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-gtm-skill-library --skill whitespace-findergit clone --depth 1 https://github.com/varunk130/ai-gtm-skill-libraryWrote 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-gtm-skill-library/whitespace-finder)<a href="https://agentmods.dev/skills/varunk130/ai-gtm-skill-library/whitespace-finder"><img src="https://agentmods.dev/badge/skills/varunk130/ai-gtm-skill-library/whitespace-finder/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-gtm-skill-library/whitespace-finder"><img src="https://agentmods.dev/badge/skills/varunk130/ai-gtm-skill-library/whitespace-finder.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 | $0.00051 | $0.01117 |
| Opus 5.5 | $0.00020 | $0.00447 |
| Sonnet 5.5 | $0.00010 | $0.00223 |
| Haiku 4.5 | $0.00005 | $0.00112 |
Grade A, and why
whitespace-finder 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 1mo 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 — 97 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Whitespace Finder (DEPTH Model)
Map the gap between what the market demands and what exists. Produces quantified, validated opportunity scores for product and GTM decisions.
When to Use
- New product ideation
- Feature prioritization against market need
- Adjacent market exploration
- Investment thesis validation
- Pre-PRD opportunity validation
What You'll Need
Critical inputs (ask if not provided):
- Market or product category to analyze
- Target customer segment(s)
- Known competitors (or ask me to research)
Nice-to-have:
- Signal Radar output (if previously run)
- JTBD Extractor output (if previously run)
- Customer feedback or support ticket themes
Process
Step 1: Demand Evidence Collection
Audit demand across 6 evidence channels:
| Channel | What to Look For | Evidence Quality |
|---|---|---|
| Community forums (Reddit, HN, Discourse) | Problem-statement posts, workaround discussions | Medium -- shows real pain |
| Review mining (G2, Capterra, TrustRadius) | 1-3 star review complaint patterns, missing feature mentions | High -- verified buyers |
| Search demand | Volume for problem queries vs. solution queries (gap = unmet need) | High -- quantifiable |
| Support tickets | Recurring themes, feature requests, workaround patterns | High -- your own customers |
| Analyst reports | Problem statements, unmet need callouts, market gaps cited | High -- expert validation |
| Adjacent product requests | Features users ask for that cross product boundaries | Medium -- shows expansion opportunities |
For each channel, extract the top 5 unmet needs with supporting evidence.
Step 2: Gap Matrix Construction
Build a 2D matrix:
- X-axis: Customer needs/jobs (from research or JTBD Extractor)
- Y-axis: Existing solutions in market (products, workarounds, manual processes)
Rate each cell:
| Rating | Meaning |
|---|---|
| 0 | Completely unaddressed -- no solution exists |
| 1 | Poorly addressed -- solutions exist but are inadequate |
| 2 | Adequately addressed -- good-enough solutions exist |
| 3 | Well addressed -- strong solutions, hard to differentiate |
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.
- 1mo ago First seen · 97 lines · 51 tokens per session scan A c5be415c1520
whitespace-finder is a skill published in the GitHub repository varunk130/ai-gtm-skill-library (7 stars, last pushed 10d ago), licensed MIT. It adds 51 tokens to every session and 1,117 once invoked, about $0.0002 per session on Opus 5.5. 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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