Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/zjunlp/Mechanistnpx agentmods add skills/zjunlp/mechanist/intervention-based-edge-searchWrote 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/zjunlp/mechanist/intervention-based-edge-search)<a href="https://agentmods.dev/skills/zjunlp/mechanist/intervention-based-edge-search"><img src="https://agentmods.dev/badge/skills/zjunlp/mechanist/intervention-based-edge-search/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/zjunlp/mechanist/intervention-based-edge-search"><img src="https://agentmods.dev/badge/skills/zjunlp/mechanist/intervention-based-edge-search.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00033 | $0.00367 |
| Opus 5 | $0.00016 | $0.00183 |
| Sonnet 5 | $0.00007 | $0.00073 |
| Haiku 4.5 | $0.00003 | $0.00037 |
Grade A, and why
automatic-circuit-discovery 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 9d 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.
What it actually says
Demo Scripts
scripts/acdc_run_demo.py
#!/usr/bin/env python3
"""
Simple runnable script demonstrating how to run the main ACDC pipeline programmatically.
This script shows how to import and execute the main function in the ACDC library,
which runs the automated circuit discovery for a default configuration.
Requires:
- Python 3.8+ environment with the Automatic-Circuit-Discovery repo installed via Poetry
- System dependencies (graphviz, etc.) installed per instructions
Usage:
python scripts/acdc_run_demo.py
"""
import sys
import os
import argparse
from acdc import main as acdc_main_module
def main():
"""
Runs the main ACDC experiment pipeline, simulating the command line interface call.
Prints progress and handles basic errors.
"""
try:
# The main.py in acdc offers a CLI main function, here we call directly
# It can take command-line like arguments, but defaults should run a demo
# For example: python acdc/main.py --help for CLI instructions
# We run it with no additional args to start the default pipeline/demo
print("Starting ACDC main pipeline demo run...")
sys.argv = ['acdc/main.py'] # Reset argv for main.py
acdc_main_module.main()
print("ACDC main pipeline finished successfully.")
except Exception as e:
print(f"Error running ACDC main pipeline: {e}")
sys.exit(1)
if __name__ == "__main__":
main()
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.
- 9d ago First seen · 58 lines · 33 tokens per session scan A cb2be24ff417
automatic-circuit-discovery is a skill published in the GitHub repository zjunlp/Mechanist (74 stars, last pushed 13d ago), licensed MIT. It adds 33 tokens to every session and 367 once invoked, about $0.0002 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.
Other skills, from other repositories
nanoresearch-writing
Draft a LaTeX research paper from all previous stage outputs.
nanoresearch-experiment
Generate a Python code skeleton from an experiment blueprint.
nanoresearch-ideation
Search academic literature and generate research hypotheses.
nnsight-remote-interpretability
Provides guidance for interpreting and manipulating neural network internals using nnsight with optional NDIF remote execution. Use when needing to run interpretability experiments on massive models (70B+) without local GPU resources, or when working with any PyTorch architecture.
sparse-autoencoder-training
Provides guidance for training and analyzing Sparse Autoencoders (SAEs) using SAELens to decompose neural network activations into interpretable features. Use when discovering interpretable features, analyzing superposition, or studying monosemantic representations in language models.
transformer-lens-interpretability
Provides guidance for mechanistic interpretability research using TransformerLens to inspect and manipulate transformer internals via HookPoints and activation caching. Use when reverse-engineering model algorithms, studying attention patterns, or performing activation patching experiments.