MCP Server Builder
anthropics/skills
Guides the design and implementation of Model Context Protocol servers in TypeScript or Python, from tool naming and error messages to evaluation.
Autonomous AI agent benchmark evaluation register: task completion rates, planning accuracy, tool invocation precision, and cost benchmarks.
Install AI Agent Evaluation Benchmarking
$ npx skills add sickn33/agentic-awesome-skills --skill ai-agent-evaluation-benchmarking -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install sickn33/agentic-awesome-skills ai-agent-evaluation-benchmarking --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/sickn33/agentic-awesome-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/ai-agent-evaluation-benchmarking .claude/skills/ai-agent-evaluation-benchmarking && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "ai-agent-evaluation-benchmarking" agent skill from https://github.com/sickn33/agentic-awesome-skills/tree/main/skills/ai-agent-evaluation-benchmarking into .claude/skills/ai-agent-evaluation-benchmarking/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ai-agent-evaluation-benchmarking", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/sickn33/agentic-awesome-skills/tree/main/skills/ai-agent-evaluation-benchmarkingType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add sickn33/agentic-awesome-skills --skill ai-agent-evaluation-benchmarking -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install sickn33/agentic-awesome-skills ai-agent-evaluation-benchmarking --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/sickn33/agentic-awesome-skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/ai-agent-evaluation-benchmarking .agents/skills/ai-agent-evaluation-benchmarking && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "ai-agent-evaluation-benchmarking" agent skill from https://github.com/sickn33/agentic-awesome-skills/tree/main/skills/ai-agent-evaluation-benchmarking into .agents/skills/ai-agent-evaluation-benchmarking/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ai-agent-evaluation-benchmarking", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add sickn33/agentic-awesome-skills --skill ai-agent-evaluation-benchmarking -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install sickn33/agentic-awesome-skills ai-agent-evaluation-benchmarking --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/sickn33/agentic-awesome-skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/ai-agent-evaluation-benchmarking .cursor/skills/ai-agent-evaluation-benchmarking && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "ai-agent-evaluation-benchmarking" agent skill from https://github.com/sickn33/agentic-awesome-skills/tree/main/skills/ai-agent-evaluation-benchmarking into .cursor/skills/ai-agent-evaluation-benchmarking/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ai-agent-evaluation-benchmarking", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/sickn33/agentic-awesome-skills.git --path skills/ai-agent-evaluation-benchmarking--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add sickn33/agentic-awesome-skills --skill ai-agent-evaluation-benchmarking -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install sickn33/agentic-awesome-skills ai-agent-evaluation-benchmarking --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/sickn33/agentic-awesome-skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/ai-agent-evaluation-benchmarking .gemini/skills/ai-agent-evaluation-benchmarking && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "ai-agent-evaluation-benchmarking" agent skill from https://github.com/sickn33/agentic-awesome-skills/tree/main/skills/ai-agent-evaluation-benchmarking into .gemini/skills/ai-agent-evaluation-benchmarking/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ai-agent-evaluation-benchmarking", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install sickn33/agentic-awesome-skills ai-agent-evaluation-benchmarkingInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add sickn33/agentic-awesome-skills --skill ai-agent-evaluation-benchmarking -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/sickn33/agentic-awesome-skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/ai-agent-evaluation-benchmarking .github/skills/ai-agent-evaluation-benchmarking && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "ai-agent-evaluation-benchmarking" agent skill from https://github.com/sickn33/agentic-awesome-skills/tree/main/skills/ai-agent-evaluation-benchmarking into .github/skills/ai-agent-evaluation-benchmarking/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ai-agent-evaluation-benchmarking", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add sickn33/agentic-awesome-skills --skill ai-agent-evaluation-benchmarking -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install sickn33/agentic-awesome-skills ai-agent-evaluation-benchmarking --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/sickn33/agentic-awesome-skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/ai-agent-evaluation-benchmarking .opencode/skills/ai-agent-evaluation-benchmarking && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "ai-agent-evaluation-benchmarking" agent skill from https://github.com/sickn33/agentic-awesome-skills/tree/main/skills/ai-agent-evaluation-benchmarking into .opencode/skills/ai-agent-evaluation-benchmarking/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ai-agent-evaluation-benchmarking", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
ai-agent-evaluation-benchmarking- GitHub stars
- 47k
- Used in
- 1 other repo
- Token cost
- ~1.3k tokens
- SKILL.md length
- 499 words
- Files
- 1
- Skills in repo
- 1,493
- Repo updated
- First seen
- Licence
- MIT
At a glance
Autonomous AI agent benchmark evaluation register: task completion rates, planning accuracy, tool invocation precision, and cost benchmarks.
