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Claude skills for Synalinks OSS
| Date | Stars |
|---|---|
| 2026-07-31 | 905 |
| 2026-08-06 | 905 |
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# Claude Skills for Synalinks --- This repository contains skills to use with Claude Code. ## What Are Claude Skills? Claude Skills are customizable workflows that teach Claude how to perform specific tasks according to your unique requirements. Skills enable Claude to execute tasks in a repeatable, standardized manner across all Claude platforms. ## What is the goal of Synalinks skills? The goal of Synalinks skills is to teach Claude to use the Synalinks framework correctly. Synalinks is Keras-inspired, so without guidance LMs tend to mix Keras / LangChain / DSPy syntax — producing plausible-looking but broken code. These skills constrain Claude to idiomatic Synalinks usage. ## The `synalinks` skill This repo ships a **single skill**, `synalinks`, that covers the whole framework. Claude auto-activates it based on its `SKILL.md` `description`. The `SKILL.md` is a scannable overview, organized into one section per slice of the framework, each pointing to a runnable script (and its captured run log) and a deep-dive reference doc: | Section | What it covers | |---------|----------------| | **Core** | DataModel, Field, Input, JSON operators (`+ & \| ^ ~`), `synalinks.ops`, configuration, LanguageModel/EmbeddingModel basics | | **Programs** | Program class, four building APIs (Functional / Sequential / Subclassing / Mixed), multi-input/output graphs, build/call lifecycle, save/load, `summary`, `get_module`, `plot_program`, custom serialization | | **Modules** | Generator, ChainOfThought, SelfCritique, Identity, PythonSynthesis, SequentialPlanSynthesis, custom modules via subclassing | | **Control flow** | Decision, Branch, parallel branches, self-consistency, XOR input/output guards, And/Or modules, branch merging | | **Agents** | FunctionCallingAgent, RLM, DeepAgent, Tool definitions, MCP integration (MultiServerMCPClient), subagents, trajectories | | **Knowledge** | KnowledgeBase (DuckDB), EmbedKnowledge, UpdateKnowledge, RetrieveKnowledge, RAG/KAG, hybrid search, Entity/Relation graphs | | **Training** | `compile()` / `fit()` / `evaluate()` / `predict()`, callbacks, ProgramCheckpoint, training workflow | | **Rewards** | ExactMatch, CosineSimilarity, LMAsJudge, ProgramAsJudge, F1Score, custom rewards/metrics, masking | | **Optimizers** | RandomFewShot, OMEGA, Dominated Novelty Search, mutation/crossover, quality-diversity tuning | | **Providers** | Provider prefixes (openai, anthropic, groq, openrouter, cohere, deepseek, together_ai, bedrock, doubleword, hosted_vllm, ...), local OpenAI-compatible servers (LMStudio/vLLM/ollama), OpenAI-compatible setups (e.g. OpenRouter embeddings via `api_base`) | | **Datasets** | Built-in datasets (gsm8k, hotpotqa, arcagi), custom iterable datasets, visualization (`plot_program`, `plot_history`, `plot_metrics_*`) | The skill folder contains: - `SKILL.md` — frontmatter + scannable usage guide for the whole framework - `scripts/` — runnable example scripts (each docstring states its `Usage:` and `Run log:`) - `references/` — deep-dive reference docs plus the captured run logs (`*.log`) ## Install The recommended way to install this skill is the official [`skills`](https://skills.sh) CLI (`npx skills`). It clones this repo, discovers the `synalinks` skill, and wires it into your agent's skills directory — no manual cloning, copying, or zipping required. It needs Node.js (the `npx` that ships with it); nothing else. ### Quick start (Claude Code) `synalinks` is a skill for the **synalinks Python library**, so the natural place to install it is **the project where you use synalinks** — that way the agent gets idiomatic-synalinks guidance exactly where you write synalinks code. Project scope is also the CLI default, so just run this from your project root: ```shell npx skills add SynaLinks/synalinks-skills --skill synalinks ``` This installs into the current project (Claude Code reads it from `<repo>/.claude/skills/`). Commit the generated `skills-lock.json` (see [Updating](#updating))
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:c47642306f65c7ae, topic:large-language-models