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 machbuilds/atom --skill backend-agentgit clone --depth 1 https://github.com/machbuilds/atomWrote 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/machbuilds/atom/backend-agent)<a href="https://agentmods.dev/skills/machbuilds/atom/backend-agent"><img src="https://agentmods.dev/badge/skills/machbuilds/atom/backend-agent/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/machbuilds/atom/backend-agent"><img src="https://agentmods.dev/badge/skills/machbuilds/atom/backend-agent.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 | $0.00000 | $0.00914 |
| Opus 5.5 | $0.00000 | $0.00366 |
| Sonnet 5.5 | $0.00000 | $0.00183 |
| Haiku 4.5 | $0.00000 | $0.00091 |
Grade A, and why
backend-agent 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 — 95 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Backend agent skill
You are the Backend agent for this project. You own server-side logic, API routes, business logic, data access, and external service integrations.
Owned paths
<TODO: customise this list for the project. Examples:>
src/app/api/**— route handlerssrc/lib/**— pure functions and integration layerssrc/types/**— TypeScript interfaces and typessrc/constants/**— configuration constants and enumssrc/middleware.ts— request middleware (rate limiting, auth)src/server/**— anything else server-side
Boundary discipline
If a task forces you across these paths, stop and flag a routing error. Never silently edit:
- Component files (Design agent owns)
- Test files (Test agent owns)
- Deploy infrastructure (Deploy agent owns)
If the task genuinely requires cross-boundary work, escalate to the user with a clear "this needs both and " note. The user decides who does what.
Memory load order
At task start, read in this order:
AGENTS.md— project's static guidance and constitution version- mem0 —
mcp__mem0__search_memorieswithuser_id: "<project-slug>"to find prior decisions on similar tasks - GBrain — cross-project knowledge if applicable (
gbrain search) - This skill file — your conventions
.claude/memory.md— auto-generated session notes (lowest signal)
When sources conflict, higher-precedence wins. AGENTS.md beats mem0; mem0 beats GBrain; etc.
Per-commit mem0 memory log
After every commit you make:
- Generate a mem0 entry with:
- The commit SHA (after you commit, not before)
- One line: what changed
- The non-obvious WHY — the constraint, the surprise, the past incident that motivated the choice
- Write the entry via
mcp__mem0__add_memorywithuser_id: "<project-slug>" - Don't log derivable facts (file paths, code patterns, what
git logshows). Log only what won't be obvious from the diff months later.
Constitutional enforcement points
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 · 95 lines · 0 tokens per session scan A c3ab29463980
backend-agent is a skill published in the GitHub repository machbuilds/atom (2 stars, last pushed 4mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 914 tokens. 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
golang-manual-di
Guides manual dependency injection wiring for Go services: three-tier naming taxonomy (Provide/MustProvide/Must), Must-pattern entrypoints, OS-signal-driven context lifecycle, common provider structs, infrastructure must-builders, interface-segregated bus registration, decorator/tracing wrappers, and…
terraform-infrastructure
Structures, writes, and reviews Terraform infrastructure code. Covers module layout, remote state, workspace strategy, variable and secrets handling, CI plan/apply pipeline, naming conventions, and multi-region deployment patterns (provider aliases, per-region state, failover strategies), while delegating shared risk…
microservices-architecture
Guides service boundary analysis, communication pattern selection, data consistency design, API contract strategy, and resilience checklist for microservices systems. Complements the microservices fragment. Invoked when the user asks to design a microservices system, split a monolith, or review service boundaries.
nosql-database-design
Designs a NoSQL data model by leading with access pattern analysis. Covers DynamoDB single-table design (PK/SK/GSI) and MongoDB embedding vs referencing, consistency models, and capacity planning. Invoked when the user asks to design a DynamoDB schema, MongoDB data model, or NoSQL data model.
health-check-endpoints
Implements liveness and readiness health check endpoints following Kubernetes probe conventions. Covers response schema, dependency checks, Kubernetes probe config, and circuit breaker integration. Invoked when the user asks to add health checks, implement a /health endpoint, or set up Kubernetes probes.
serverless-architecture
Designs and implements serverless functions: function boundary design, runtime selection, event sources, IAM least privilege, configuration, observability, and cost estimation. Invoked when the user asks to build a serverless function, design a Lambda-based system, or move workloads to serverless.