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 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/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/patrickserrano/lacquer/customer-research"><img src="https://agentmods.dev/badge/skills/patrickserrano/lacquer/customer-research.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 | $0.00211 | $0.03307 |
| Opus 5.5 | $0.00084 | $0.01323 |
| Sonnet 5.5 | $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 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.
This is a copy
100% identical to customer-research — 7 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.
- 1mo ago First seen · 306 lines · 211 tokens per session scan A ae4769147f63
customer-research is a skill published in the GitHub repository patrickserrano/lacquer (5 stars, last pushed today), licensed MIT. It adds 211 tokens to every session and 3,307 once invoked, about $0.0008 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 100% identical to customer-research, differing in 7 lines, and is treated as a copy.
Other skills, from other repositories
printing-press-amend
Amend a published CLI from one of two input sources: (1) dogfood mode mines the active Claude Code session transcript for friction (missing flags, hand- rolled API payloads, silent-null returns); (2) direct-input mode accepts user-supplied asks (rename a command, add commands or feeds, fix a named bug, optionally…
agent-manager-reference
Query agent-manager.dev's reference API and MCP server for documentation, the coding CLIs it manages, the tools its MCP server exposes, and the current release. Use instead of scraping the website's HTML.
code-review
You are a senior code reviewer. You receive diffs via stdin.
scaffold-project
Scaffold a new app, API, backend, fullstack project, mobile app, polyglot service, monorepo, or starter with Better Fullstack. Use when the user wants to create, start, bootstrap, initialize, or generate a project from a stack description. Prefer the bundled Better Fullstack MCP server: guidance, schema…
pi-loop-forensics
Diagnose pi-go agent loops and degenerate turns — "agent loop aborted", runaway thinking with no tool calls, repeated phrases. Discriminates genuine model repetition collapse from a race, a tool-parse failure, or a too-low guard, and A/B replays a seed session across providers.
nightly-session-watch
Nightly sweep of the last 24h of pi-go sessions — anomalous runs, loop aborts, tool error rates, token waste, real prompt-token spend, and whether the observation and palace pipelines are still recording. Triages each finding to the specialist skill that diagnoses it. Use for an unattended daily health check, or on…