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Competitive Research Snapshot

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deanpeters
competitive-research-snapshot

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.

Overview

Publisherdeanpeters
RepositoryProduct-Manager-Skills
Skill namecompetitive-research-snapshot
Stars
7K
Forks
831
Bundled files
3
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 3 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by deanpeters on GitHub. Read the source before you install it.

Installation

Install the Competitive Research Snapshot AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/deanpeters/Product-Manager-Skills.git /tmp/Product-Manager-Skills
mkdir -p .claude/skills
cp -r /tmp/Product-Manager-Skills/skills/competitive-research-snapshot .claude/skills/competitive-research-snapshot
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Competitive Research Snapshot in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Competitive Research Snapshot on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Competitive Research Snapshot is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Competitive Research Snapshot

Purpose

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.

Input

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.

Key Concepts

  • Governing protocol: honors the 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.
  • Discipline mix: OSINT (press, reviews, analyst coverage) + FININT (filings, earnings language)
  • Decision-support snapshot, not market report. Every section earns its place by serving the decision named in Scope. Research value is decision support, not page count.
  • Snapshot as baseline. The schema is a contract with future runs: competitive-intel-watch diffs the world against this document. Section order never changes.
  • Three or four competitors, not eight. Depth on the players who matter beats coverage of players 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.
  • Do-not-invent list (this domain's fabrication risks): competitors, features, pricing, market share, customer wins, roadmap items, product claims.
  • When NOT to use: you need market sizingtam-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.

Application

  1. Credit inline context, then ask only the unanswered questions (max 3):
    1. What company/product/segment?
    2. What decision should this support?
    3. Known competitors, or should I identify them? If unanswered, proceed with labeled assumptions.
  2. Show the 3-bullet search plan — what you'll search, source types, how facts will be separated from inference. Continue unless revised.
  3. Select competitors — provided ones, or top 3 identified (4 only if clearly needed). For each: name; why relevant; source URL; confidence.
  4. Research in Just Enough Mode with mixed sources: company sites, product/pricing pages, customer stories, press releases, investor materials, credible news, analyst/review sites.
  5. Emit the schema below exactly — it is a stable schema for diffing runs over time.

Output schema (do not reorder)

markdown
# 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.

Final Step (offer exactly 4 options)

  1. Competitive battle card (battle-card-builder)
  2. Executive comparison matrix
  3. Product risks/opportunities for the next 2 quarters
  4. Discovery questions to validate the assumptions

Accept 1, 2, 3, 4, 1 and 2, Verbose Mode, or a custom path.

Examples

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.

Common Pitfalls

  • The market-report trap. Twenty pages on industry trends nobody asked for. If a section doesn't serve the decision in Scope, it's padding — Just Enough Mode is the contract.
  • Marketing-page credulity. A competitor's claimed capabilities recorded as facts. Their site is a Fact about what they claim; whether it works is review-site territory or an Inference, labeled.
  • Coverage over depth. Eight competitors with two facts each. Select three that matter and go deep — the comparison matrix should be full, not wide.
  • Unlabeled So What. Implications stated with borrowed confidence. The label and confidence on each So What bullet is what makes it usable in a roadmap argument.
  • Regenerating instead of diffing. Re-running this skill weekly and re-reading the whole output is theater — that's what competitive-intel-watch is for.

References

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Competitive Research Snapshot AI skill do?

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.

Why use Competitive Research Snapshot on TypingMind?

Because you install it once and use it with any model. Competitive Research Snapshot is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Competitive Research Snapshot in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/deanpeters/Product-Manager-Skills/tree/main/skills/competitive-research-snapshot. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Competitive Research Snapshot?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Competitive Research Snapshot?

As many as you like. As long as a model supports skills, you can use Competitive Research Snapshot with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Competitive Research Snapshot AI skill free?

It is published on GitHub by deanpeters. Check the repository for licensing terms. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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