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 agentmods add skills/profsynapse/synaptic-tuner/evaluationnpx skills add ProfSynapse/Synaptic-Tuner --skill evaluationgit clone --depth 1 https://github.com/ProfSynapse/Synaptic-TunerWhat 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 | $0.00079 | $0.01667 |
| Opus 5 | $0.00039 | $0.00834 |
| Sonnet 5 | $0.00016 | $0.00333 |
| Haiku 4.5 | $0.00008 | $0.00167 |
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 2d 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 — 137 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 |
Status System
| Status | Meaning | When |
|---|---|---|
| PASS | Configured checks passed | correct assertions passed, and optional environment/judge checks passed |
| FAIL | Configured checks failed or request errored | No correct.any path matched, required environment checks failed, judge failed, or backend errored |
Schema/structural validation may still be reported for debugging, but it is not the source of task correctness. Correctness belongs in scenario YAML.
Key Directories
Evaluator/- Core evaluation codeEvaluator/config/scenarios/- YAML test scenariosEvaluator/config/tool_schema.yaml- Current CLI wrapper/tool schema metadataEvaluator/config/rubrics/- LLM-as-judge rubricsEvaluator/results/- Evaluation output JSON and MarkdownEvaluator/config/scenarios/embedding_retrieval_smoke.yaml- Corpus-level retrieval scenario (theretrievalverifier)
What ships with it
5 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.
- 2d ago First seen · 137 lines · 79 tokens per session scan A e3f7fab2f76e
evaluation is a skill published in the GitHub repository ProfSynapse/Synaptic-Tuner (27 stars, last pushed 2d ago), licensed MIT. It adds 79 tokens to every session and 1,667 once invoked, about $0.0004 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 2026-08-30.
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