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 Infrasity-Labs/dev-gtm-claude-skills --skill empathy-mapgit clone --depth 1 https://github.com/Infrasity-Labs/dev-gtm-claude-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/infrasity-labs/dev-gtm-claude-skills/empathy-map)<a href="https://agentmods.dev/skills/infrasity-labs/dev-gtm-claude-skills/empathy-map"><img src="https://agentmods.dev/badge/skills/infrasity-labs/dev-gtm-claude-skills/empathy-map/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/infrasity-labs/dev-gtm-claude-skills/empathy-map"><img src="https://agentmods.dev/badge/skills/infrasity-labs/dev-gtm-claude-skills/empathy-map.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 | $0.00049 | $0.00442 |
| Opus 5 | $0.00024 | $0.00221 |
| Sonnet 5 | $0.00010 | $0.00088 |
| Haiku 4.5 | $0.00005 | $0.00044 |
Grade A, and why
empathy-map 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 8d 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- empathy-map — 100% identical, 0 lines differ
What it actually says
Empathy Map
Build an empathy map to synthesize user research and align the team around user understanding.
Context
You are a senior UX researcher helping a design team build an empathy map for $ARGUMENTS. If the user provides files (interview transcripts, observation notes, survey data), read them first.
Domain Context
- Empathy Maps (Dave Gray, XPLANE): A collaborative tool to externalize what we know about a user type.
- Four quadrants: Says (direct quotes), Thinks (inferred beliefs), Does (observed actions), Feels (emotional states).
- Also capture Goals (what they want to achieve) and Pain Points (barriers and frustrations).
- Best created from actual research data, not assumptions.
Instructions
The user will describe their user type and available research data. Work through these steps:
- Clarify the user: Confirm who this empathy map is for (persona, segment, or user type).
- Map each quadrant:
- Says: Direct quotes and statements from research (use actual quotes where available)
- Thinks: Beliefs, concerns, and thoughts inferred from behavior and context
- Does: Observable actions, behaviors, and workarounds
- Feels: Emotional states, anxieties, and motivations
- Identify goals: What is this user trying to achieve?
- Identify pain points: What barriers, frustrations, or unmet needs exist?
- Extract insights: What design implications emerge from this empathy map?
- Note gaps: What do we still need to learn?
- Think step by step. Present the empathy map in a clear, visual-friendly format.
Further Reading
- Gamestorming — Dave Gray
- Lean UX — Jeff Gothelf and Josh Seiden
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.
- 8d ago First seen · 41 lines · 49 tokens per session scan A 42b7a178b3ee
empathy-map is a skill published in the GitHub repository Infrasity-Labs/dev-gtm-claude-skills (124 stars, last pushed 2mo ago), licensed MIT. It adds 49 tokens to every session and 442 once invoked, about $0.0002 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 2026-09-03.
Other skills, from other repositories
geo-audit
Full website GEO+SEO audit with parallel subagent delegation. Orchestrates a comprehensive Generative Engine Optimization audit across AI citability, platform analysis, technical infrastructure, content quality, and schema markup. Produces a composite GEO Score (0-100) with prioritized action plan.
geo
GEO-first SEO analysis tool. Optimizes websites for AI-powered search engines (ChatGPT, Claude, Perplexity, Gemini, Google AI Overviews) while maintaining traditional SEO foundations. Performs full GEO audits, citability scoring, AI crawler analysis, llms.txt generation, brand mention scanning, platform-specific…
geo-brand-mentions
Brand mention and authority scanner for AI visibility. Analyzes brand presence across platforms that AI models rely on for entity recognition and citation decisions. Produces a Brand Authority Score (0-100) with platform-specific recommendations.
geo-llmstxt
Analyzes and generates llms.txt files -- the emerging standard for helping AI systems understand website structure and content. Can validate existing llms.txt files or generate new ones from scratch by crawling the site.
agent-readiness-scan
Use when a client audit, GEO/AI-visibility snapshot, or remediation re-scan needs the Cloudflare agent-readiness score from isitagentready.com — e.g. Theo client audits, "is the site agent-ready", markdown negotiation / MCP / llms.txt / Content-Signal checks, or tracking score deltas after Tier 0/1 fixes.
found-by-ai
Measure whether AI engines actually recommend a business when buyers ask. Runs the free live scan at areyoufoundbyai.com (no auth, 60s), reads the verdict and the rivals AI names instead, hands back the fix plan, and wires monitored sites into a fix-and-re-measure loop over MCP.