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 strikersam/autonomous-ai-agency --skill user-researchgit clone --depth 1 https://github.com/strikersam/autonomous-ai-agencyWrote 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/strikersam/autonomous-ai-agency/user-research)<a href="https://agentmods.dev/skills/strikersam/autonomous-ai-agency/user-research"><img src="https://agentmods.dev/badge/skills/strikersam/autonomous-ai-agency/user-research/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/strikersam/autonomous-ai-agency/user-research"><img src="https://agentmods.dev/badge/skills/strikersam/autonomous-ai-agency/user-research.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 2 findings, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Agent Snooping · line 179 Skill enumerates or reads other installed skills. Access to other skills' SKILL.md files or the skills directory reveals prompt instructions, capabilities, and secrets that should be invisible to peer skills.Fix: Remove all code or instructions that list or read other skills' files or directories. Skills should operate independently; cross-skill access is a privilege escalation.
- medium Agent Snooping · line 184 Skill enumerates or reads other installed skills. Access to other skills' SKILL.md files or the skills directory reveals prompt instructions, capabilities, and secrets that should be invisible to peer skills.Fix: Remove all code or instructions that list or read other skills' files or directories. Skills should operate independently; cross-skill access is a privilege escalation.
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 | $0.00019 | $0.01737 |
| Opus 5 | $0.00010 | $0.00869 |
| Sonnet 5 | $0.00004 | $0.00347 |
| Haiku 4.5 | $0.00002 | $0.00174 |
Grade A, and why
user-research 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 9d 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 — 186 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Skill: user-research
Module:
agent/user_research_skill.pyAgent tools registered:user_research_plan,user_research_qual,user_research_quant,user_research_synthesizeCapability tag:user_research(sub-tags:plan,qualitative,quantitative,synthesis) Maturity: stable
Purpose
Structured user-research workflows for the agent platform. Implements the four core capabilities adapted from the cookiy-ai/user-research-skill reference architecture:
| Capability | Tool name | What it does |
|---|---|---|
| Plan | user_research_plan |
Produce a structured research plan (objectives, hypotheses, methods, sample size, timeline) from a research question. |
| Qual | user_research_qual |
Extract themes, pain points, and desires from interview transcripts or open-ended survey responses. |
| Quant | user_research_quant |
Compute descriptive statistics (mean, median, σ, distribution, segment cuts) for a numeric series. |
| Synthesize | user_research_synthesize |
Combine qual + quant into a decision-ready research brief with executive summary, findings, and recommendations. |
When to Use
- Plan: Before kicking off a new research initiative — defines scope, objectives, methods, and sample size.
- Qual: After collecting 3+ interview transcripts or open-ended responses — finds themes and pain points.
- Quant: After collecting numeric survey data or experiment metrics — produces the descriptive stats and segment cuts.
- Synthesize: After both qual and quant are available — produces the executive brief a stakeholder will read.
Architecture
The skill is implemented as a pure-function library with a thin tool-wrapping layer:
- All four capabilities are pure functions (
plan_research,analyze_qualitative,analyze_quantitative,synthesize_research) that take and return Pydantic v2 models. - The functions are then registered with the agent
ToolRegistryvia the@registry.agent_tooldecorator, so the agent loop can invoke them like any other tool. - No LLM calls inside the skill. The LLM is the executor that uses the tool; the tool provides the structural framework (Pydantic contracts, sample-size math, theme extraction heuristics, descriptive stats). This keeps the skill fast, testable, and free of hidden costs.
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.
- 9d ago First seen · 186 lines · 19 tokens per session scan A 127a57dd2c27
user-research is a skill published in the GitHub repository strikersam/autonomous-ai-agency (8 stars, last pushed today), licensed MIT. It adds 19 tokens to every session and 1,737 once invoked, about $0.0001 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
assimilate-popular-workflows
This skill should be used when the user asks to "find skills in the wild", "assimilate popular workflows", "discover SKILL.md files in repos", "research external skills", "find workflow patterns", "survey the skill landscape", "what skills exist out there", or wants to investigate public repositories for extractable…
process-builder
Scaffold new babysitter process definitions following SDK patterns, proper structure, and best practices. Guides the 3-phase workflow from research to implementation.
mcp-app-verification
Comprehensive verification checklists for MCP Apps. Tests with basic-host reference, validates handler-before-connect, text fallback, resource URI linking, single-file bundling, host styling, CSP, and legacy pattern detection.
guardrails-ai-setup
Guardrails AI validation framework setup for LLM applications. Implement input/output validation, safety checks, and structured output enforcement.
mcp-app-scaffolding
Scaffolds MCP App project structure with correct directory layout, dependencies, entry points, and framework-specific templates. Handles React (useApp hook), Vanilla JS, Vue, Svelte, Preact, and Solid.
mcp-csp-investigation
Comprehensive Content Security Policy audit for MCP Apps in sandboxed iframes. Discovers all network origins, traces them to source, and generates CSP configuration for registerAppResource.