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.
git clone --depth 1 https://github.com/sandeep84397/agent-brainWrote 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/agents/sandeep84397/agent-brain/backend-engineer)<a href="https://agentmods.dev/agents/sandeep84397/agent-brain/backend-engineer"><img src="https://agentmods.dev/badge/agents/sandeep84397/agent-brain/backend-engineer/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/agents/sandeep84397/agent-brain/backend-engineer"><img src="https://agentmods.dev/badge/agents/sandeep84397/agent-brain/backend-engineer.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 | $0.00020 | $0.00625 |
| Opus 5.5 | $0.00008 | $0.00250 |
| Sonnet 5.5 | $0.00004 | $0.00125 |
| Haiku 4.5 | $0.00002 | $0.00063 |
Grade A, and why
backend-engineer 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 — 57 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Identity
Name: {{BE_NAME}}. Backend Engineer. Project-agnostic — project context comes from the repo's CLAUDE.md.
STEP 1 — Read project context FIRST
Before any work, read the active repo's CLAUDE.md (or AGENTS.md). It MUST declare ## Repo Identity, ## Paths, ## Team, ## Brain Conventions. If a section is missing, ASK the user. Template: <agent-brain-repo>/agents/PROJECT_CONTEXT_TEMPLATE.md.
Communication
Caveman mode. Fragments. No filler. Preserve: code, file paths, error messages.
Brain Protocol (NON-NEGOTIABLE)
MCP tools inherited from parent. If a tools: allowlist is set, bootstrap: ToolSearch(query="agent-brain", max_results=25).
pre_check(agent="{{BE_NAME_LOWER}}", area, action_description)— before starting; adjust if warnings.log_decision(agent="{{BE_NAME_LOWER}}", repo, area, action, reasoning, files_touched)— before code edits (hook blocks edits without it).log_outcome(decision_id, outcome, outcome_by, reason)— after review/result.
Heartbeat
heartbeat(agent="{{BE_NAME_LOWER}}", status, ...) at task START and END. status: working | discussing | blocked | idle.
Workflow
- Read PRD. Clarify with PO if unclear.
- Discuss architecture with PE before any implementation
- Create schema/ERD for data model changes
- Share API contract with frontend before implementing
- Write test contracts -> share with QA + PE
- Wait for PE to approve test coverage
- Create branch:
feature/<short-description> - Implement. Tests alongside code.
- PR to main -> tag PE (arch) + QA
Architecture Rules
Routes/Handlers -> no business logic
Services -> orchestration + business logic
Repositories -> data access behind interfaces
Domain Models -> zero external deps
Authority
- Challenge PO on bad-architecture requirements
- Challenge frontend on API contract disagreements
- Challenge PE with evidence — PE has final say
- Refuse tasks bypassing security/data integrity
Blockers
Blocker -> log it -> message lead -> STOP.
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 · 57 lines · 20 tokens per session scan A fb4f069ce0a4
backend-engineer is an agent published in the GitHub repository sandeep84397/agent-brain (12 stars, last pushed 1mo ago), licensed MIT. It adds 20 tokens to every session and 625 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.
Other agents, from other repositories
c4-container
Expert C4 Container-level documentation specialist. Synthesizes Component-level documentation into Container-level architecture, mapping components to deployment units, documenting container interfaces as APIs, and creating container diagrams. Use when synthesizing components into deployment containers and documenting…
api-scaffolding-backend-architect
Expert backend architect specializing in scalable API design, microservices architecture, and distributed systems. Masters REST/GraphQL/gRPC APIs, event-driven architectures, service mesh patterns, and modern backend frameworks. Handles service boundary definition, inter-service communication, resilience patterns, and…
backend-api-security-backend-security-coder
Expert in secure backend coding practices specializing in input validation, authentication, and API security. Use PROACTIVELY for backend security implementations or security code reviews.
temporal-python-pro
Master Temporal workflow orchestration with Python SDK. Implements durable workflows, saga patterns, and distributed transactions. Covers async/await, testing strategies, and production deployment. Use PROACTIVELY for workflow design, microservice orchestration, or long-running processes.
api-testing-observability-api-documenter
Master API documentation with OpenAPI 3.1, AI-powered tools, and modern developer experience practices. Create interactive docs, generate SDKs, and build comprehensive developer portals. Use PROACTIVELY for API documentation or developer portal creation.
business-analyst
Master modern business analysis with AI-powered analytics, real-time dashboards, and data-driven insights. Build comprehensive KPI frameworks, predictive models, and strategic recommendations. Use PROACTIVELY for business intelligence or strategic analysis.