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 Productfculty-aipm/PM-Copilot-by-Product-Faculty --skill regression-testinggit clone --depth 1 https://github.com/Productfculty-aipm/PM-Copilot-by-Product-FacultyWrote 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/productfculty-aipm/pm-copilot-by-product-faculty/regression-testing)<a href="https://agentmods.dev/skills/productfculty-aipm/pm-copilot-by-product-faculty/regression-testing"><img src="https://agentmods.dev/badge/skills/productfculty-aipm/pm-copilot-by-product-faculty/regression-testing/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/productfculty-aipm/pm-copilot-by-product-faculty/regression-testing"><img src="https://agentmods.dev/badge/skills/productfculty-aipm/pm-copilot-by-product-faculty/regression-testing.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 | $0.00083 | $0.01213 |
| Opus 5 | $0.00042 | $0.00607 |
| Sonnet 5 | $0.00017 | $0.00243 |
| Haiku 4.5 | $0.00008 | $0.00121 |
Grade A, and why
regression-testing 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 7d 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.
The source is not reproduced here
A licence we could not identify
The repository carries a LICENSE file, but it is custom or dual enough that GitHub cannot name it and neither can this catalogue. Unknown terms are not permission, so the body is not copied here. Read the licence at the source and decide for yourself.
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.
- 7d ago First seen · 113 lines · 83 tokens per session scan A 0cbb37a9ff20
regression-testing is a skill published in the GitHub repository Productfculty-aipm/PM-Copilot-by-Product-Faculty (76 stars, last pushed 4mo ago), with no licence file. It adds 83 tokens to every session and 1,213 once invoked, about $0.0004 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
meta-verify
Runs a second-pass quality check on a skill's output before it goes out — re-checks it against the originating skill's own Quality Gate and Operating Rules, and returns specific fixes rather than a pass/fail verdict alone. Trigger on: "verify this", "check this before I send it", "second-pass this brief", "did that…
dummy-dataset
Generate realistic dummy datasets for testing with customizable columns, constraints, and output formats (CSV, JSON, SQL, Python script). Use when creating test data, building mock datasets, or generating sample data for development and demos.
test-scenarios
Create comprehensive test scenarios from user stories with test objectives, starting conditions, user roles, step-by-step actions, and expected outcomes. Use when writing QA test cases, creating test plans, defining acceptance tests, or preparing for feature validation.
pmm-resume
Resume reviewer and tailoring engine for Product Marketing Managers (IC to VP, including AI PMM roles). Takes baseline resume + job description → dissects JD → ranks bullets by impact fit → rebuilds complete resume in one pass. Trigger on: resume + JD paste, "tailor this", "which bullets for this role", "rebuild for…
prd
Guides Product Managers and Product Marketing Managers to co-create complete Product Requirements Documents with embedded Solution Stories. Reads brain context (positioning, ICP, Revenue Levers) to anchor PRDs in strategy. Outputs: structured Solution Story for GTM communications + full PRD for execution alignment.
pre-mortem
Identifies and pressure-tests failure modes for any strategic initiative (product launch, pricing change, GTM pivot, new market entry, feature rollout) by running a cross-functional risk exercise. Loads brain context (ICP, positioning, competitive landscape) and, when available, guardrails from prior pre-mortems…