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 saemihemma/lead-producer-oss --skill team-open-sourcegit clone --depth 1 https://github.com/saemihemma/lead-producer-ossWrote 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/saemihemma/lead-producer-oss/team-open-source)<a href="https://agentmods.dev/skills/saemihemma/lead-producer-oss/team-open-source"><img src="https://agentmods.dev/badge/skills/saemihemma/lead-producer-oss/team-open-source/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/saemihemma/lead-producer-oss/team-open-source"><img src="https://agentmods.dev/badge/skills/saemihemma/lead-producer-oss/team-open-source.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 | $0.00023 | $0.00459 |
| Opus 5.5 | $0.00009 | $0.00184 |
| Sonnet 5.5 | $0.00005 | $0.00092 |
| Haiku 4.5 | $0.00002 | $0.00046 |
Grade A, and why
team-open-source 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 2mo 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 — 56 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Open Source Team
Purpose
Decide whether a project is ready to live in public and whether an existing open source surface is healthy enough to scale.
Composition
- role-open-source-engineer: licensing, dependency hygiene, release process, supply-chain
- role-community-developer: contributor onboarding, governance, ecosystem health
- role-security-engineer: attack surface, disclosure readiness, security risk
- role-technical-product-manager: strategy, roadmap fit, operational cost of openness
- Synthesis: Devil's Advocate pressure, acceptance
Use When
- Preparing to open source a project
- Reviewing health of existing open source project
- Accepting external contributions at scale
- Evaluating high-impact dependency or governance change
Do NOT Use When
- Routine internal code review
- Live security incident
- Community moderation with no code-surface question
- Generic marketing
Workflow
- Load truth sources: licenses, dependencies, release process, governance, contributor path.
- Review compliance, security, contributor experience, strategy together.
- Surface conflicts: openness vs risk, community demand vs product direction.
- Synthesize single release or rework recommendation.
Default Output
OPEN SOURCE TEAM REVIEW
=======================
Readiness: release blockers, licensing/dependency blockers
Security: exposure risks, disclosure/supply-chain concerns
Community: onboarding friction, governance/transparency gaps
Strategy: why public now or not, maintenance obligations
Verdict: Ready / Rework / Not Yet, required next actions
Conflict Resolution
- Open Source Engineer owns compliance and release process. Security Engineer can block on exposure risk.
- Community Developer flags onboarding friction but cannot override security or compliance blockers.
- TPM owns strategic fit and operational cost assessment.
Anti-Drift Rules
- "It compiles" != "safe to publish." Transitive dependencies are part of the release surface.
- Do not conflate community engagement with code readiness.
- Maintenance burden is part of the go/no-go decision, not an afterthought.
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.
- 2mo ago First seen · 56 lines · 23 tokens per session scan A 90092cfa1ed3
team-open-source is a skill published in the GitHub repository saemihemma/lead-producer-oss (2 stars, last pushed 2mo ago), licensed MIT. It adds 23 tokens to every session and 459 once invoked, about $0.0001 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 2026-08-31.
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specification-writing
A workflow for writing complete patent specifications from patent claims and an invention disclosure. It adapts the document to a chosen jurisdiction, such as the US, Europe, or China.
terms-of-service
Draft terms of service and a companion privacy notice matched to the product's form and feature modules, not legal advice, delivered as a Word document.
x-scorecard
OpenSSF Scorecard for assessing open source project security. Check security best practices and compliance. Dependency: This is an x-cmd module. Install x-cmd first (see x-cmd skill for installation options). see x-cmd skill for installation.
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Apply the NIST AI Risk Management Framework (AI RMF 1.0) and adjacent guidance to AI / ML systems — model lifecycle governance, fairness and bias evaluation, robustness, transparency, accountability, third-party model risk, monitoring for drift, and AI incident response. Broader than prompt-injection (which is the…