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 rusel95/ios-agent-skills --skill benchmarkinggit clone --depth 1 https://github.com/rusel95/ios-agent-skillsWrote 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/rusel95/ios-agent-skills/benchmarking)<a href="https://agentmods.dev/skills/rusel95/ios-agent-skills/benchmarking"><img src="https://agentmods.dev/badge/skills/rusel95/ios-agent-skills/benchmarking/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/rusel95/ios-agent-skills/benchmarking"><img src="https://agentmods.dev/badge/skills/rusel95/ios-agent-skills/benchmarking.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 | $0.00149 | $0.04736 |
| Opus 5 | $0.00075 | $0.02368 |
| Sonnet 5 | $0.00030 | $0.00947 |
| Haiku 4.5 | $0.00015 | $0.00474 |
Grade A, and why
skill-benchmarking 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 8d 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 — 453 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Skill Benchmarking
Strict, agent-agnostic benchmark runner for evals.json skill evaluation. Produces benchmark-<model>.json with pass rates and a discriminating assertion list. Only assertions that actually discriminate between with-skill and without-skill responses are kept; non-discriminating noise is removed via the assertion hygiene process.
This skill works with any AI coding assistant -- Claude Code, Gemini CLI, GitHub Copilot, Cursor, Windsurf, or any agent that can read files and run shell commands.
Quick Start for Non-Claude Agents
If you are using Gemini CLI, GitHub Copilot, Cursor, or another AI coding assistant:
- Read this file (
scripts/benchmarking/SKILL.md) -- it is the complete workflow guide - Follow the phases below in order. Each phase tells you exactly what to do
- Run Python scripts via your terminal or shell tool. All scripts use only the Python standard library (no pip installs needed)
- For grading (Phase 3), you MUST use a separate/fresh context that has NOT read the skill being tested. If your agent supports subagents or separate chat sessions, use that. If not, start a new chat session for grading
- File paths in this guide are relative to the repository root. Adjust if your working directory differs
Key differences from Claude Code usage
| Claude Code feature | Equivalent for other agents |
|---|---|
Explore subagent |
Start a fresh chat session, or use your agent's subprocess/tool-use feature |
Read / Write tools |
Use your agent's file read/write commands, or cat / shell redirects |
Bash tool |
Use your agent's terminal/shell execution capability |
All Python scripts are standalone and require only Python 3.10+. No external dependencies.
Non-Negotiable Invariants
- Grader isolation -- the context/session that generated responses does NOT grade them
- Evidence-only -- assertions pass only when the required content is EXPLICITLY stated in the response; implication, adjacency, and partial coverage all fail
- Blind grading -- the grader does not know whether it is grading a with_skill or without_skill response
- Model-agnostic -- model slug is always supplied by the caller; never infer or hardcode it
What ships with it
15 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- __init__.py 99 B runs code
- aggregate_benchmark.py 15 KB runs code
- aggregate.py 7.8 KB runs code
- eval-viewer/generate_review.py 16 KB runs code
- eval-viewer/viewer.html 44 KB
- grade_responses.py 13 KB runs code
- grade_with_claude.py 7.2 KB runs code
- ingest-grades.py 3.4 KB runs code
- references/analyzer-prompt.md 10 KB
- references/comparator-prompt.md 7.1 KB
- references/grader-prompt.md 5.9 KB
- references/grading-rules.md 3.4 KB
- run_benchmark.md 12 KB
- scaffold.py 5.1 KB runs code
- unpack-outputs.py 4.1 KB runs code
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.
- 8d ago First seen · 453 lines · 149 tokens per session scan A 5c81e0fcf2e9
skill-benchmarking is a skill published in the GitHub repository rusel95/ios-agent-skills (7 stars, last pushed 4mo ago), licensed MIT. It adds 149 tokens to every session and 4,736 once invoked, about $0.0007 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 2026-08-31.
Other skills, from other repositories
pdlc-feature
An automated workflow for taking a new software feature from a description or requirements document through requirements, design, testing, implementation, review, and release preparation. It keeps one feature ID across the documents and requires tests before implementation.
pdlc-quality
A quality gate that runs configured coverage, end-to-end, and lint checks, compares their real results with declared targets, and creates a report for human approval.
pdlc-test-setup
A project setup workflow that finds the programming language, package manager, test framework, and available checking tools, then records verified commands in test-commands.yml. It can also create test folders and connect local hooks.
pdlc-tdd
A test-first development workflow guided by existing design documents. TDD means writing tests before the code they check; PDLC is a staged product-development process with required preparation steps.
pdlc-implement
A guarded coding workflow that implements a feature from its design document and existing tests, then checks the result. It requires tests to be written first, following TDD (test-driven development).
pdlc-api-mock
A command that reads API design documents and creates realistic sample responses and configuration for a mock service. A mock service imitates an API so frontend code can be tested before the real backend is ready.