- Works in 3 steps: Define the parameters, thresholds, and… → Select appropriate boundary enforcement… → Export standardized artifacts (CSV…
- Tasks that involve Agent evaluation and testing
- SKILL.md covers Overview, When to Use This Skill, How It Works and Field Reference, plus 9 more sections
- Calls claude and gemini
What it does
AI Agent Evaluation Benchmarking is an agent skill from sickn33/agentic-awesome-skills. Autonomous AI agent benchmark evaluation register: task completion rates, planning accuracy, tool invocation precision, and cost benchmarks.
Its SKILL.md is about 1.3k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.
It sits in Agent Workflows, covering Agent evaluation and testing. It works with SQL. The repository describes itself as: AAS Core is the local, agent-first control plane for complete catalog discovery, agent-owned selection, stack validation, and planning, backed by 2,400+ agentic skills. Includes… The licence is MIT.
When your agent uses it
- Tasks that involve Agent evaluation and testing
Example prompts
- “/ai-agent-evaluation-benchmarking”
Workflow steps
3 steps, taken from the first numbered list in SKILL.md.
- Define the parameters, thresholds, and identity bindings required for the target operational register.
- Select appropriate boundary enforcement values from validated enum select sets.
- Export standardized artifacts (CSV table, SQL DDL, JSON Schema) to integrate into validation CI pipelines.
What it can do on your machine
Read from SKILL.md and the folder at commit 680176d. It shows what the files ask for, not the result of running them.
Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
Shell commands in SKILL.md call:
claudegemini
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Context cost
AI Agent Evaluation Benchmarking loads about 1.3k tokens when it runs. Until then it costs about 43 tokens; SKILL.md has 499 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
Safety
Auto-check passed
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.
SKILL.md
The full file from sickn33/agentic-awesome-skills at commit 680176d, republished under its MIT licence (© sickn33). 499 words, ~1,347 tokens.
Download SKILL.mdSave it as
.claude/skills/ai-agent-evaluation-benchmarking/SKILL.md (or your agent's skills folder).- name
- ai-agent-evaluation-benchmarking
- description
- Autonomous AI agent benchmark evaluation register: task completion rates, planning accuracy, tool invocation precision, and cost benchmarks.
- category
- engineering
- risk
- safe
- source
- self
- source_type
- self
- date_added
- 2026-10-01
- author
- Ranjeet2063
- tags
- ai, agents, evaluation, benchmarks, llm, automation, quality
- source_repo
- Ranjeet2063/agentic-awesome-skills
AI Agent Capability Evaluation & Benchmarking
What it is: Standardizes multi-metric capability benchmarking, token cost efficiency, and regression monitoring across autonomous coding agents.
Overview
Provides a standardized, auditable framework and data model for AI Agent Capability Evaluation & Benchmarking operations across distributed engineering and decentralized application systems.
When to Use This Skill
- When formalizing architectural contracts, security invariants, or operational limits for AI Agent Capability Evaluation & Benchmarking.
- When cross-functional review is required between protocol developers, smart contract auditors, and AI engineering agents.
- When generating reproducible CSV, SQL DDL, JSON Schema, and Notion property registers for tracking compliance.
How It Works
- Define the parameters, thresholds, and identity bindings required for the target operational register.
- Select appropriate boundary enforcement values from validated enum select sets.
- Export standardized artifacts (CSV table, SQL DDL, JSON Schema) to integrate into validation CI pipelines.
