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 tmj-90/gaffer --skill product-discoverygit clone --depth 1 https://github.com/tmj-90/gafferWrote 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/tmj-90/gaffer/product-discovery)<a href="https://agentmods.dev/skills/tmj-90/gaffer/product-discovery"><img src="https://agentmods.dev/badge/skills/tmj-90/gaffer/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/tmj-90/gaffer/product-discovery"><img src="https://agentmods.dev/badge/skills/tmj-90/gaffer/product-discovery.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 | $0.00064 | $0.00931 |
| Opus 5 | $0.00032 | $0.00465 |
| Sonnet 5 | $0.00013 | $0.00186 |
| Haiku 4.5 | $0.00006 | $0.00093 |
Grade A, and why
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 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 — 81 lines — stays where its author put it; the contents beside it link to each section on GitHub.
De-risk product bets before building
Discovery's job is to fail fast and cheaply — identify wrong assumptions before they're baked into shipped software.
Opportunity Solution Tree (Teresa Torres)
Desired outcome (metric to move)
└── Opportunity (unmet user need / pain / desire)
└── Solution idea (intervention)
└── Experiment (cheapest test)
Rules:
- Opportunities come from user evidence — interviews, support tickets, analytics — not internal opinions.
- One desired outcome per tree. Multiple outcomes = no prioritisation.
- Solutions are hypotheses; experiments are the cheapest way to test each hypothesis.
- The tree is a living document — update as evidence accumulates.
Assumption mapping
For each solution idea, map its assumptions across four risk dimensions:
| Dimension | Question | Example assumption |
|---|---|---|
| Desirability | Do users want this? | "Users will pay $10/month for this feature" |
| Viability | Does this create sustainable business value? | "This will reduce churn by 5%" |
| Feasibility | Can we build this? | "The API supports the required event granularity" |
| Usability | Can users use this without training? | "Users will understand the new onboarding flow without docs" |
Score each assumption: Risk (1–3) × Certainty (1–3 inverse — low certainty = high score). Highest scores = test first.
Validation methods (choose by cost)
| Method | Cost | Validates |
|---|---|---|
| Desk research | Hours | Market size, competitor landscape, existing solutions |
| Customer interview (problem) | Days | Pain existence, frequency, severity, willingness to solve |
| Fake-door test | Days | Demand signal (click-through to a "coming soon" page) |
| Prototype usability test | Days–week | Usability, core interaction |
| Wizard-of-Oz / concierge | Week | Desirability + willingness to pay without building the feature |
| A/B experiment | Week–months | Behavioural impact on a metric |
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 · 81 lines · 64 tokens per session scan A c0fb08610b01
product-discovery is a skill published in the GitHub repository tmj-90/gaffer (2 stars, last pushed 2d ago), licensed Apache-2.0. It adds 64 tokens to every session and 931 once invoked, about $0.0003 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 2026-08-31.
Other skills, from other repositories
bulwark-brainstorm
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plan-creation
Create structured implementation plans via a 4-role scrum team (Product Owner, Architect, Eng/Delivery Lead, QA/Critic) with optional Agent Teams peer debate mode.
anthropic-validator
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code-review
Comprehensive code review with distinct aspect based sections. Use when reviewing code, checking for security issues, finding type safety problems, auditing code quality, or when user asks to review code, PRs or changes. Three-phase workflow runs static tools, LLM judgment, and writes diagnostic log.
create-subagent
Generates single-purpose Claude Code sub-agents for use via the Task tool. Use when creating dedicated sub-agents, scaffolding agent definitions, or generating agents with diagnostics and permissions setup.
test-audit
Audit test suites for T1-T4 violations using AST analysis, mock detection, and multi-stage synthesis. Invoke when user asks to audit tests, check test quality, find mock violations, review test effectiveness, or inspect test suites for over-mocking. Triggers automatic rewrites when quality gates fail.