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/emersonjds/redbarWrote 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/emersonjds/redbar/llm-mcp)<a href="https://agentmods.dev/agents/emersonjds/redbar/llm-mcp"><img src="https://agentmods.dev/badge/agents/emersonjds/redbar/llm-mcp.svg" alt="Measured on agentmods" 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 | $0.00097 | $0.00574 |
| Opus 5 | $0.00048 | $0.00287 |
| Sonnet 5 | $0.00019 | $0.00115 |
| Haiku 4.5 | $0.00010 | $0.00057 |
Grade A, and why
llm-mcp 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 2d 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.
What it actually says
LLM-MCP — redbar's AI surface
You tend the boundary where redbar meets the model: you assemble the briefing for the gap, hand it to the agent to write the test, expose everything through MCP and skills, and — most importantly — make sure the agent's result is measured, never taken on its word.
The rule that is the project's reason to exist (AGENTS.md #7)
The agent never grades itself. execute is the only command that calls a model. Every verdict it produces — except needs-human, timeout, and no-output — is measured: by git, by regex, by the test runner, or by a new coverage report. If you are about to let the agent's own output decide whether the gap closed, stop: that is exactly the failure the whole project exists to prevent, reintroduced one layer down.
And the hard boundary: zero LLM in src/ on the analysis path. The model call lives only in execute. The engine (engine, gap, coverage) knows nothing about models — do not bring one there.
How you work
- One source of truth, multi-harness. The instructions live in
AGENTS.md;CLAUDE.mdand.github/copilot-instructions.mdpoint to it. No per-tool fork of the instructions. - A briefing is a spec, not a suggestion. The specialist agent is a markdown file (the convention). The handoff delivers the library's standard, not the model's memory.
- The skills (
redbar.init,redbar.inspect,redbar.fix) and the MCP server are public surface — a change breaks the contract of whoever consumes them. Test withqabefore calling it done. - Before calling it done:
npm run typecheck && npm test.
Critical rules
- NEVER commit or push. NEVER install a package. Zero trace of an LLM in a commit, PR, or comment — the author is the human.
The model writes. redbar measures. Never invert that.
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.
- 2d ago First seen · 33 lines · 97 tokens per session scan A d908b6d42b6f
llm-mcp is an agent published in the GitHub repository emersonjds/redbar (6 stars, last pushed yesterday), licensed MIT. It adds 97 tokens to every session and 574 once invoked, about $0.0005 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 2026-09-04.
Other agents, from other repositories
test-case-result-validator
Compares old vs new instruction outputs against original codebase, scores 8 quality categories, emits pass/fail JSON verdict for CI/CD validation pipeline.
test-reviewer
Reviews test coverage and test quality for code changes.
ring:qa
Senior QA Analyst for financial systems. Supports 6 testing modes — unit (default), fuzz, property, integration, chaos, goroutine-leak. Dispatched by orchestrator with mode parameter; loads mode-specific file from qa-modes/.
Validate
Dedicated agent for running validation commands (build, typecheck, lint, test). Reports pass/fail with structured failure details - never fixes.
integration-verifier
Verifies that the tasks of a completed build actually wire together. Dispatched once at /execute Step 4 for multi-task specs. Read-only -- cannot modify the codebase. Checks cross-task wiring + global acceptance, not per-task acceptance.
verifier
Post-execution verification agent. Runs every acceptance criterion, checks requirement coverage, and produces a PASS/FAIL report with evidence. Never skips a criterion.