Field Reference
| # | Field Name | Type | SQL Type | JSON Schema Type | Notion Property Type | Example Value |
|---|---|---|---|---|---|---|
| 1 | Benchmark Run ID | id | SERIAL PRIMARY KEY | integer | Text | BENCH-001 |
| 2 | Evaluated Agent Model | select | VARCHAR(64) | string | Select | Claude 3.7 Sonnet |
| 3 | Benchmark Suite Domain | select | VARCHAR(64) | string | Select | SWE-bench Verified |
| 4 | Tasks Evaluated Count | number | INTEGER | number | Number | 100 |
| 5 | Pass Rate Percentage | number | NUMERIC(5,2) | number | Number | 78.40 |
| 6 | Tool Hallucination Rate % | number | NUMERIC(5,2) | number | Number | 0.60 |
| 7 | Average Tokens Per Task | number | INTEGER | number | Number | 42500 |
| 8 | Cost Per Solved Task USD | currency | NUMERIC(8,4) | number | Number | 0.3420 |
| 9 | Regression Verdict | select | VARCHAR(32) | string | Select | Superior |
| 10 | Evaluation Lead | text | VARCHAR(64) | string | Text | Ranjeet2063 |
| 11 | Benchmark Execution Date | date | DATE | string, format: date | Date | 2026-10-01 |
Select Options
Evaluated Agent Model
Claude 3.7 Sonnet | Claude 3.5 Sonnet | GPT-4o | Gemini 2.0 Flash | DeepSeek V3Benchmark Suite Domain
SWE-bench Verified | WebArena | AgentBench | HumanEval-Rust | Web3AuditBenchRegression Verdict
Superior | Parity Baseline | Regression FailureRelations
Audit Reference-> links to the formal review documentation or test repository.Target Architecture-> links to the deployed contract or autonomous agent runtime component.
Show full SKILL.md (204 more words)Show less
Examples
Prompt
How do I configure and track AI Agent Capability Evaluation & Benchmarking for our production environment?Recommended Next Step
Generate the unified field schema, SQL DDL migration, and JSON validation schema to register into your system catalog.
Workflow: Define criteria -> Run automated verification -> Record baseline -> Monitor invariants.
Best Practices
- Enforce strict typing on numerical bounds and currency amounts; avoid unstructured free-text fields for critical states.
- Re-run validation test suites on every state-altering commit or parameter change.
- Keep example data synthetic and isolated from production cryptographic keys or private endpoints.
Limitations
- Provides architectural specifications, data models, and verification schemas; does not execute direct transaction signing without authorized external tooling.
- Requires network connectivity and valid RPC credentials when querying on-chain states.
Security & Safety Notes
- All parameters declare
risk: safe. No unauthorized state modification or privileged credential access is performed. - Use synthetic dummy keys and mock addresses in test suites and local verification scripts.
Common Pitfalls
- Problem: Mismatched decimal precision between contract runtime and database register. Solution: Always verify decimals using the explicit field mapping in this reference.
- Problem: Missing authorization checks prior to state update. Solution: Cross-validate against the Security Audit register before deployment.
Related Skills
- @ai-agent-tool-routing - covers tool schema registration and retry policy.
- @ai-prompt-regression-testing - covers prompt regression baselines and drift.
- @ai-code-generation-guardrails - covers static guardrails for generated code.
Reusable Prompt
I want to establish a verified AI Agent Capability Evaluation & Benchmarking register for our production protocol.
Guide me through the required field parameters and output the corresponding SQL DDL and JSON Schema.© sickn33, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Files
Just SKILL.md in skills/ai-agent-evaluation-benchmarking of sickn33/agentic-awesome-skills.
Open the folder on GitHubat commit 680176d
Used in 1 other repository
We found 5 copies of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in sickn33/agentic-awesome-skills, which our catalogue first saw on October 7, 2026.
