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 agentmods add skills/patrickserrano/lacquer/customer-researchnpx skills add patrickserrano/lacquer --skill customer-researchgit clone --depth 1 https://github.com/patrickserrano/lacquerWrote 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/patrickserrano/lacquer/customer-research)<a href="https://agentmods.dev/skills/patrickserrano/lacquer/customer-research"><img src="https://agentmods.dev/badge/skills/patrickserrano/lacquer/customer-research.svg" alt="Measured on agentmods" 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 | $0.00211 | $0.03307 |
| Opus 5 | $0.00105 | $0.01654 |
| Sonnet 5 | $0.00042 | $0.00661 |
| Haiku 4.5 | $0.00021 | $0.00331 |
Grade A, and why
customer-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 4d 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.
This is a copy
100% identical to customer-research — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 306 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Customer Research
You are an expert customer researcher. Your goal is to help uncover what customers actually think, feel, say, and struggle with — so that everything from positioning to product to copy is grounded in reality rather than assumption.
Before Starting
Check for product marketing context first:
If .agents/product-marketing.md exists (or .claude/product-marketing.md, or the legacy product-marketing-context.md filename, in older setups), read it before asking questions. Use that context to skip questions already answered.
Three Modes of Research
Mode 1: Analyze Existing Assets
You have raw research material (transcripts, surveys, reviews, tickets). Your job is to extract signal.
Mode 2: Mine Existing Signal (Online)
You gather intel from online sources (Reddit, G2, forums, communities, review sites) — customers speaking in public, unprompted. Your job is to know where to look and what to extract.
Mode 3: Go Ask (Primary Research)
No signal exists yet, or you need answers only the customer can give. You run interviews and surveys directly. For the full playbook — the PMF survey, 5-why laddering, outreach templates, incentives, best-customer recruiting, and the confirmation-bias guardrail — read references/interviews-and-surveys.md.
Most engagements combine modes. Mine what's already public (Mode 2) before you ask (Mode 3) — it tells you what to ask and in whose words. Establish which mode(s) apply before proceeding.
Mode 1: Analyzing Existing Research Assets
Asset Types
Customer interview / sales call transcripts
- Extract: pains, triggers, desired outcomes, language used, objections, alternatives considered
- Look for: the moment they decided to look for a solution, what they tried before, what success looks like to them
Survey results
- Segment responses by customer tier, use case, or tenure before drawing conclusions
- Flag: what open-ended answers say vs. what multiple-choice answers say (they often conflict)
- Identify: the 20% of responses that contain the most useful signal
What ships with it
3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 4d ago First seen · 306 lines · 211 tokens per session scan A ae4769147f63
customer-research is a skill published in the GitHub repository patrickserrano/lacquer (3 stars, last pushed yesterday), licensed MIT. It adds 211 tokens to every session and 3,307 once invoked, about $0.0011 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to customer-research, differing in 0 lines, and is treated as a copy.
Other skills, from other repositories
opik
This skill should be used when the user needs to add Opik tracing or integrations to their code, instrument an LLM application, or needs reference for Opik SDK usage (Python, TypeScript, REST API). Use for tasks like "add tracing", "instrument my code", "use trackopenai", "add OpikTracer", "what span types are…
ollama-review
Get a second opinion from a local Ollama LLM on your current code changes. Analyzes staged/unstaged diffs and returns prioritized findings. No API keys needed. Use when user asks to "review with Ollama", "local code review", or "review offline".
mcp-builder
Guide for creating high-quality MCP (Model Context Protocol) servers that enable LLMs to interact with external services through well-designed tools. Use when building MCP servers to integrate external APIs or services, whether in Python (FastMCP) or Node/TypeScript (MCP SDK).
swarm
Run a multi-agent audit of a codebase by spawning specialized parallel subagents (security, performance, tests, architecture, dead-code), then synthesize their findings into a single prioritized action plan. Use this whenever the user runs /swarm, asks to "audit the repo," "review this codebase," "find issues across…
hyper-plan
Use when about to start a non-trivial implementation that needs decomposition before coding. Also when the user invokes /hyperclaude:hyper-plan. Produces an ordered, bite-sized plan in .hyperclaude/plans/ — the input for /hyperclaude:hyper-plan-review and /hyperclaude:hyper-implement.
hyper-docs-review
Use after documentation edits — typically after the documenter agent runs, or when the user invokes /hyperclaude:hyper-docs-review. Runs Codex for accuracy, drift, completeness, broken links, cross-doc inconsistencies, redundancy — NOT prose or style. Distinct from /hyperclaude:hyper-code-review (code diffs) and…