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Competitive Intel Watch

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deanpeters
competitive-intel-watch

Scheduled delta monitoring against a prior competitive snapshot. Use when tracking competitors on a cadence: material shifts only, cited evidence, battle-card update flags, runs unattended.

Overview

Publisherdeanpeters
RepositoryProduct-Manager-Skills
Skill namecompetitive-intel-watch
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 Intel Watch 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-intel-watch .claude/skills/competitive-intel-watch
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Competitive Intel Watch 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 Intel Watch 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 Intel Watch 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 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-investigation contract — 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-disciplines is 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-snapshot first; the scope itself changed (new segment, pivot) → re-snapshot from scratch rather than diffing a stale scope.

Application

  1. Determine mode. Prior snapshot provided → delta mode. None → baseline mode: produce a snapshot per the competitive-research-snapshot schema and stop.
  2. Credit inline context, then ask only the unanswered questions (max 2):
    1. Do you have the previous snapshot, or should I create a baseline?
    2. Anything specific you're watching for this cycle? If unanswered, proceed: baseline mode if no snapshot, default materiality bar otherwise.
  3. Read the prior snapshot fully before searching. The diff target is the document, not your memory of the market.
  4. 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.
  5. Sweep and filter through the materiality bar. When nothing clears it, say so plainly.
  6. Emit the schema below exactly — runs must be diffable.

Output schema (do not reorder)

markdown
# 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)

  1. Update the battle card sections flagged above (battle-card-builder)
  2. Deep-dive the most significant change
  3. Produce the refreshed full snapshot (new baseline)
  4. 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

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 Intel Watch AI skill do?

Scheduled delta monitoring against a prior competitive snapshot. Use when tracking competitors on a cadence: material shifts only, cited evidence, battle-card update flags, runs unattended.

Why use Competitive Intel Watch on TypingMind?

Because you install it once and use it with any model. Competitive Intel Watch 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 Intel Watch in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/deanpeters/Product-Manager-Skills/tree/main/skills/competitive-intel-watch. 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 Intel Watch?

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 Intel Watch?

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

Is the Competitive Intel Watch 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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