Benchmark agent accuracy, tool calling reliability, and task completion rates.
npx askills install multiagent-evals-benchmark-suite
Client Trigger & Invocation Simulator
$multiagent-evals-benchmark-suite <your task>Type `$multiagent-evals-benchmark-suite` in the chat or invoke via natural language. Codex automatically identifies the skill from ~/.agents/skills or project .skills, mounts bundled scripts, and executes inside the workspace sandbox.
$multiagent-evals-benchmark-suite Evaluate an autonomous coding agent against 100 benchmark software bug resolution tasks.
Overview & Problem Solved
When modern frontier LLMs (like Claude 3.7, GPT-4o, or deep reasoning agents) execute complex domain tasks without standard operating procedures, they frequently suffer from context drift, incorrect CLI arguments, or non-standard file hierarchies. Multi-Agent Evals & Benchmark Scoring Suite eliminates probabilistic guessing by supplying the Agent with an authoritative, sandboxed execution specification.
By packaging verified workflows into a structured SKILL.md manifest, this skill establishes deterministic boundaries, verifies dependencies, and executes tasks using proven engineering conventions.
Operational Impact: Without Skill vs. With Skill
Raw frontier models possess deep knowledge, but probabilistic prompting lacks rigid engineering guardrails. Packaging Multi-Agent Evals & Benchmark Scoring Suite into a verified SKILL.md transforms speculative model behavior into reproducible, deterministic delivery.
- Speculative CLI Flags: Model guesses experimental command arguments or deprecated API endpoints.
- Lazy Placeholder Code: Outputs
// TODO: implement error handlingor skips critical verification phases. - Directory Pollution: Generates non-standard file structures that break existing project linters and monorepo roots.
- Extraneous Context Bloat: Wastes thousands of output tokens on narrative pleasantries rather than verified deliverables.
function process() {
// TODO: Connect database here
console.log("Done");
}- Codified Workflow Stages: Follows strict step-by-step algorithms explicitly codified in SKILL.md.
- Zero-Placeholder Delivery: Every deliverable is 100% implemented, with complete error boundaries and types.
- Automatic Pre-Flight Check: Agent inspects environment dependencies (Python/Node/Docker) before modifying files.
- Self-Healing Verification: Runs automated linting, test suites, or syntax parsers before concluding the turn.
export async function execute() {
const validated = schema.parse(input);
return await auditAndPersist(validated);
}Core Capabilities & Guarantees
Follows pre-tested instructions and step-by-step algorithms, avoiding experimental or hallucinated flags.
Runs with identical behavioral fidelity across OpenAI Codex, Claude Code, Cursor IDE, and Windsurf Cascade.
Enforces workspace-scoped file mutations and shields environment secrets against accidental prompt leakage.
Automates test verification, linter checks, and syntax validation before completing any user turn.
Agent Execution Pipeline
Intent Matching & Context Ingestion
The agent scans the user request against the skill's YAML frontmatter triggers and loads the verified instructions into active working memory.
Toolchain & Dependency Validation
The runtime verifies that necessary tools, interpreters, and environment configurations exist within the workspace sandbox before proceeding.
Deterministic Execution & Delivery
The agent executes the codified SOP, runs verification checks, and outputs high-fidelity deliverables without extraneous narration.
Validated Prompt Templates
(Click to copy)“Evaluate an autonomous coding agent against 100 benchmark software bug resolution tasks.”
“Measure tool calling failure rates and recovery efficiency across frontier models.”
“Calculate cost per successful task completion to optimize model selection.”
Automated Security & Audit Report
Workspace Scoped
Cannot escape workspace rootInstruction Only
Zero system subprocessesOffline Sandbox
No internet egressNone Required
Runs fully standaloneEnterprise & Production Use Cases
Frequently Asked Questions
Q: What makes Multi-Agent Evals & Benchmark Scoring Suite different from a standard MCP server?
MCP (Model Context Protocol) provides external protocol connections to databases or APIs. In contrast, Multi-Agent Evals & Benchmark Scoring Suite is a high-level operational skill (SKILL.md) that teaches AI Agents procedural intelligence, deterministic step-by-step SOPs, parameter constraints, and self-correction workflows.
Q: Is Multi-Agent Evals & Benchmark Scoring Suite compatible with frontier models like Claude 3.7 Sonnet, GPT-4o, and DeepSeek-R1?
Yes. The skill is packaged with standard markdown instructions (SKILL.md) and YAML frontmatter, adhering to the universal agent skill standard natively supported across Codex, Claude Code, Cursor, and Windsurf.
Q: What permissions does Multi-Agent Evals & Benchmark Scoring Suite require in agent mode?
Every listed skill undergoes automated static analysis scoring 98/100. It strictly adheres to workspace-scoped filesystem boundaries and does not execute untrusted remote code.
Q: Can our team customize or extend the rules in this SKILL.md?
Yes. Once installed in your project root (.skills/multiagent-evals-benchmark-suite/SKILL.md or ~/.agents/skills/multiagent-evals-benchmark-suite/), you can customize the instructions to include internal team guidelines, custom linters, or proprietary APIs.
SKILL.md Source Specification
---
name: "multiagent-evals-benchmark-suite"
description: "Benchmark agent accuracy, tool calling reliability, and task completion rates."
metadata:
author: "OpenAI"
version: "1.0.0"
runtime_compatibility:
- "codex"
- "claude-code"
- "cursor"
- "windsurf"
triggers:
- "benchmark agent performance"
- "evaluate tool calling accuracy"
- "run pass@k evals"
permissions:
fs_read: true
fs_write: "workspace"
shell_exec: false
net_access: false
---
# Multi-Agent Evals & Benchmark Scoring Suite
Runs automated evaluation suites against LLM agents: ground truth comparison, tool trajectory scoring, hallucination rates, token cost profiling, and pass@k metric calculation.
## When to Invoke
Invoke this skill whenever you need to:
- benchmark agent performance
- evaluate tool calling accuracy
- run pass@k evals
## Execution SOP & Rules
1. Establish a diverse test dataset of input prompts with verified expected outputs or unit tests.
2. Measure deterministic metrics: execution success rate, syntax errors, and test pass rates.
3. Track intermediate tool trajectory: penalize unnecessary loops, hallucinated tools, or redundant reads.
4. Generate structured benchmark reports with confidence intervals and latency breakdowns.
## Verification Checklist
- Run local linting, syntax validation, and type-checks before reporting completion.
- Ensure all created or modified files adhere to workspace boundaries and avoid uncommitted secrets.
- Verify tests or build commands exit cleanly with return code 0.
Related Agents Skills
Run and customize open-source language models locally on CPU and Apple Silicon.
Maintain long-running agent conversations with hierarchical rolling memory summaries.
Compose declarative, streaming, and production-grade LLM chains with LCEL syntax.