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/intelligence-collection-disciplines

@ba3fc8c

Run competitive research like an intelligence agency: eight collection disciplines (OSINT to MASINT), signal-to-inference chains, and fusion. Use when one-source research isn't enough.

Use this Skill: https://skilld.dev/gh/deanpeters/product-manager-skills/intelligence-collection-disciplines

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examplessample.md

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Intelligence Collection Disciplines — Worked Example

All companies, products, URLs, and figures are fictional. This example shows a complete collection plan and its fusion outcome — the operation the compendium teaches. It's the same fictional scenario as the suite's other examples (Fieldlight, a trades-scheduling tool expanding into FSM), and it shows the collection side of the fused conclusion that appears in the competitive-analysis-process example's Step 6.


Collection Plan: DispatchCrow / suspected dispatch-depth build

Instantiation

[TARGET] = DispatchCrow [MARKET] = field service management software; NAICS 513210 (software publishers) [GEOGRAPHY] = United States, Canada [BUYER] = owner or office manager, trades shops of 10-100 technicians [CAPABILITY] = building real dispatch/capacity-planning depth (moving upmarket into our target segment) [DECISION] = how much of our two-quarter quoting window is real — sets the FSM expansion pace

Discipline Selection

Discipline Running? Why (which signal chains) Key sources (free first)
OSINT ✅ review complaints clustering on their dispatch board = their roadmap pressure point review sites, trades forums
FININT ◻ not yet private company; Crunchbase free tier only — thin until they raise again Crunchbase
GEOINT/DEMOINT ◻ no terrain already mapped in the June TAM pass; refresh annually —
TECHINT ✅ changelog + API docs diffs → beta capabilities before announcement their changelog, API docs, app store notes
HUMINT ✅ hiring surge/specialty chain → building a capability, not a feature LinkedIn jobs, their careers page, blog
SIGINT ✅ pricing-gate moves + new subdomains → monetization strategy and launch staging pricing page snapshots, Wayback, crt.sh
MASINT ◻ no software target; ops-capacity signals folded into HUMINT job-post reading —

Four disciplines, chosen because they feed [DECISION]; the table's empty rows are decisions too.

Cadence

  • Weekly (SIGINT, ~30 min): pricing page diff; crt.sh check for new *.dispatchcrow.com certs
  • Monthly (OSINT + HUMINT): review mining for dispatch-board complaints; careers-page sweep
  • Quarterly (TECHINT deep pass): API docs diff; app store release-note review
  • Event-driven (48h): funding announcement (re-opens FININT); any "capacity" language shipping anywhere

Fusion Table (as of 2026-07-31)

Discipline Signal found Label Source URL + date
HUMINT head of dispatch experience + senior PM hired, both ex-incumbent Fact blog post, Jul 15
SIGINT payments gate moved down a tier — take-rate becoming the model Fact pricing archive diff, Jul 31
TECHINT "capacity view — coming to Pro" appears on pricing page feature list Fact pricing page, Jul 31
OSINT upmarket-switcher reviews: "fine until we hit 40 techs, then the board falls over" Fact (that they say it) review threads, Jun-Jul

Fusion verdict: 4 disciplines, one story → actionable intelligence → they are building dispatch depth to move upmarket, funded by payments volume. Recommended response: compress the quoting timeline — plan against a Q1 2027 capacity-planning launch, not "someday"; brief the roadmap owner this week.

One deliberate down-grade: the OSINT complaint cluster alone was a watch item back in June — it says customers want depth, not that DispatchCrow is building it. It joined the story only when HUMINT and TECHINT arrived. That's the stacking rule preventing a June overreaction.


Why this example works

  • The empty rows are the discipline. Three disciplines were consciously not run, with reasons — the artifact-mapping table's whole point. Eight checkmarks would mean nobody chose.
  • [DECISION] shaped everything. "How real is our two-quarter window?" selected the four disciplines that read their velocity, and set cadences to how fast each channel actually changes (pricing weekly, patents never — they're a five-person startup).
  • The stacking rule ran in both directions. Four agreeing channels escalated to actionable; but the example also shows the June signal correctly held at watch-item until independent channels corroborated. Escalation discipline is the rule's other half.
  • Announcements-as-intent never triggered — none of the four signals is a press release. The hires, the gate move, and the pricing-page feature list are all commitment-side evidence, which is why the verdict earns "actionable" with no announcement at all.

Source: SKILL.md on GitHub

1 warning1mo3 checks · Risk SAFE
  • Gen Agent Trust Hub1mo

    The skill provides a framework for competitive intelligence research. While the methodology is sound, it inherently involves processing data from untrusted external sources, presenting a low risk for indirect prompt injection.

  • Socket1mo

    No alerts

  • Snyk1mo

    Risk: MEDIUM · 1 issue

Signed by skilld at ba3fc8c. This ties the file your Agent reads to that commit on GitHub. It does not review the instructions.

Last checked against GitHub last month.

Activeupdated 2 months ago
type
component
theme
market-intelligence
estimated_time
reference skill; a single-discipline pass takes 30-60 min
Other metadata
intent
Give product managers the intelligence community's collection playbook: eight independent disciplines, each with free and paid sources, signal-to-inference chains, and the PM artifact it feeds — then fuse them with confidence stacking so three weak signals become one strong conclusion.
best_for
[
  "Choosing which collection channels to run for a competitive or market question",
  "Turning raw public signals (patents, job posts, filings, web diffs) into defensible inferences",
  "Cross-validating a suspected competitor move across independent evidence channels"
]
scenarios
[
  "I think a competitor is building a platform play — how do I confirm it before their launch?",
  "My TAM slide got shredded — where do I find data that survives scrutiny?"
]

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