stg-extracting-insights

name	stg-extracting-insights
description	Processes governor-provided expert sources into structured, tier-labeled insights that feed the hypothesis register as evidence. Use when governor provides a book, article, video, or podcast for processing.
serves	strategist
domain	knowledge-extraction
affects	all-hypotheses
depends-on	none
produces	tier-labeled evidence items for register integration

Insight Extraction

Process governor-provided expert sources into structured, tier-labeled insights. Every extracted claim carries a tier label and maps to a specific hypothesis. Behavioral vs hypothetical evidence distinction maintained throughout.

Procedure

Step 1: Ingest Source [S]

Read: source URL, file, or transcript (governor-provided).

• If URL: WebFetch to retrieve content.
• If file: Read file content.
• If transcript: Read transcript.

Identify structure (chapters, sections, speakers). Capture metadata: date, author, platform, publication context.

Produce: raw content + metadata.

Gate: source_ingested: bool -- content retrieved, metadata captured (date, author).

• Pass: Step 2.
• Fail: If URL returns error, report to governor. If content is gated/paywalled, report: "Cannot access -- governor must provide content directly."

Step 2: Extract Claims and Frameworks [K-grounded]

Grounded in: raw content from Step 1.

Identify and extract:

Category	What to Look For	Example
Frameworks	Mental models, decision structures	"The 4 properties of a good problem"
Principles	Universal rules, guidelines	"Never price below 10x the cost of the alternative"
Tactics	Specific actions, playbooks	"Use 5-second tests to validate landing page messaging"
Data points	Benchmarks, metrics, statistics	"Average PLG conversion rate is 3-5%"
Warnings	Anti-patterns, failure modes	"Teams that skip problem validation fail 3x more often"

For each: state the claim, cite the specific quote or passage (with location/timestamp if applicable), note the context.

Produce: extracted claims list.

Gate: claims_extracted: bool -- at least 3 claims extracted, each with specific citation from source.

• Pass: Step 3.
• Fail: If source is too thin for 3 claims, extract what exists and note: "Source produced limited actionable claims."

Step 3: Tier-Label Each Claim [R]

For each claim, apply the operational test: "What new data would I need to see to change my mind about this?"

Answer	Tier	Example
"None -- this is definitional or from cited data"	T1	"SaaS gross margins are typically 70-85%" (from benchmark report)
"Validate the reasoning chain"	T2	"Teams with this problem spend 40% of time on workarounds" (expert estimate)
"Customer/market data I don't have"	T3	"Customers will pay 10x for this solution" (expert opinion about WTP)

Also distinguish:

• Behavioral evidence: "We observed X happen" -- stronger
• Hypothetical evidence: "X should work because Y" -- weaker

Produce: tier-labeled claims.

Gate: claims_labeled: bool -- every claim has tier label and behavioral/hypothetical classification.

• Pass: Step 4.
• Fail: Claims without clear tier are likely T2 or T3. Default to T2 for reasoning, T3 for predictions.

Step 4: Map to Hypotheses [R]

For each claim, determine which hypothesis it supports or challenges:

Mapping Target	Evidence About
Problem hypothesis	Pain existence, frequency, severity, alternatives
Segment hypothesis	Who has the problem, observable characteristics
Unit Economics hypothesis	Pricing, costs, conversion rates, benchmarks
Solution Design	Features, growth architecture, MVP approach, positioning, differentiation, jobs-to-be-done
General	Domain knowledge, not hypothesis-specific

Explicitly search for claims that contradict current register. Challenging evidence is more valuable than confirming evidence.

Produce: hypothesis-mapped claims.

Gate: claims_mapped: bool -- every claim mapped to at least one hypothesis or marked "General."

• Pass: Step 5.
• Fail: If all claims map to "General," the source may not be relevant to current strategy work. Report to governor.

Step 5: Write Outputs [S]

For hypothesis-specific insights: format as EvidenceItems:

• [{TYPE: WEB_RESEARCH or OBSERVATION}] [{T1|T2|T3}] {date} -- {source}: {detail}
• Note which hypothesis they should be added to

For general insights: write to knowledge/ as standalone file if source is broadly relevant.

The agent integrates evidence items into the register on the next BUILD/CHALLENGE pass.

Produce: evidence items for register integration + optional knowledge file.

Gate: outputs_written: bool -- evidence items formatted for register integration, each with hypothesis target.

• Pass: Done.
• Fail: Ensure every output has the required format fields: type, tier, date, source, detail.

Quality Criteria

• Every extracted claim cites specific passage from source (not paraphrased without reference)
• Tier labels applied to every claim with justification
• Behavioral vs hypothetical distinction maintained (quotes about what happened vs what should happen)
• Claims mapped to specific hypotheses (not all dumped as "general")
• Source metadata complete (date, author, URL if applicable)

Failure Modes

Mode	Signal	Recovery
Uncritical extraction	All claims extracted as T1 or all labeled as important	Apply tier test rigorously. Expert opinion about what customers want is still T3. Expert frameworks are T2. Only cited data is T1
Confirmation bias in extraction	Only insights supporting existing hypotheses extracted; challenging insights omitted	Explicitly search for claims that contradict current register. Challenging evidence is more valuable than confirming evidence
Quote without context	Extracted quote is stripped of qualifying language that changes its meaning	Include surrounding context. "This works" may have been preceded by "In enterprise markets above $50K ACV" -- the qualifier matters

Boundaries

In scope: Source ingestion (URL, file, transcript), claim extraction (frameworks, principles, tactics, data points, warnings), tier labeling, behavioral/hypothetical classification, hypothesis mapping, evidence item formatting.

Out of scope: Autonomous web research (strategist does this directly), competitive analysis (stg-analyzing-competition), market sizing (stg-sizing-markets), hypothesis construction (skills handle their own domains).