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 sfc-gh-eraigosa/dotfiles --skill teamgit clone --depth 1 https://github.com/sfc-gh-eraigosa/dotfilesWrote 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/sfc-gh-eraigosa/dotfiles/team)<a href="https://agentmods.dev/skills/sfc-gh-eraigosa/dotfiles/team"><img src="https://agentmods.dev/badge/skills/sfc-gh-eraigosa/dotfiles/team/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/sfc-gh-eraigosa/dotfiles/team"><img src="https://agentmods.dev/badge/skills/sfc-gh-eraigosa/dotfiles/team.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 | $0.00020 | $0.01344 |
| Opus 5 | $0.00010 | $0.00672 |
| Sonnet 5 | $0.00004 | $0.00269 |
| Haiku 4.5 | $0.00002 | $0.00134 |
Grade A, and why
team 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 — 46 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Route a development task to the best-fit specialist agent from the AI teams.
If the request names a squad, recommend that squad's members. If it names a team,
list that team's members. Otherwise infer the team from the task (file types, domain,
keywords), then pick the single best member — and a squad if the task spans teams.
Invoke a member with @agent-<team>-<role> (Claude) / <team>-<role> (Antigravity).
Prefer the most specific member; do not fan out the whole team. Explain the choice in
one line.
Members (team-role: use when)
- web-api: Designing or changing backend HTTP/GraphQL APIs, OpenAPI/GraphQL schemas, request validation, route handlers, auth middleware, or database migrations that shape the frontend contract.
- web-fe: Building or modifying UI components, styling, accessibility (ARIA/axe), client-side routing, Storybook stories, or data-bound views against an API contract
- web-webarch: Delegate for framework selection/upgrades, rendering-strategy decisions (SSR/SSG/ISR/CSR), monorepo package boundaries, cache-control and CDN topology, design-system governance, or any cross-cutting architectural trade-off requiring an RFC/ADR.
- web-webqa: End-to-end/cross-browser test authoring or review, Playwright/Cypress suites, Lighthouse performance/accessibility budgets, visual-regression diffing, or filing/triaging bug reports for web features marked ready for QA.
- web-websec: Auditing web code, configs, or dependencies for vulnerabilities — OWASP Top 10 checks, security header/CSP validation, npm/pnpm dependency audits, secret scanning, auth/session/token review, or CORS policy enforcement before merge or release.
- go-goarch: Designing or reviewing service boundaries, defining Go interfaces before implementation, governing .proto/RPC contracts, modeling goroutine lifecycles, or planning cross-service refactors and distributed tracing topology.
- go-godev: Writing or modifying Go source — implementing services, CLIs (cobra), libraries, concurrency, error handling, or module hygiene in cmd/, internal/, or pkg/.
- go-goqa: Validating a Go implementation marked Ready for QA — running the race detector, writing fuzz/benchmark/integration tests, enforcing coverage gates, or chasing goroutine leaks and flaky tests.
- go-gosec: A Go change needs a security review: gosec/govulncheck findings, new go.mod dependencies, CVE/SBOM auditing, secret handling, TLS/mTLS config, or container privilege hardening before staging promotion.
- terraform-aws-cloudarch: Designing AWS account topology, landing zones, VPC/network tiers, multi-region RPO/RTO strategy, ADRs, or evaluating designs against the Well-Architected pillars and cost governance.
- terraform-aws-cloudsec: Static policy scanning of Terraform (checkov/tfsec), IAM role and policy audits, encryption-at-rest enforcement, CloudTrail/VPC Flow Log verification, S3 public access blocks, or SCP guardrail authoring.
- terraform-aws-infra: Authoring or refactoring Terraform modules, composing root modules in environments/, writing variable blocks, configuring remote state, reviewing terraform plan output, or applying tagging and lifecycle rules to AWS resources
- terraform-aws-platform: Setting up or fixing CI/CD pipelines, GitHub Actions workflows, Atlantis configs, ECR lifecycle policies, ECS/EKS rollouts, container build automation, or CI secret handling via OIDC for AWS workloads.
- ai-ci-aiarch: Designing or reviewing agentic workflows, RAG/retrieval architecture, multi-model chains, model tier policies, eval harnesses, or LLMOps system design and ADRs
- ai-ci-aidev: Building or modifying LLM-backed features — prompt pipelines, RAG, embeddings, structured-output parsing, model-abstraction wrappers, or eval suites under evals/
- ai-ci-ci: Authoring or fixing CI pipelines — GitHub Actions YAML, Makefile targets, caching strategy, build matrices, runner provisioning, concurrency controls, or speeding up lint/test jobs.
- ai-ci-modelops: The session needs to deploy, configure, or scale model-serving stacks (Ollama/LiteLLM/vLLM), manage GPU resource limits and VRAM, maintain the model registry, set inference latency SLOs, or design canary rollouts and air-gap model mirrors.
- architecture-adversary: Independently verifying or refuting a claim, finding, or another agent's answer before it is trusted — adversarial second-opinion on audit/review output, fact-checking a conclusion against the actual source, calibrating severity, and filtering false positives out of a set of findings.
- architecture-em: The main session needs cross-team conflict resolution among architects, translation of business goals into engineering objectives, roadmap or risk-register updates, priority arbitration, plain-language stakeholder summaries of architectural decisions, or organizing a new objective (a GitHub issue or a gss draft PR) into the docs/mbo design→spec→plan pipeline via the mbo-plan skill.
- architecture-principal: A change touches auth, data migrations, public APIs, or core infrastructure and needs a mandatory critical review; engineering standards, linter configs, or the design pattern library must be defined or enforced; tech debt must be cataloged or prioritized for sprint allocation.
- architecture-secarch: Designing or reviewing security architecture — STRIDE threat models, zero-trust enforcement, identity/OAuth2/OIDC flows, secrets management strategy, compliance mapping (SOC 2, GDPR, HIPAA), or blast-radius analysis of a new service or major feature.
- architecture-sysarch: Significant design decisions, service-boundary or data-store choices, multi-team/multi-service changes, cross-cutting contracts (observability/auth/error propagation), dependency-graph review, or capacity modeling before production.
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 · 46 lines · 20 tokens per session scan A f3f6d693042d
team is a skill published in the GitHub repository sfc-gh-eraigosa/dotfiles (46 stars, last pushed today), licensed Apache-2.0. It adds 20 tokens to every session and 1,344 once invoked, about $0.0001 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 2026-08-30.
Other skills, from other repositories
review-pr
Review and merge GitHub pull requests for Spatie packages. Use when asked to review a PR, review a pull request, merge a PR, or when given a GitHub PR URL to review. Also triggers on 'review this PR,' 'check this pull request,' 'merge this,' or '/review-pr'. Uses gh CLI for all GitHub operations.
git-commit
This skill should be used BEFORE running any git commit command. Triggers when about to run git commit. Ensures commit messages follow Conventional Commits specification and prompts for the Jira ticket number.
gh-cli
../../../.claude/skills/gh-cli/SKILL.md.
gh-pr
../../../.claude/skills/gh-pr/SKILL.md.
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