Claude Code Templates is a command-line tool and catalogue for configuring Anthropic’s Claude Code with agents, commands, settings, hooks, integrations, skills, and project templates. Developers use it to browse and install reusable components for their coding workflows. The catalogue includes many of these Claude Code components.
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 davila7/claude-code-templates --skill emerging-techniques-model-merginggit clone --depth 1 https://github.com/davila7/claude-code-templatesWrote 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/davila7/claude-code-templates/emerging-techniques-model-merging)<a href="https://agentmods.dev/skills/davila7/claude-code-templates/emerging-techniques-model-merging"><img src="https://agentmods.dev/badge/skills/davila7/claude-code-templates/emerging-techniques-model-merging.svg" alt="Measured on agentmods" height="20"></a>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 | $0.00073 | $0.03685 |
| Opus 5 | $0.00036 | $0.01843 |
| Sonnet 5 | $0.00015 | $0.00737 |
| Haiku 4.5 | $0.00007 | $0.00368 |
Grade A, and why
model-merging 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 3d 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.
Copies of this mod
2 near-identical copies found in the catalogue:
- model-merging — 100% identical, 0 lines differ
- model-merging — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 540 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Model Merging: Combining Pre-trained Models
When to Use This Skill
Use Model Merging when you need to:
- Combine capabilities from multiple fine-tuned models without retraining
- Create specialized models by blending domain-specific expertise (math + coding + chat)
- Improve performance beyond single models (often +5-10% on benchmarks)
- Reduce training costs - no GPUs needed, merges run on CPU
- Experiment rapidly - create new model variants in minutes, not days
- Preserve multiple skills - merge without catastrophic forgetting
Success Stories: Marcoro14-7B-slerp (best on Open LLM Leaderboard 02/2024), many top HuggingFace models use merging
Tools: mergekit (Arcee AI), LazyMergekit, Model Soup
Installation
# Install mergekit
git clone https://github.com/arcee-ai/mergekit.git
cd mergekit
pip install -e .
# Or via pip
pip install mergekit
# Optional: Transformer library
pip install transformers torch
Quick Start
Simple Linear Merge
# config.yml - Merge two models with equal weights
merge_method: linear
models:
- model: mistralai/Mistral-7B-v0.1
parameters:
weight: 0.5
- model: teknium/OpenHermes-2.5-Mistral-7B
parameters:
weight: 0.5
dtype: bfloat16
# Run merge
mergekit-yaml config.yml ./merged-model --cuda
# Use merged model
python -m transformers.models.auto --model_name_or_path ./merged-model
SLERP Merge (Best for 2 Models)
# config.yml - Spherical interpolation
merge_method: slerp
slices:
- sources:
- model: mistralai/Mistral-7B-v0.1
layer_range: [0, 32]
- model: teknium/OpenHermes-2.5-Mistral-7B
layer_range: [0, 32]
parameters:
t: 0.5 # Interpolation factor (0=model1, 1=model2)
dtype: bfloat16
Core Concepts
1. Merge Methods
Linear (Model Soup)
- Simple weighted average of parameters
- Fast, works well for similar models
- Can merge 2+ models
merged_weights = w1 * model1_weights + w2 * model2_weights + w3 * model3_weights
# where w1 + w2 + w3 = 1
What ships with it
3 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.
- 3d ago First seen · 540 lines · 73 tokens per session scan A 39bcead42b69
model-merging is a skill published in the GitHub repository davila7/claude-code-templates (30,545 stars, last pushed today), licensed MIT. It adds 73 tokens to every session and 3,685 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 2026-09-03.
Other skills, from other repositories
model-merging
Merge multiple fine-tuned models using mergekit to combine capabilities without retraining. Use when creating specialized models by blending domain-specific expertise (math + coding + chat), improving performance beyond single models, or experimenting rapidly with model variants. Covers SLERP, TIES-Merging, DARE, Task…
model-merging
Merge multiple fine-tuned models using mergekit to combine capabilities without retraining. Use when creating specialized models by blending domain-specific expertise (math + coding + chat), improving performance beyond single models, or experimenting rapidly with model variants. Covers SLERP, TIES-Merging, DARE, Task…
model-merging
Merge multiple fine-tuned models using mergekit to combine capabilities without retraining. Use when creating specialized models by blending domain-specific expertise (math + coding + chat), improving performance beyond single models, or experimenting rapidly with model variants. Covers SLERP, TIES-Merging, DARE, Task…
model-merging
Merge multiple fine-tuned models using mergekit to combine capabilities without retraining. Use when creating specialized models by blending domain-specific expertise (math + coding + chat), improving performance beyond single models, or experimenting rapidly with model variants. Covers SLERP, TIES-Merging, DARE, Task…
model-merging
Merge multiple fine-tuned models using mergekit to combine capabilities without retraining. Use when creating specialized models by blending domain-specific expertise (math + coding + chat), improving performance beyond single models, or experimenting rapidly with model variants. Covers SLERP, TIES-Merging, DARE, Task…
evaluating-llms-harness
Evaluates LLMs across 60+ academic benchmarks (MMLU, HumanEval, GSM8K, TruthfulQA, HellaSwag). Use when benchmarking model quality, comparing models, reporting academic results, or tracking training progress. Industry standard used by EleutherAI, HuggingFace, and major labs. Supports HuggingFace, vLLM, APIs.