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 aryaniyaps/lamina --skill lamina-product-discoverygit clone --depth 1 https://github.com/aryaniyaps/laminaWrote 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/aryaniyaps/lamina/lamina-product-discovery)<a href="https://agentmods.dev/skills/aryaniyaps/lamina/lamina-product-discovery"><img src="https://agentmods.dev/badge/skills/aryaniyaps/lamina/lamina-product-discovery/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/aryaniyaps/lamina/lamina-product-discovery"><img src="https://agentmods.dev/badge/skills/aryaniyaps/lamina/lamina-product-discovery.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00069 | $0.00541 |
| Opus 5.5 | $0.00028 | $0.00216 |
| Sonnet 5.5 | $0.00014 | $0.00108 |
| Haiku 4.5 | $0.00007 | $0.00054 |
Grade A, and why
lamina-product-discovery 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 — 36 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Lamina Product Discovery
Reference-loading protocol
- Match the request's primary product decision to one row below.
- Open that linked reference before answering. Add another only when a second decision materially changes the answer; do not preload the directory.
- Start the response with
Using lamina-product-discovery: <topic path(s)>so the selected decision lens is auditable.
Topic index
| Decision signal | Read | Adds |
|---|---|---|
| Need to turn a vague request into a bounded problem and explicit non-goals | Problem Framing | actors, outcomes, domain boundary, and blocking unknowns |
| Need to establish or update the product's business foundation | Business Context | evidence-backed context artifact, assumptions, and changelog |
| Need to reconcile goals, permissions, or conflicts across multiple actors | Multi-Actor Goal Alignment | explicit conflict resolution and authority mapping |
| Need to derive candidate product behavior from an outcome or user ask | Feature Discovery | entities, operations, workflows, and clarify gates |
| Need to choose which workflows or risks belong in the current slice | Scope Prioritization | invariant-, actor-, dependency-, and verification-aware priority |
| Need to express accepted scope as testable product behavior | Requirements Definition | promises, graph nodes, scenarios, and traceability |
| Need to classify assumptions, policy forks, or high-consequence choices | Product Decisions and Tradeoffs | reversibility, provenance, confidence, and blocking rules |
| Need to rank findings or decide whether a change targets product or contract | Decision Making | impact filter, fix target, and next-action ordering |
Working rule
What ships with it
8 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.
- references/business-context.md 8.2 KB
- references/decision-making.md 2.0 KB
- references/feature-discovery.md 508 B
- references/feature-prioritization.md 620 B
- references/problem-framing.md 489 B
- references/requirements-definition.md 693 B
- references/stakeholder-alignment.md 649 B
- references/tradeoffs.md 627 B
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 · 36 lines · 69 tokens per session scan A 1672e9b70118
lamina-product-discovery is a skill published in the GitHub repository aryaniyaps/lamina (121 stars, last pushed 10d ago), licensed Apache-2.0. It adds 69 tokens to every session and 541 once invoked, about $0.0003 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 2026-08-30.
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