Compare with similar skills
AI Agent Evaluation Benchmarking next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| AI Agent Evaluation Benchmarking this skillsickn33/agentic-awesome-skills | 47k | 1 repos | ~1.3k | Automated safety check: Pass | MIT | |
| MCP Server Builderanthropics/skills | 180k | 63 repos | ~2.3k | Automated safety check: Pass | Apache-2.0 | |
| Diagnosing Superpowers Sessionsobra/superpowers | 297k | 3 repos | ~1.7k | Automated safety check: Pass | MIT | |
| Darwin Skill Optimizeralchaincyf/darwin-skill | 6.2k | 1 repos | ~4.7k | Automated safety check: Pass | MIT | |
| Skill Release Gaterohitg00/ai-engineering-from-scratch | 66k | — | ~1k | Automated safety check: Pass | MIT | |
| CodeGraph Agent Evalcolbymchenry/codegraph | 74k | — | ~950 | Automated safety check: Pass | MIT |
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Categories
Questions about AI Agent Evaluation Benchmarking
What does AI Agent Evaluation Benchmarking do?
Autonomous AI agent benchmark evaluation register: task completion rates, planning accuracy, tool invocation precision, and cost benchmarks. AI Agent Evaluation Benchmarking is an agent skill from sickn33/agentic-awesome-skills. Autonomous AI agent benchmark evaluation register: task completion rates, planning accuracy, tool invocation precision, and cost benchmarks.
When should I use AI Agent Evaluation Benchmarking?
AI Agent Evaluation Benchmarking fits situations like: tasks that involve Agent evaluation and testing.
How do I install AI Agent Evaluation Benchmarking in Claude Code?
Run `npx skills add sickn33/agentic-awesome-skills --skill ai-agent-evaluation-benchmarking -a claude-code`. Or copy the skill folder (skills/ai-agent-evaluation-benchmarking in sickn33/agentic-awesome-skills) into .claude/skills/ai-agent-evaluation-benchmarking in your project. Claude Code loads it when a task matches its description.
How do I install AI Agent Evaluation Benchmarking in Codex?
Run `npx skills add sickn33/agentic-awesome-skills --skill ai-agent-evaluation-benchmarking -a codex`. Or copy the skill folder (skills/ai-agent-evaluation-benchmarking in sickn33/agentic-awesome-skills) into .agents/skills/ai-agent-evaluation-benchmarking in your project. Codex loads it when a task matches its description.
Can I use AI Agent Evaluation Benchmarking in Cursor, Gemini CLI or GitHub Copilot?
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add sickn33/agentic-awesome-skills --skill ai-agent-evaluation-benchmarking -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/ai-agent-evaluation-benchmarking, .gemini/skills/ai-agent-evaluation-benchmarking, .github/skills/ai-agent-evaluation-benchmarking and .opencode/skills/ai-agent-evaluation-benchmarking in your project.
What does AI Agent Evaluation Benchmarking need to run?
Going by SKILL.md and its folder, AI Agent Evaluation Benchmarking needs the command-line tools its instructions call (claude and gemini).
Does AI Agent Evaluation Benchmarking access the network?
SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.
Is AI Agent Evaluation Benchmarking safe to install?
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.
What licence does AI Agent Evaluation Benchmarking use?
AI Agent Evaluation Benchmarking is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does AI Agent Evaluation Benchmarking use?
About 1.3k tokens (SKILL.md is roughly 5.4k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.
What are the alternatives to AI Agent Evaluation Benchmarking?
Skills that share tags, products or a category with AI Agent Evaluation Benchmarking: MCP Server Builder (anthropics/skills, 180k stars), Diagnosing Superpowers Sessions (obra/superpowers, 297k stars), Darwin Skill Optimizer (alchaincyf/darwin-skill, 6.2k stars) and Skill Release Gate (rohitg00/ai-engineering-from-scratch, 66k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains AI Agent Evaluation Benchmarking?
sickn33 (a GitHub user) maintains it in sickn33/agentic-awesome-skills, which has 47,379 GitHub stars. The repository holds 1,493 skills in this directory. The repository was last updated on October 9, 2026.
Source: sickn33/agentic-awesome-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.