intel-discipline-advisor
About
This skill helps developers triage market intelligence questions by interactively determining the appropriate research disciplines, cadence, and execution skills needed. It guides users through three adaptive questions to map decisions to specific intelligence channels like OSINT or TECHINT. Use it when you need to research a competitive question but are unsure which investigation methods to employ.
Quick Install
Claude Code
Recommendednpx skills add deanpeters/Product-Manager-Skills -a claude-code/plugin add https://github.com/deanpeters/Product-Manager-Skillsgit clone https://github.com/deanpeters/Product-Manager-Skills.git ~/.claude/skills/intel-discipline-advisorCopy and paste this command in Claude Code to install this skill
Documentation
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-facilitationas 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:
- Suspected competitor move — you think someone is building, entering, or repositioning
- An artifact to build or refresh — TAM/SAM/SOM, battle card, positioning, ICP/personas, pricing analysis
- A margin or market-structure puzzle — margins eroding, category shifting, entry decision
- 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):
| Situation | Discipline mix | Executing skill |
|---|---|---|
| Suspected move | Corroborate the seen signal: TECHINT + HUMINT + SIGINT + FININT, fused | intelligence-collection-disciplines fusion template, then competitive-intel-watch |
| Battle card | SIGINT + OSINT + HUMINT | battle-card-builder, kept fresh by the watch |
| TAM/SAM/SOM | GEOINT/DEMOINT + FININT capture rates | tam-sam-som-calculator Mode 3 |
| Positioning / ICP | OSINT + GEOINT/DEMOINT (+ VoC) | voice-of-customer-miner → positioning-statement |
| Industry structure | FININT + OSINT | porters-five-forces |
| One company's position | OSINT + FININT + HUMINT | swot-analysis or company-intel |
| Pricing question | SIGINT + FININT | pricing-packaging-tracker |
| Macro exposure | GEOINT/DEMOINT + regulatory FININT | pestel-delta-monitor |
| Standing watch | SIGINT weekly, OSINT+HUMINT monthly, FININT+TECHINT quarterly — scaled to stated capacity | competitive-intel-watch |
| The full picture | All of the above, sequenced | competitive-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:
- 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.
- 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.
- 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.
- Set the watch instead — if this can't get hours this quarter, wire [Competitor A] into
competitive-intel-watchmonthly 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
intelligence-collection-disciplines(Component) — the compendium this advisor routes into; the pedagogic pairworkshop-facilitation(Interactive) — facilitation protocolautonomous-investigation(Workflow) — confidence stacking and evidence labels the recommendations lean on- Executing skills:
competitive-intel-watch,battle-card-builder,tam-sam-som-calculator,porters-five-forces,swot-analysis,voice-of-customer-miner,pricing-packaging-tracker,pestel-delta-monitor,competitive-analysis-process discovery-interview-prep(Interactive) — the off-ramp when the question belongs to discovery- Companion to Dean Peters' "Competitive Research on Steroids" compendium (Productside).
GitHub Repository
Frequently asked questions
What is the intel-discipline-advisor skill?
intel-discipline-advisor is a Claude Skill by deanpeters. Skills package instructions and resources that Claude loads on demand, so Claude can perform intel-discipline-advisor-related tasks without extra prompting.
How do I install intel-discipline-advisor?
Use the install commands on this page: add intel-discipline-advisor to Claude Code as a plugin, or clone its repository into your skills directory, then restart Claude so it picks up the skill.
What category does intel-discipline-advisor belong to?
intel-discipline-advisor is in the Meta category, tagged general.
Is intel-discipline-advisor free to use?
Yes. intel-discipline-advisor is listed on AIMCP and free to install.
Related Skills
This skill provides a production-tested setup for Content Collections, a TypeScript-first tool that transforms Markdown/MDX files into type-safe data collections with Zod validation. Use it when building blogs, documentation sites, or content-heavy Vite + React applications to ensure type safety and automatic content validation. It covers everything from Vite plugin configuration and MDX compilation to deployment optimization and schema validation.
This skill enables developers to build applications with the Polymarket prediction markets platform, including API integration for trading and market data. It also provides real-time data streaming via WebSocket to monitor live trades and market activity. Use it for implementing trading strategies or creating tools that process live market updates.
This skill helps developers create OpenCode plugins that hook into 25+ event types like commands, files, and LSP operations. It provides the plugin structure, event API specifications, and implementation patterns for JavaScript/TypeScript modules. Use it when you need to intercept, monitor, or extend the OpenCode AI assistant's lifecycle with custom event-driven logic.
SGLang is a high-performance LLM serving framework that specializes in fast, structured generation for JSON, regex, and agentic workflows using its RadixAttention prefix caching. It delivers significantly faster inference, especially for tasks with repeated prefixes, making it ideal for complex, structured outputs and multi-turn conversations. Choose SGLang over alternatives like vLLM when you need constrained decoding or are building applications with extensive prefix sharing.
