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Intel Discipline Advisor

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deanpeters
intel-discipline-advisor

Triage a competitive or market question into the right intelligence disciplines, cadence, and executing skill. Use when you know something needs researching but not which channel to run.

Overview

Publisherdeanpeters
RepositoryProduct-Manager-Skills
Skill nameintel-discipline-advisor
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 Intel Discipline Advisor 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/intel-discipline-advisor .claude/skills/intel-discipline-advisor
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Intel Discipline Advisor 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 Intel Discipline Advisor 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 Intel Discipline Advisor 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.

Intel Discipline Advisor

Purpose

Triage a competitive or market question into the right intelligence response: which of the eight collection disciplines to run, on what cadence, feeding which artifact, executed by which skill. The intelligence-collection-disciplines compendium holds everything about every channel; this advisor answers the question a busy PM actually has — "given what's on my desk, which two or three channels matter, and where do my limited hours go?" Running every discipline on every question is the failure mode; scoping to the decision is the craft. The advisor teaches the mapping as it routes, so by your third session you won't need it. That's the goal.

Input

Works best with: the decision or question on your desk, in your words — "I think [Competitor A] is building something," "my TAM slide got shredded," "sales keeps getting surprised." Also useful: any signal you've already noticed (a job posting, a pricing change, an earnings remark), your time budget, and whether you're limited to free sources.

Anything supplied with the invocation itself — text after the skill name, a pasted context dump, or an appended ARGUMENTS: line — counts as answers already given. Use it and skip whatever it covers; don't re-ask.

Arriving empty-handed? That works too. The advisor opens by asking what's on your desk, with enumerated situations to pick from.

Example invocation: Intel discipline advisor: two of their senior engineers just followed our CTO on a preprint server, and their careers page doubled — what do I run?

Key Concepts

  • Facilitation protocol: use workshop-facilitation as the default interaction protocol (entry modes, one question per turn, progress labels, numbered recommendations). This file defines the domain logic.
  • The routing brain is the artifact-mapping table in intelligence-collection-disciplines: every PM artifact has a known discipline mix and refresh cadence. The advisor's job is matching the user's situation to a row — and showing the match, because the mapping is the lesson.
  • Signals already seen are a head start. If the user noticed a job posting surge, HUMINT has already flagged once — the recommendation starts from "1 discipline flagged" on the confidence stacking ladder and names which independent channels would corroborate (see autonomous-investigation).
  • Cadence must match evidence speed and human capacity. Pricing pages change monthly; statistics releases change annually. A watch the user can't sustain is worse than none — it produces false confidence that someone is watching.
  • The honest off-ramp. Some questions don't need an investigation: if the question is "why do customers churn," the answer is win/loss interviews and discovery, not a patent sweep. The advisor says so.

Application

This interactive skill asks 3 adaptive questions, then offers numbered, context-aware recommendations.

Question 1: What's on your desk?

"What's the situation? Pick the closest, or describe your own:

  1. Suspected competitor move — you think someone is building, entering, or repositioning
  2. An artifact to build or refresh — TAM/SAM/SOM, battle card, positioning, ICP/personas, pricing analysis
  3. A margin or market-structure puzzle — margins eroding, category shifting, entry decision
  4. Standing watch setup — you want ongoing coverage, not a one-off answer"

Question 2: Adaptive follow-up

  • If 1 (suspected move): "What tipped you off — a job posting, a pricing change, an exec's post, a patent, a customer remark? (Whatever you saw is one discipline already flagging; we'll pick the independent channels that could corroborate it.)"
  • If 2 (artifact): "Which artifact, and does a prior version exist to diff against?"
  • If 3 (structure puzzle): "Is the question about the whole industry's structure, or one company's position in it?"
  • If 4 (watch): "Honestly, how much recurring time can you or your team commit — 30 minutes a week, a half-day a month, a day a quarter?"

Question 3: Constraints

"Two quick constraints: free sources only or is paid tooling available, and any geographic focus? (Region changes which registries and statistics bureaus apply.)"

Then: Recommend

Synthesize and offer 3-5 numbered recommendations, each naming: discipline mix → cadence → executing skill → artifact fed, with one line on when it's the right choice. Always show why the disciplines were chosen (the mapping-table logic), and include the off-ramp when it's honest. Handle single selection, combinations ("1 and 3"), and custom directions per the facilitation protocol.

