Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add varunk130/ai-customer-discovery-skills --skill assumption-mappergit clone --depth 1 https://github.com/varunk130/ai-customer-discovery-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/varunk130/ai-customer-discovery-skills/assumption-mapper)<a href="https://agentmods.dev/skills/varunk130/ai-customer-discovery-skills/assumption-mapper"><img src="https://agentmods.dev/badge/skills/varunk130/ai-customer-discovery-skills/assumption-mapper/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/varunk130/ai-customer-discovery-skills/assumption-mapper"><img src="https://agentmods.dev/badge/skills/varunk130/ai-customer-discovery-skills/assumption-mapper.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00068 | $0.01115 |
| Opus 5 | $0.00034 | $0.00558 |
| Sonnet 5 | $0.00014 | $0.00223 |
| Haiku 4.5 | $0.00007 | $0.00112 |
Grade A, and why
assumption-mapper scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 11d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 84 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Assumption Mapper
Every product bet rests on a stack of assumptions. This skill makes them explicit, classifies each as Known / Believed / Hoped, and produces a ranked test plan so the team validates the riskiest assumptions before committing to build.
Core Principle
The assumptions you don't write down are the ones that kill the product. Hidden assumptions become "obvious in hindsight" only because no one stress-tested them. The mapper's job is to drag them into the open and force a verdict on each.
The 3-Tier Classification
| Tier | Definition | Action |
|---|---|---|
| Known | Direct evidence, multiple independent sources, recent data | No test needed; document the evidence link |
| Believed | Reasonable inference from indirect or partial evidence | Test if assumption is critical; defer if not |
| Hoped | Wishful thinking dressed as reasoning; no real evidence | Test before any meaningful investment |
Cross with criticality (Critical / High / Medium / Low) → the Critical × Hoped quadrant is the team's actual risk surface.
Assumption Categories
The mapper walks through 5 categories to surface assumptions a one-shot brainstorm misses:
| Category | Example Assumptions |
|---|---|
| Customer / Demand | "SMB ops leaders feel this pain weekly"; "buyers will pay $X" |
| Solution / Product | "Our approach is materially better than the workaround"; "the model can hit 90% accuracy on this task" |
| Channel / Distribution | "We can acquire this segment via content"; "our sales team can sell this without retraining" |
| Economic / Unit | "CAC stays under $X at this segment"; "support cost scales sublinearly" |
| Competitive / Time | "Incumbents won't ship this within 6 months"; "the platform shift continues for 2+ years" |
Output
Save to outputs/assumption-map-[initiative]-[YYYY-MM-DD].md
- Assumption Inventory - every assumption surfaced, by category
- Classification Matrix - Known / Believed / Hoped × Critical / High / Medium / Low
- Risk Surface - the Critical × Hoped quadrant, called out explicitly
- Test Plan - for each Critical × (Hoped or Believed) assumption: smallest test that would invalidate it, expected duration, success criteria
- Defer List - Low-criticality assumptions that don't justify a test (named so they're not silently treated as Known)
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 11d ago First seen · 84 lines · 68 tokens per session scan A 8a747e84ad3d
assumption-mapper is a skill published in the GitHub repository varunk130/ai-customer-discovery-skills (2 stars, last pushed 1mo ago), licensed MIT. It adds 68 tokens to every session and 1,115 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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profile-builder
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insight-synthesis
Turns whatever evidence the BA managed to gather into structured insights — weighted by source quality so the team doesn't treat one SME's hunch as equal to five user interviews.
user-interviews
Direct user interviews. Run only when useraccess allows it.
journey-mapping
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proposal
Agency-to-client proposal document. The output the client reads, signs (or doesn't), and budgets against.