapshot
## 1. Scope
**Company/product:** | **Category:** | **Decision supported:** | **Competitors analyzed:**
## 2. Competitor Snapshots
For each competitor, max 5 bullets:
### Competitor: [Name]
- **Positioning:**
- **Relevant capability:**
- **Likely strength:**
- **Likely weakness:**
- **Key source URL:**
## 3. Quick Comparison
| Dimension | Company | Comp 1 | Comp 2 | Comp 3 |
|---|---|---|---|---|
| Target customer | | | | |
| Core use case | | | | |
| Main strength | | | | |
| Main weakness | | | | |
| Evidence quality | | | | |
## 4. So What?
- **3** product strategy implications
- **2** competitive risks
- **2** product opportunities
- **3** assumptions to validate
Each bullet: label, confidence, source URL where relevant.
A copy/paste fill-in version of this schema, with quality checks, lives in template.md.
battle-card-builder)Accept 1, 2, 3, 4, 1 and 2, Verbose Mode, or a custom path.
A competitor snapshot with honest labels (fictional):
Competitor: Ledgerline
- Positioning: "finance automation for mid-market CFOs" — Fact (homepage, Jul 2026)
- Relevant capability: approval-chain builder shipped in May — Fact (release notes)
- Likely strength: ERP integrations; 40+ listed, reviewers confirm the top 5 work well — Fact (G2 reviews)
- Likely weakness: implementation time; complaint cluster across 11 reviews since March — Inference (review mining; no benchmark data)
- Key source URL: pricing page
The "Evidence quality" row doing its job: the comparison matrix rates Comp 3's column low — every claim traces to their own marketing. The So What section then refuses to list Comp 3 as a primary risk: "insufficient independent evidence — Assumption to validate via customer references." That row exists so weak columns can't masquerade as strong ones.
See examples/sample.md for a complete worked snapshot (fictional
FSM-software market) that consumes the market-landscape-scan example and becomes the baseline the
competitive-intel-watch example diffs against. examples/sample-industrial.md
shows the same schema on an industrial evidence diet — filings, registries, and honest
absence-of-evidence.
competitive-intel-watch is for.autonomous-investigation (Workflow) — the governing protocolintelligence-collection-disciplines (Component) — discipline sources and signal chainsmarket-landscape-scan (Workflow) — upstream: surfaces which players deserve this snapshotcompetitive-intel-watch (Workflow) — downstream: diffs future runs against this baselinebattle-card-builder (Workflow) — downstream: turns the snapshot into a field-action cardcompany-research, company-intel — single-company deep divesmarket-intelligence/competitive-research-snapshot-prompt.md in the
https://github.com/deanpeters/product-manager-prompts repo.No comments yet — start the thread.
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Prompts · Shell · v0.1.0
Map evidence-backed growth options across the Ansoff Matrix with risk-rated sequencing. Use when the question is where the next tranche of growth comes from, and at what risk.
#ai-agents#ai-product-management#claude-skills
Prompts · Shell · v0.1.0
Build a phase-gated EOL checklist sized to the sunset, with a named owner on every item. Use when the decision to retire is made and you need the operational plan.
#ai-agents#ai-product-management#claude-skills
Prompts · Shell · v0.1.0
Guide teams through Lean UX Canvas v2. Use when framing a business problem, surfacing assumptions, and defining what to learn next.
#ai-agents#ai-product-management#claude-skills
Prompts · Shell · v0.1.0
Design multi-agent AI workflows with clear boundaries, handoffs, and monitoring. Use when a complex PM task should run as parallel specialized agents instead of one linear process.
#ai-agents#ai-product-management#claude-skills