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/zjunlp/mechanist/transcodernpx skills add zjunlp/Mechanist --skill transcodergit clone --depth 1 https://github.com/zjunlp/MechanistWhat 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 | $0.00050 | $0.05378 |
| Opus 5 | $0.00025 | $0.02689 |
| Sonnet 5 | $0.00010 | $0.01076 |
| Haiku 4.5 | $0.00005 | $0.00538 |
Grade A, and why
transcoder-circuits 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 — 673 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Transcoder Circuits: Reverse-Engineering LLM Circuits with Transcoders
When to Use
Activate this skill when:
- Reverse-engineering circuits inside transformer language models (GPT-2, Pythia, etc.)
- Training transcoders to decompose MLP sublayers into sparse linear combinations of features
- Analyzing interpretable features in LLMs using sparse autoencoders or transcoders
- Building feature dashboards or activation visualizations
- Performing mechanistic interpretability research on neural networks
- Comparing SAE vs. transcoder feature interpretability
- Running circuit analysis, replacement contexts, or activation patching
Keywords: transcoder, SAE, sparse autoencoder, mechanistic interpretability, LLM circuits, MLP features, GPT-2, Pythia, feature dashboard, circuit analysis, activation patching
Quick Reference
- Repository: https://github.com/jacobdunefsky/transcoder_circuits
- Transcoder Weights (HuggingFace): https://huggingface.co/pchlenski/gpt2-transcoders
- SAELens (upstream SAE code): https://github.com/jbloomAus/SAELens
- Walkthrough Notebook:
walkthrough.ipynb
Installation / Setup
Prerequisites
- Python 3.8+
- CUDA-capable GPU recommended
Quick Setup (from README)
bash setup.sh
This script installs dependencies and downloads transcoder weights from HuggingFace (pchlenski/gpt2-transcoders).
Manual Installation
pip install -r requirements.txt
Requirements (from requirements.txt)
Key dependencies include:
transformer_lens— for loading and hooking into transformer modelstorch— PyTorcheinopsdatasetshuggingface_hubwandb(optional, for training logging)
Core Features
- Transcoder Training: Train transcoders on LLM MLP sublayers to decompose activations into sparse interpretable features (
sae_training/) - Circuit Analysis: Reverse-engineer fine-grained feature circuits within a model (
transcoder_circuits/circuit_analysis.py) - Feature Dashboards: Generate dashboards for exploring transcoder and SAE features (
transcoder_circuits/feature_dashboards.py) - Replacement Context: Swap MLP sublayers with transcoder reconstructions during inference (
transcoder_circuits/replacement_ctx.py) - Activations Store: Stream tokens and generate/store activations during training (
sae_training/activations_store.py) - Geometric Median: Utility for computing geometric median for initialization (
sae_training/geom_median/) - SAE/Transcoder Comparison: Evaluation notebooks comparing SAEs and transcoders on Pythia-410M
What ships with it
4 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 · 673 lines · 50 tokens per session scan A 8fa0c4780966
transcoder-circuits is a skill published in the GitHub repository zjunlp/Mechanist (51 stars, last pushed 6d ago), licensed MIT. It adds 50 tokens to every session and 5,378 once invoked, about $0.0003 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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