All skills
deanpeters avatar

/autonomous-investigation

@eb7a51e

The protocol behind every investigation skill. Use when AI research must proceed without you: search-plan gate, Fact/Inference/Assumption labels, confidence stacking, diffable outputs.

Use this Skill: https://skilld.dev/gh/deanpeters/product-manager-skills/autonomous-investigation

This session only. Nothing lands on disk.

examplesprotocol-in-action-industrial.md

≈1.3k tokens on demand. Your agent reads this file only when SKILL.md points to it.

Autonomous Investigation Protocol — In Action (Industrial)

All companies, products, URLs, and figures are fictional. Industrial sibling of protocol-in-action.md. The SaaS run showed the gate revision and the gaps list; this one shows two clauses the SaaS run couldn't stress as hard: confidence stacking across physical-world channels (customs, registries, permits) and the conflict case — when two disciplines disagree and the protocol says dig instead of average.


1. Invocation, budget, and one surgical question

User: Investigate whether Meridian Freight Systems is entering plant-floor automation — this supports how much of Northfield's 2027 roadmap we fence off for a defensive play.

Target and decision arrived inline. The skill spends one question:

Agent: One question: does "entering" mean shipping a product, or building the capability? The evidence bars differ — capability shows up in filings and hiring years before a product shows up in a catalog.

User: Capability. If it's real, 2027 planning has to know.

2. The search-plan gate

Agent: Search plan (say "revise" to change it):

  • Sweep certification registries, customs/trade data, and facility/permit records for commitment-side signals (money and atoms, not words)
  • Cross-check with hiring patterns, API/docs changes, and their public solutions pages
  • Facts get URLs and dates; interpretations get labeled Inference; unknowables go to the gaps list, not the estimate mill

Continue unless revised — the user lets it run.

3. Findings — a stack, and a conflict

Key findings:

  • Certification filing "in process" for a plant-floor equipment class, via a Meridian subsidiary — Fact (registry entry, Oct 2026)
  • Component import volumes rising two consecutive quarters — Fact (trade records)
  • "Manufacturing early access" signup on their solutions page — Fact (page diff, Nov 2026)
  • API docs already model generic asset classes — Fact (docs)

The stack (4 channels agree): registry (MASINT-adjacent) + customs (MASINT) + web staging (SIGINT) + architecture (TECHINT) → capability build is real — actionable intelligence, not a watch item.

The conflict: HUMINT disagrees. Their careers page shows no manufacturing-domain hires — no controls engineers, no plant-floor sales, no channel roles — Fact (job boards, checked Nov 2026). Four channels say "building"; the people channel says "not staffing it." Per the protocol: channels in conflict means someone is bluffing — dig, don't average.

The dig (one more pass, scoped to the conflict): the certification filing lists a partner engineering firm as the responsible party — Fact (registry detail page). Resolution hypothesis: they're building through partners, not headcount — which changes the defensive read entirely: a partner-led entry can be slowed by winning the partners — Inference, confidence: medium.

Gaps (couldn't find, not guessed):

  • No way to see the partner agreement's exclusivity terms from public record
  • Import volumes can't be allocated between warehouse (their core) and plant-floor (the bet)

Reading for your decision: capability build confirmed, product 12-36 months out (certification clock), and the entry vector is partner-led. Fence off a partner-program response in 2027, not a product-defense sprint — Inference.

Final Step — reply 1, 2, 3, 4, a combination, or "Verbose Mode":

  1. Map their likely partner-firm candidates (the integrator roll-ups first)
  2. Set tripwires: registry progress, distributor listings, domain hiring
  3. Trace the partner-led scenario into 2027 roadmap assumptions
  4. Brief leadership with the stack + conflict resolution as-is

Why this example works

  • The conflict case is the star. Four agreeing channels would have shipped a tidy "they're coming" brief; the disagreeing fifth channel forced a dig that changed the strategic response (win the partners vs. race the product). Averaging the signals — "80% confident" — would have buried the most decision-relevant fact in the whole run.
  • Commitment-side evidence led the plan. Registries, customs, permits — money and atoms. The announcement layer (their marketing site) appears only as staging corroboration, exactly the "ambition is OSINT; commitment shows up elsewhere" corollary.
  • The gaps list stayed honest under pressure — import allocation and partner terms are unknowable from outside, and the report says so instead of estimating, even though both numbers would have made the brief more satisfying.
  • Read the two siblings together: SaaS run = gate revision + graceful unattended rerun; industrial run = stacking + conflict resolution. Between them, all seven clauses appear under load in two different physics.

Source: SKILL.md on GitHub

1 warning2mo3 checks · Risk SAFE
  • Gen Agent Trust Hub2mo

    The skill establishes a structured protocol for autonomous research, focusing on evidence labeling (Fact, Inference, Assumption), search planning, and consistent output formatting. It includes explicit ethical guardrails and contains no executable code or malicious patterns.

  • Socket2mo

    No alerts

  • Snyk2mo

    Risk: MEDIUM · 1 issue

Signed by skilld at eb7a51e. 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 3 months ago
type
workflow
theme
market-intelligence
estimated_time
protocol reference; investigations vary (15-45 min per run)
Other metadata
intent
Provide the canonical contract for autonomous research skills: a bounded question budget, a search-plan gate, three-level evidence labeling, do-not-invent lists, just-enough output, stable diffable schemas, and confidence stacking — so investigations are trustworthy, schedulable, and comparable run over run.
best_for
[
  "Defining consistent behavior for research skills that run as agent tasks or on schedules",
  "Keeping AI research honest: labeled evidence, real citations, no invented facts",
  "Making run N and run N+1 diffable so delta monitoring is possible"
]
scenarios
[
  "Set up a competitive scan that can re-run quarterly without me babysitting it",
  "I want research output where I can tell facts from the AI's guesses"
]

README badge

README badge for deanpeters/product-manager-skills/autonomous-investigation