validate

name	validate
description	Activate for: validate, build measure learn, BML, pivot, persevere, pilot results, what did we learn, experiment results, assumption test results, was I right, did it work, should I pivot, what should I change, learning synthesis, validated learning, invalidated assumption, pilot analysis, what our pilot taught us, early customer data, what customers told us, post-pilot analysis, pivot or continue, kill or continue. NOT for: assumption mapping (use hypothesis), idea generation (use idea), sprint planning (use sprint).
license	Apache-2.0
metadata	{"author":"Panaversity","version":"1.0","plugin-commands":"/validate"}

CONTEXT LOADING

Before executing, check for innov.local.md in the working directory. If found, extract:

• venture: name, stage, type, problem_statement
• key_assumptions: all entries with IDs, risk levels, evidence, test status
• sprint_log: previous sprint results and learnings
• customer_profiles: personas, pains
• financial_model: current_state (for impact assessment)

If innov.local.md is not found: Continue with conversation context. After first substantive output, prompt: "I'm working without your venture context. Run Exercise 8 from Chapter 40 to build innov.local.md -- it will make every subsequent output specific to your venture rather than generic."

STAGE-AWARE CALIBRATION

Check venture.stage and calibrate:

• IDEA: N/A -- validation requires something to validate. Consider running /discovery or /hypothesis first.
• DISCOVERY: Validation is appropriate for discovery-stage assumptions -- did the problem exist as hypothesised?
• VALIDATION: This is your focus stage. Full BML analysis and pivot decisions are the priority.
• MVP: This is your focus stage. Pilot results analysis and assumption updates are critical.
• GROWTH: Validation remains important for new features and expansion hypotheses.

DLA PROGRESSION CHECK

If no key_assumptions exist in innov.local.md or all are UNTESTED: "You are trying to validate without an assumption map. Validation requires knowing what you were testing and what success/failure looks like. Consider running /hypothesis first to build your assumption map."

BUILD-MEASURE-LEARN WORKFLOW

Task Types

TYPE 1: BUILD-MEASURE-LEARN ANALYSIS Input: What was tested; pilot results (metrics, adoption, customer feedback) Output: Validated/invalidated assumptions; unexpected learnings; pivot/persevere recommendation; V1 priorities

TYPE 2: PIVOT DECISION FRAMEWORK Input: Invalidated assumption(s); what is still true Output: 5 pivot directions; pivot recommendation with rationale

TYPE 3: LEARNING SYNTHESIS Input: Raw pilot data; customer interviews; usage metrics; NPS/feedback Output: Pattern map; assumption updates; open questions; next sprint priority

TYPE 4: ASSUMPTION STATUS UPDATE Input: New data from any source (pilot; interview; market research) Output: Specific assumption updates for innov.local.md

BML Analysis Output Structure

BUILD-MEASURE-LEARN ANALYSIS
Sprint/Pilot: [N] | Period: [Start]-[End] | Date: [Date]
================================================================
WHAT WE TESTED:
  Learning goal: [Assumption(s) targeted]
  Method:        [How we tested -- pilot / survey / interview / experiment]
  Sample:        [N customers / N users / N transactions]

WHAT WE MEASURED:
  [Metric 1]: [Result] vs. [Success criterion] -- [PASS / FAIL / PARTIAL]
  [Metric 2]: [Result] vs. [Success criterion] -- [PASS / FAIL / PARTIAL]
  [Metric 3]: [Result] vs. [Success criterion] -- [PASS / FAIL / PARTIAL]

ASSUMPTION OUTCOMES:
  A-00X ([Assumption]): VALIDATED / INVALIDATED / INCONCLUSIVE
  Evidence: [Specific -- "3 of 3 pilots signed at $X" not "customers liked it"]
  Confidence: [HIGH / MEDIUM / LOW -- based on sample size and data quality]

  [Repeat for each assumption that was tested or affected]

UNEXPECTED LEARNINGS:
  [Things you discovered that you were not looking for]
  [New assumptions revealed by the pilot]
  [Customer behaviour that surprised you]
  Implication: [What each unexpected learning means for direction]

PIVOT OR PERSEVERE RECOMMENDATION:
  [PERSEVERE / PIVOT ON SPECIFIC ELEMENT / FULL PIVOT]
  Rationale: [Why -- based on the evidence, not on attachment to the idea]
  If PERSEVERE: [What is the next most critical assumption to test?]
  If PIVOT: [On what specifically -- see pivot framework below]

innov.local.md UPDATES PROPOSED:
  [Specific changes to assumption status, canvas blocks, personas, financials]
================================================================

Pivot Types (from Lean Startup methodology)

ZOOM-IN PIVOT: One feature becomes the whole product. ZOOM-OUT PIVOT: The whole product becomes one feature of a larger product. CUSTOMER SEGMENT PIVOT: Same product; different customer. CUSTOMER NEED PIVOT: Same customer; different problem. PLATFORM PIVOT: Application becomes a platform (or vice versa). BUSINESS ARCHITECTURE PIVOT: High-margin/low-volume to low-margin/high-volume. TECHNOLOGY PIVOT: Same positioning; different technology. CHANNEL PIVOT: Same product; different distribution channel.

Evidence Quality Standard

VALIDATED means: customers paid for it OR used it N times per week for N weeks. Not: "They said they would use it" (interest != behaviour) Not: "They signed up for the waitlist" (intent != payment) Not: "They said it was great" (enthusiasm != value)

Evidence hierarchy (most to least reliable):

1. Customer paid AND renewed (revealed preference over time)
2. Customer paid once (revealed preference at a moment)
3. Customer signed a letter of intent with specific terms
4. Customer used the product N times without prompting
5. Customer said they would pay [specific amount] in an interview
6. Customer said the problem is real and painful
7. Multiple people described the same problem

Pivot Decision Checklist

Before recommending a pivot:

• Have you run at least 2 iterations of the current approach?
• Is the invalidation based on behaviour (what customers did) or opinions (what they said)?
• Is the team emotionally ready for a pivot?
• What is still true -- what learning is preserved into the pivot?

ASSUMPTION TRACKING

After any validation output:

• Update all affected assumption statuses with specific evidence
• Propose innov.local.md updates for assumptions, canvas, financials, personas
• Surface the next most critical untested assumption
• Always distinguish between ASSUMED, ANECDOTAL, and VALIDATED evidence

NEVER DO THESE

• NEVER call a "no results" inconclusive -- if you ran the test correctly and customers did not engage, that IS a result: treat it as INVALIDATED
• NEVER pivot based on one customer's feedback -- one customer is a data point; a pattern across 3+ customers is signal
• NEVER persevere past 3 iterations of the same invalidated assumption
• NEVER update innov.local.md assumption status to VALIDATED without specific evidence (N customers, $X paid, N% retention)

ALL OUTPUTS REQUIRE REVIEW BY A QUALIFIED PROFESSIONAL BEFORE USE IN BUSINESS DECISIONS.