Edit session — my cofounder

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CONTEXT
Current tool set: Ask (open-ended Q&A on corpus), Compare (top-N tactics for your circumstance), Campaign Planner (coming soon — full plan). User feels a tool is missing and proposed a "Validation" tool (aka bullshit meter / article analyzer): user pastes a pre-canned efficacy statement, a blog/article URL, or a raw claim (e.g. "SMS has a pp of 3.5pp"), and the tool describes where it's right, wrong, and the research around it. Core problem it targets: research skews toward low-salience elections, which inherently produce higher pp effects; those small-campaign/low-salience results get over-extrapolated into generally-accepted "knowledge."

ASSESSMENT OF THE VALIDATION TOOL
- Strategically the best-sequenced third tool — better than Campaign Planner. It's descriptive/epistemic, zero prescription, low blast radius. Fits the standing discipline of "don't drift into Plan" (Plan issues prescriptions we can't yet back with our own validated outcomes).
- It's also the most direct expression of the moat: the credential thesis is that Ask must know the LIMITS of the literature. This tool turns that credential into its own front door — the single feature that most separates PL from a GPT wrapper.
- Distinct job from Ask (not just a mode): Ask = "I have a question, what's the answer." This = reverse vector, "I found a claim in the wild, adjudicate it for me." URL ingestion meets real behavior (people forward each other decks/blog posts).

TWO SHARPENINGS
1. Kill the "bullshit meter" framing. Users ARE the practitioners we're auditioning for track-two relationships, and many are repeating these over-extrapolated numbers in their own fundraising decks. A tool that feels built to call them wrong reads as adversarial. Goal = they walk away feeling MORE competent, not corrected. Same mechanism as Ask.
2. Reframe from truth to transportability. The problem isn't a truth problem, it's a transportability problem. "SMS = 3.5pp" is usually TRUE in its own study; it breaks only when transported to a different context (higher salience, higher baseline turnout). So the tool's job = take (claim) + (user context: race type, salience, baseline turnout) and return: the effect it rests on, the context it came from, how much it likely shrinks/holds for you, and the uncertainty. Rename to Applicability / "Does-This-Travel." (Note: Applicability = the user's proposed Validation tool, renamed. NOT the evidence-gap map, which is separate.)

OTHER IDEAS WEIGHED AGAINST IT
- Test/holdout designer (highest ceiling): the "make it trivial to design a measurable program — universe split, randomized holdout, pre-registered analysis plan" idea. This basically IS the Analyst Institute partnership / the experiment-design + meta-analysis layer, and the only path to proprietary data vs. re-serving the public corpus. More valuable long-term but relationship-gated — not a v1. The free tools earn the right to build it.
- Evidence-gap / coverage map (sleeper, separate tool): systemic version of the same core problem. Instead of debunking one claim at a time, it visualizes where the evidence base is thin (which tactics, race types, salience levels have real RCT support vs. one small low-salience study extrapolated everywhere). Cheap off existing corpus, doubles as the funder pitch ("evidence base for local races is thin — here's proof, fund N tests"), and is a standing credibility asset rather than a one-off utility.

RECOMMENDED SEQUENCE
1. Build the Applicability tool (the user's Validation tool, reframed to transportability) as the third shipped tool — low-risk, and it IS the credential.
2. Prototype the evidence-gap map alongside it — shares the backend, carries the funder story.
3. Hold the test/holdout designer until the relationships are real.

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Third tool: Validation/Applicability tool + evidence-gap map