A configuration-based system for testing and comparing AI model responses. It uses YAML files, which are human-readable configuration files, to define prompts and acceptable answers.

Use it to run quick or full evaluations, test prompts, compare models or training runs, filter scenarios by tags, and connect to backends such as vLLM or LM Studio. It also supports HuggingFace integration and dry-run configuration checks.

It replaces one-off manual checks with repeatable scenarios and correctness rules. It can show response results, scores, model comparisons, and optional judgments by another language model.

How ProfSynapse/Synaptic-Tuner configures Claude Code

Reuse

Borrowing it

Nothing to install: this file belongs to ProfSynapse/Synaptic-Tuner. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.

Copy the file

curl -O https://raw.githubusercontent.com/ProfSynapse/Synaptic-Tuner/main/.agents/skills/evaluation/SKILL.md

Clone the repo

git clone --depth 1 https://github.com/ProfSynapse/Synaptic-Tuner

Made for: Claude Code.

Wrote 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.

README.md

[![agentmods](https://agentmods.dev/badge/skills/profsynapse/synaptic-tuner/evaluation/github.svg)](https://agentmods.dev/skills/profsynapse/synaptic-tuner/evaluation)

Your own site

<a href="https://agentmods.dev/skills/profsynapse/synaptic-tuner/evaluation"><img src="https://agentmods.dev/badge/skills/profsynapse/synaptic-tuner/evaluation/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.

Your own site · 80×15

<a href="https://agentmods.dev/skills/profsynapse/synaptic-tuner/evaluation"><img src="https://agentmods.dev/badge/skills/profsynapse/synaptic-tuner/evaluation.svg" alt="Reviewed on agentmods" width="80" height="20"></a>

Per session 79 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.

When invoked 2,739 The whole file, excluding the scripts and references it only reads on demand.

Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits

NVIDIA SkillSpector pass 7 Sept 2026

How audits are shown

Origin original No closer match found in the catalogue.

Token cost

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	$0.00079	$0.02739
Opus 5.5	$0.00032	$0.01096
Sonnet 5.5	$0.00016	$0.00548
Haiku 4.5	$0.00008	$0.00274

Measured 9d ago against content hash c140dd9c5a41, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

evaluation 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 9d 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.

.agents/skills/evaluation/SKILL.md · 210 lines

How it starts

The opening of the file, as written. The whole thing — 210 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Model Evaluation

Config-first evaluation framework for testing model responses against YAML-defined correctness assertions.

The evaluator does not hardcode a specific tool family, manager id, wrapper name, or behavior rule as correctness. Scenarios define the prompt and the acceptable response shape directly under correct.

Quick Reference

Task	Command
Interactive menu	`./run.sh` then Evaluate
Tool CLI eval	`python -m Evaluator.cli --backend vllm --model MODEL --scenario tool_prompts.yaml --host 127.0.0.1 --port 8011`
Full configured eval	`python -m Evaluator.cli --backend lmstudio --model MODEL --preset full`
Quick smoke test	`python -m Evaluator.cli --backend lmstudio --model MODEL --preset quick`
Tag filter	`python -m Evaluator.cli --backend lmstudio --model MODEL --scenario tool_prompts.yaml --tags storageManager`
Dry run config load	`python -m Evaluator.cli --backend lmstudio --model MODEL --scenario tool_prompts.yaml --dry-run`
Eval with environment runtime	`python -m Evaluator.cli --backend lmstudio --model MODEL --scenario tool_prompts.yaml --env-backend local`
Eval with LLM judge	`python -m Evaluator.cli --backend lmstudio --model MODEL --scenario tool_prompts.yaml --judge --judge-rubrics tool_call_quality`
Eval + upload to HF	`python -m Evaluator.cli --backend unsloth --model PATH --upload-to-hf user/model`
Train/eval contamination check	`python tuner.py check-contamination --train-data Datasets/<train>.jsonl [--eval-source <scenario.yaml>] [--json]`
Compare / quant regression gate	`python -m Evaluator.compare --reference f16.json --candidate q4.json --max-pass-rate-drop 3`
Restricted native MLX evaluation	Config-first `examples/training_qualification/native_evaluation.py` and `native_evaluation_cases.py`; generation-only candidate, separately reviewed exact admission required; read `reference/native-mlx.md`

Contamination Check Before Trusting Scores

Read the full file on GitHub · 210 lines

Files

What ships with it

6 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.

Changes

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.

9d ago Changed · +1 lines c140dd9c5a41
23d ago Changed · +51 lines 452b5c91a3aa
1mo ago Changed · +20 lines d1418982e82e
1mo ago Changed · +1 lines 1d4b06548466
2mo ago First seen · 137 lines · 79 tokens per session scan A e3f7fab2f76e

Subscribe to this mod's changes

evaluation is a skill published in the GitHub repository ProfSynapse/Synaptic-Tuner (28 stars, last pushed 3d ago), licensed MIT. It adds 79 tokens to every session and 2,739 once invoked, about $0.0003 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.

Analytics and metrics Testing

evaluation next to 6 skills from other repositories
Mod	Type	Stars	Last push	Licence	Scan	Tokens	Works with
evaluationProfSynapse/Synaptic-Tuner	Skill	28	3d ago	MIT	A	79	Claude Code
imagegenopenai/codex	Skill	133k	yesterday	Apache-2.0	A	113	Codex
playwright-climicrosoft/playwright	Skill	99k	yesterday	Apache-2.0	A	19	Claude Code
local-ai-agentsmicrosoft/ai-agents-for-beginners	Skill	79k	4d ago	MIT	A	200	Claude Code, Codex
systematic-debuggingobra/superpowers	Skill	313k	3d ago	MIT	A	21	Claude Code
next-cache-components-adoptionvercel/next.js	Skill	143k	yesterday	MIT	A	95	Claude Code
next-partial-prefetching-adoptionvercel/next.js	Skill	143k	yesterday	MIT	A	103	Claude Code

Synaptic-Tuner: Skill for Claude Code