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/case-studiesnpx skills add ProfSynapse/Synaptic-Tuner --skill case-studiesgit 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 | $0.00114 | $0.01491 |
| Opus 5 | $0.00057 | $0.00745 |
| Sonnet 5 | $0.00023 | $0.00298 |
| Haiku 4.5 | $0.00011 | $0.00149 |
Grade A, and why
case-studies 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 — 122 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Case Studies: Implementing the Training Pipeline
Three end-to-end worked examples showing how to take a capability from concept to trained model.
Why Case Studies?
The other skills teach you how to use individual tools:
- synthetic-data-generation — how to run SynthChat
- fine-tuning — how to run trainers
- evaluation — how to run evals
- upload-deployment — how to ship models
This skill shows you how they all connect — the decisions, the iteration, and the order of operations that turn an idea into a trained capability.
The Three Case Studies
| Case Study | What It Teaches | Reference |
|---|---|---|
| Tool Calling | Structured output training — teaching a model to call APIs with correct syntax, context objects, and parameters | reference/tool-calling-pipeline.md |
| Essay Style | Creative output training — teaching a model to transform messy brainstorms into structured outlines with voice and personality | reference/essay-style-pipeline.md |
| Agentic Search | RAG agent training — teaching a model to search a corpus, select relevant documents, and answer grounded in sources | reference/agentic-search-pipeline.md |
The Universal Pipeline
All three case studies follow the same high-level pipeline, but diverge in dataset design and validation:
┌──────────────────────────────────────────────────────────┐
│ 1. DEFINE THE CAPABILITY │
│ What should the model do? What does good look like? │
│ → Rubrics, schemas, specifications │
└────────────────────┬─────────────────────────────────────┘
│
▼
┌──────────────────────────────────────────────────────────┐
│ 2. CREATE TRAINING DATA │
│ How do we generate enough high-quality examples? │
│ → SynthChat scenarios, handcrafted seeds, self-play │
└────────────────────┬─────────────────────────────────────┘
│
▼
┌──────────────────────────────────────────────────────────┐
│ 3. VALIDATE & IMPROVE │
│ How do we ensure quality before training? │
│ → Schema validation, rubric scoring, manual review │
└────────────────────┬─────────────────────────────────────┘
│
▼
┌──────────────────────────────────────────────────────────┐
│ 4. TRAIN │
│ SFT first (learn the format), then KTO (learn │
│ preferences), optionally GRPO (optimize rewards) │
│ → Trainers with YAML config │
└────────────────────┬─────────────────────────────────────┘
│
▼
┌──────────────────────────────────────────────────────────┐
│ 5. EVALUATE │
│ Does the model do what we trained it to do? │
│ → Evaluator with YAML scenarios │
└────────────────────┬─────────────────────────────────────┘
│
▼
┌──────────────────────────────────────────────────────────┐
│ 6. ITERATE │
│ What failed? Generate more data targeting weaknesses. │
│ → Failure analysis → targeted generation → retrain │
└──────────────────────────────────────────────────────────┘
What ships with it
7 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 · 122 lines · 114 tokens per session scan A 378235e6d6f1
case-studies is a skill published in the GitHub repository ProfSynapse/Synaptic-Tuner (27 stars, last pushed 2d ago), licensed MIT. It adds 114 tokens to every session and 1,491 once invoked, about $0.0006 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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