intel-discipline-advisor

--- name: intel-discipline-advisor argument-hint: "[the decision or competitive question on your desk]" description: "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." intent: >- Interactive triage for market intelligence: three adaptive questions about the decision on your desk, then numbered recommendations naming the discipline mix (OSINT, FININT, TECHINT...), the cadence, and the investigation skill that executes — teaching the decision-to-discipline mapping through use, so you eventually stop needing the advisor. type: interactive theme: market-intelligence best_for: - "Picking the two or three collection disciplines a specific decision actually needs" - "Setting up a watch cadence that matches how fast the evidence really changes" - "Learning the decision-to-discipline mapping instead of running everything on everything" scenarios: - "I think a competitor is up to something but I don't know where to look first" - "I have four hours this quarter for competitive intel — where do they go?" estimated_time: "5-10 min" --- # 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`](../intelligence-collection-disciplines/SKILL.md) 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`](../workshop-facilitation/SKILL.md) 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`](../autonomous-investigation/SKILL.md)). - **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): | Situation | Discipline mix | Executing skill | |---|---|---| | Suspected move | Corroborate the seen signal: TECHINT + HUMINT + SIGINT + FININT, fused | [`intelligence-collection-disciplines`](../intelligence-collection-disciplines/SKILL.md) fusion template, then [`competitive-intel-watch`](../competitive-intel-watch/SKILL.md) | | Battle card | SIGINT + OSINT + HUMINT | [`battle-card-builder`](../battle-card-builder/SKILL.md), kept fresh by the watch | | TAM/SAM/SOM | GEOINT/DEMOINT + FININT capture rates | [`tam-sam-som-calculator`](../tam-sam-som-calculator/SKILL.md) Mode 3 | | Positioning / ICP | OSINT + GEOINT/DEMOINT (+ VoC) | [`voice-of-customer-miner`](../voice-of-customer-miner/SKILL.md) → [`positioning-statement`](../positioning-statement/SKILL.md) | | Industry structure | FININT + OSINT | [`porters-five-forces`](../porters-five-forces/SKILL.md) | | One company's position | OSINT + FININT + HUMINT | [`swot-analysis`](../swot-analysis/SKILL.md) or [`company-intel`](../company-intel/SKILL.md) | | Pricing question | SIGINT + FININT | [`pricing-packaging-tracker`](../pricing-packaging-tracker/SKILL.md) | | Macro exposure | GEOINT/DEMOINT + regulatory FININT | [`pestel-delta-monitor`](../pestel-delta-monitor/SKILL.md) | | Standing watch | SIGINT weekly, OSINT+HUMINT monthly, FININT+TECHINT quarterly — scaled to stated capacity | [`competitive-intel-watch`](../competitive-intel-watch/SKILL.md) | | The full picture | All of the above, sequenced | [`competitive-analysis-process`](../competitive-analysis-process/SKILL.md) | Close by offering the one-page **triage card** ([`template.md`](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`](../discovery-interview-prep/SKILL.md) — "no public-web sweep answers a question your own churned prospects can answer better." See [`examples/conversation-flow.md`](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`](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`](../intelligence-collection-disciplines/SKILL.md) (Component) — the compendium this advisor routes into; the pedagogic pair - [`workshop-facilitation`](../workshop-facilitation/SKILL.md) (Interactive) — facilitation protocol - [`autonomous-investigation`](../autonomous-investigation/SKILL.md) (Workflow) — confidence stacking and evidence labels the recommendations lean on - Executing skills: [`competitive-intel-watch`](../competitive-intel-watch/SKILL.md), [`battle-card-builder`](../battle-card-builder/SKILL.md), [`tam-sam-som-calculator`](../tam-sam-som-calculator/SKILL.md), [`porters-five-forces`](../porters-five-forces/SKILL.md), [`swot-analysis`](../swot-analysis/SKILL.md), [`voice-of-customer-miner`](../voice-of-customer-miner/SKILL.md), [`pricing-packaging-tracker`](../pricing-packaging-tracker/SKILL.md), [`pestel-delta-monitor`](../pestel-delta-monitor/SKILL.md), [`competitive-analysis-process`](../competitive-analysis-process/SKILL.md) - [`discovery-interview-prep`](../discovery-interview-prep/SKILL.md) (Interactive) — the off-ramp when the question belongs to discovery - Companion to Dean Peters' "Competitive Research on Steroids" compendium (Productside).

[{"step":"Define the research goal and context","action":"Start by clearly articulating the [TOPIC] you need to research and the [DECISION/STRATEGY] it will inform. Specify the [COMPANY/TEAM] and [TIMEFRAME] to ensure the output is actionable. For example, 'I need to research the competitive landscape for our upcoming product launch in Q3 to inform our go-to-market strategy.'","tip":"Avoid vague topics like 'market trends.' Instead, use specific questions like 'How are our top 3 competitors positioning their AI features in 2024?'"},{"step":"Select the primary discipline","action":"Use the prompt template to guide the AI in identifying the most relevant intelligence discipline(s). For instance, if the topic is 'customer churn rates in the SaaS industry,' the primary discipline might be Market Research, with secondary support from Customer Intelligence.","tip":"If unsure, let the AI suggest a mix of disciplines (e.g., 70% competitive intelligence, 30% financial analysis) and validate the split with your team."},{"step":"Define the cadence and tools","action":"Work with the AI to determine the optimal update frequency (e.g., weekly, monthly) and the tools required to operationalize the research. For example, if the discipline is 'financial analysis,' tools like Bloomberg Terminal, PitchBook, or SEC filings might be recommended.","tip":"Align the cadence with your team's decision-making cycles. For example, if your product team meets bi-weekly, aim for bi-weekly updates."},{"step":"Assign execution skills and next steps","action":"Break down the required skills (e.g., data scraping, stakeholder interviews) and assign them to team members. Use the AI's output to create a concrete action plan with deadlines.","tip":"Prioritize skills that are critical but missing on your team. For example, if no one has experience with SWOT analysis, use the AI's guidance to upskill or hire a consultant."},{"step":"Validate and iterate","action":"Review the AI's recommendations with stakeholders to ensure alignment with business goals. Adjust the disciplines, cadence, or tools as needed based on feedback.","tip":"Run a pilot for 2-4 weeks to test the approach, then refine based on early learnings."}]