Routing quick-reference (from the artifact-mapping table):

SituationDiscipline mixExecuting skill
Suspected moveCorroborate the seen signal: TECHINT + HUMINT + SIGINT + FININT, fusedintelligence-collection-disciplines fusion template, then competitive-intel-watch
Battle cardSIGINT + OSINT + HUMINTbattle-card-builder, kept fresh by the watch
TAM/SAM/SOMGEOINT/DEMOINT + FININT capture ratestam-sam-som-calculator Mode 3
Positioning / ICPOSINT + GEOINT/DEMOINT (+ VoC)voice-of-customer-minerpositioning-statement
Industry structureFININT + OSINTporters-five-forces
One company's positionOSINT + FININT + HUMINTswot-analysis or company-intel
Pricing questionSIGINT + FININTpricing-packaging-tracker
Macro exposureGEOINT/DEMOINT + regulatory FININTpestel-delta-monitor
Standing watchSIGINT weekly, OSINT+HUMINT monthly, FININT+TECHINT quarterly — scaled to stated capacitycompetitive-intel-watch
The full pictureAll of the above, sequencedcompetitive-analysis-process

Close by offering the one-page triage card (template.md) filled in from the session — the route, its "because," and a review date, as a handoff artifact.

Examples

Sample flow (fictional):

Q1 → User: "1 — I think [Competitor A] is building a data-platform play." Q2 → User: "Their careers page doubled, mostly data engineers." Q3 → User: "Free sources, US only."

"Your hiring observation means HUMINT has already flagged once — one discipline is an anecdote, so the job is corroboration from independent channels. Recommendations:

  1. TECHINT sweep this week (patents.google.com + their GitHub org + arXiv affiliations, free) — a paper-or-patent cluster in the same specialty as the hiring surge is the strongest fusion pair available. Pick this first: highest corroboration value per hour.
  2. SIGINT check, 30 minutes (crt.sh for new subdomains, Wayback diff on their product pages) — catches launch staging if this is further along than hiring suggests.
  3. FININT pass at their next earnings call — listen for capex dodges and segment-language shifts; free via their IR page. Slower, but turns hypothesis into briefable intelligence.
  4. Set the watch instead — if this can't get hours this quarter, wire [Competitor A] into competitive-intel-watch monthly and let the cadence catch it.

Reply 1, 2, 3, 4, a combination like '1 and 2', or tell me more. (Two agreeing disciplines = working hypothesis; three = brief your leadership.)"

The off-ramp in action: user picks "artifact: ICP refresh," but Q2 reveals the real question is "why did our last three enterprise deals stall?" The honest recommendation leads with win/loss interviews (HUMINT's ground-truth layer) and discovery-interview-prep — "no public-web sweep answers a question your own churned prospects can answer better."

See examples/conversation-flow.md for a full end-to-end session: inline input crediting two of the three questions, a capacity answer that gets believed, a combination selection, and a "tell me more" that earns a teaching answer. examples/conversation-flow-industrial.md shows the routing shift for a physical-world signal — permits and customs data instead of site diffs.

Common Pitfalls

  • Prescribing the full eight. Recommending every discipline is refusing to triage. Two or three channels matched to the decision beats coverage theater — the mapping table exists so you can skip.
  • Ignoring the seen signal. The user's tip-off is a free head start on the stacking ladder. Recommending channels that re-detect the same signal type adds no corroboration; independence is what stacks.
  • Cadence fantasy. Designing a weekly watch for a team with a quarterly attention span. Ask the capacity question and believe the answer.
  • Routing without teaching. Handing over a recommendation without the "because" strips the lesson. Every recommendation shows its mapping-table logic — the user should leave better at triage, not just triaged.
  • No off-ramp. Forcing every question into an investigation. Discovery, win/loss, and support tickets answer some questions better than any public-web sweep; say so when it's true.

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 Intel Discipline Advisor AI skill do?

Triage a competitive or market question into the right intelligence disciplines, cadence, and executing skill. Use when you know something needs researching but not which channel to run.

Why use Intel Discipline Advisor on TypingMind?

Because you install it once and use it with any model. Intel Discipline Advisor 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 Intel Discipline Advisor in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/deanpeters/Product-Manager-Skills/tree/main/skills/intel-discipline-advisor. 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 Intel Discipline Advisor?

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 Intel Discipline Advisor?

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

Is the Intel Discipline Advisor 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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