Research 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.
npx skills add https://github.com/deanpeters/Product-Manager-Skills --skill competitive-research-snapshot
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-investigationcontract in full — question budget of 3, search-plan gate, Fact/Inference/Assumption labels, Just
Enough Mode, stable schema, 4-option Final Step.
+ SIGINT (pricing pages, site changes) — see
intelligence-collection-disciplines.
decision named in Scope. Research value is decision support, not page count.
competitive-intel-watchdiffs the world against this document. Section order never changes.
who don't. Use provided competitors; if none, identify the top 3 (4 only if clearly needed), each
with name, why relevant, source URL, and confidence.
share, customer wins, roadmap items, product claims.
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.
If unanswered, proceed with labeled assumptions.
from inference. Continue unless revised.
name; why relevant; source URL; confidence.
customer stories, press releases, investor materials, credible news, analyst/review sites.
~~~markdown
Company/product: | Category: | Decision supported: | Competitors analyzed:
For each competitor, max 5 bullets:
| Dimension | Company | Comp 1 | Comp 2 | Comp 3 |
|---|---|---|---|---|
| Target customer | | | | |
| Core use case | | | | |
| Main strength | | | | |
| Main weakness | | | | |
| Evidence quality | | | | |
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.
serve the decision in Scope, it's padding — Just Enough Mode is the contract.
a Fact about *what they claim*; whether it works is review-site territory or an Inference, labeled.
deep — the comparison matrix should be full, not wide.
each So What bullet is what makes it usable in a roadmap argument.
is theater — that's what 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 thehttps://github.com/deanpeters/product-manager-prompts repo.
Assists in writing high-quality content by conducting research, adding citations, improving hooks, iterating on outlines, and providing real-time feedback on each section. Transforms your writing process from solo effort to collaborative partnership.
Identifies high-quality leads for your product or service by analyzing your business, searching for target companies, and providing actionable contact strategies. Perfect for sales, business development, and marketing professionals.
Use this skill to query your Google NotebookLM notebooks directly from Claude Code for source-grounded, citation-backed answers from Gemini. Browser automation, library management, persistent auth. Drastically reduced hallucinations through document-only responses.
Efficient database search tool for bioRxiv preprint server. Use this skill when searching for life sciences preprints by keywords, authors, date ranges, or categories, retrieving paper metadata, downloading PDFs, or conducting literature reviews.
Query and analyze scholarly literature using the OpenAlex database. This skill should be used when searching for academic papers, analyzing research trends, finding works by authors or institutions, tracking citations, discovering open access publications, or conducting bibliometric analysis across 240M+ scholarly works. Use for literature searches, research output analysis, citation analysis, and academic database queries.
Access USPTO APIs for patent/trademark searches, examination history (PEDS), assignments, citations, office actions, TSDR, for IP analysis and prior art searches.
Multiagent AI system for scientific research assistance that automates research workflows from data analysis to publication. This skill should be used when generating research ideas from datasets, developing research methodologies, executing computational experiments, performing literature searches, or generating publication-ready papers in LaTeX format. Supports end-to-end research pipelines with customizable agent orchestration.
Automated LLM-driven hypothesis generation and testing on tabular datasets. Use when you want to systematically explore hypotheses about patterns in empirical data (e.g., deception detection, content analysis). Combines literature insights with data-driven hypothesis testing. For manual hypothesis formulation use hypothesis-generation; for creative ideation use scientific-brainstorming.
Take deanpeters/competitive-research-snapshot from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.