autonomous-investigation

Defines a seven-clause protocol for autonomous research with labeled evidence and diffable outputs.

6.8k|818|Updated Feb 5, 2026
One-click install
npx skills add https://github.com/deanpeters/Product-Manager-Skills --skill autonomous-investigation
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: autonomous-investigation
Source: https://github.com/deanpeters/Product-Manager-Skills/tree/main/skills/autonomous-investigation
Command: npx skills add https://github.com/deanpeters/Product-Manager-Skills --skill autonomous-investigation

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

AI-driven research often produces unverifiable claims, invented citations, and bloated reports that cannot be trusted or compared over time. This Skill provides a canonical contract for autonomous investigations so research can run unattended, stay honest, and produce outputs that diff cleanly against previous runs.

Core Features & Use Cases

  • Seven-Clause Contract: Enforces a question budget, search-plan gate, Fact/Inference/Assumption evidence labels, do-not-invent lists, Just Enough output mode, stable schemas, and a 4-option Final Step block.
  • Confidence Stacking: Grades multi-channel findings from watch item (1 channel) to actionable intelligence (3+ channels), with a dig rule when channels conflict.
  • Schedulable Investigations: Designed so research skills can run as agent tasks or on schedules, degrading gracefully with labeled assumptions when no human answers.
  • Use Case: Set up a quarterly competitive scan that re-runs without supervision, where every claim is labeled, gaps are listed honestly, and the team reads the delta between runs instead of full reports.

Quick Start

Run an autonomous investigation on a competitor's move into a new market, stating the decision the research should support.

Frequently Asked Questions about autonomous-investigation

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I make AI research run autonomously without supervision?

Use a protocol with a hard question budget (usually 3), a search-plan gate that auto-continues, and labeled assumptions for unanswered gaps. This lets investigations run as scheduled agent tasks that degrade gracefully instead of stalling when nobody responds.

How to stop AI from inventing facts in competitive research?

Require every key claim to carry a Fact, Inference, or Assumption label, with real checkable URLs for facts. Define a domain-specific do-not-invent list naming fabrication risks like pricing, market share, and customer wins, and put unfound items in an explicit gaps list.

What is confidence stacking in competitive intelligence?

Confidence stacking grades findings by independent channel agreement: one channel is a watch item, two channels form a working hypothesis, and three or more channels make actionable intelligence. Conflicting channels trigger a dig rather than averaging, since conflict often signals deception.

Can AI research outputs be compared across quarterly runs?

Yes, if the investigation uses a stable output schema where section order and structure never drift between runs. Diffable schemas enable delta monitoring, so teams read what changed since last quarter instead of re-reading full reports.

What are the ethical limits of autonomous competitive intelligence gathering?

Collection must be legal, open-source work: anything published, filed, posted, or publicly observable. It excludes pretexting, soliciting NDA-protected information, or scraping against accepted terms, following the SCIP Code of Ethics stage test.