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 mgechev/skillgrade --skill skillgrade-setupgit clone --depth 1 https://github.com/mgechev/skillgradeWrote 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/mgechev/skillgrade/skillgrade-setup)<a href="https://agentmods.dev/skills/mgechev/skillgrade/skillgrade-setup"><img src="https://agentmods.dev/badge/skills/mgechev/skillgrade/skillgrade-setup/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/mgechev/skillgrade/skillgrade-setup"><img src="https://agentmods.dev/badge/skills/mgechev/skillgrade/skillgrade-setup.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00058 | $0.01227 |
| Opus 5.5 | $0.00023 | $0.00491 |
| Sonnet 5.5 | $0.00012 | $0.00245 |
| Haiku 4.5 | $0.00006 | $0.00123 |
Grade A, and why
skillgrade-setup 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 7d 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 — 73 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Skillgrade Evaluation Setup
Procedures
Step 1: Install Skillgrade
- Verify Node.js 20+ and Docker are available.
- Run
npm i -g skillgradeto install the CLI globally.
Step 2: Initialize an Eval Configuration
- Navigate to any project directory.
- If the directory contains a
SKILL.mdand an API key is set,skillgrade initgenerates AI-powered tasks and graders tailored to the skill. - Without a
SKILL.mdor API key,skillgrade initcreates a ready-to-edit generic eval template. - Pass
--forceto overwrite an existingeval.yaml.
Step 3: Configure eval.yaml
- Read
references/eval-yaml-spec.mdfor the full configuration schema. - Define tasks under the
tasks:key. Each task requires:name: unique task identifierinstruction: what the agent should accomplish (ORturns:for multi-turn evals)graders: one or more scoring mechanisms (see theskillgrade-gradersskill)
- Optionally add
workspace:(files the agent edits) andcontext:(reference material not directly graded). - Configure
defaults:for agent, provider, trials, timeout, and threshold. - For multi-turn evals, replace
instructionwithturns:— each turn has its owninstructionand optionalgraders. The workspace persists across turns. - For batch evals, add
dataset: path/to/file.jsonl(or.csv) to a task. Each row expands into a separate task with{{key}}template variables interpolated in the instruction.
Step 4: Run Evaluations
- Select an appropriate preset based on the evaluation goal:
--smoke(5 trials): Quick capability check.--reliable(15 trials): Reliable pass rate estimate.--regression(30 trials): High-confidence regression detection.
- Run the evaluation:
skillgrade --smoke. - Run a specific eval by name:
skillgrade --eval=fix-linting. - Run multiple evals:
skillgrade --eval=fix-linting,write-tests. - Run only deterministic graders (skip LLM calls):
skillgrade --grader=deterministic. - Run only LLM rubric graders:
skillgrade --grader=llm_rubric. - Run only human review graders:
skillgrade --grader=human. - The agent is auto-detected from the API key. Override with
--agent=gemini|claude|codex|acp|opencode|command. - For ACP, pass
--acp-command="gemini --acp"or setdefaults.acp.command. - For OpenCode, pass
--opencode-agent=build|plan|exploreor--opencode-model=provider/model. - For a custom agent, pass
--agent=command --command="node mycli.js"or setdefaults.command. The instruction is piped to the command's stdin. - Override the provider with
--provider=docker|local(or any custom-registered provider).
What ships with it
2 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.
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.
- 7d ago Changed · +12 lines · +6 tokens per session 339272bf957c
- 1mo ago First seen · 61 lines · 52 tokens per session scan A 03b5d27752ad
skillgrade-setup is a skill published in the GitHub repository mgechev/skillgrade (726 stars, last pushed 7d ago), licensed MIT. It adds 58 tokens to every session and 1,227 once invoked, about $0.0002 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 2026-08-30.
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