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 instructions/goodfire-ai/param-decomp/claude-mdgit clone --depth 1 https://github.com/goodfire-ai/param-decompWrote 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/instructions/goodfire-ai/param-decomp/claude-md)<a href="https://agentmods.dev/instructions/goodfire-ai/param-decomp/claude-md"><img src="https://agentmods.dev/badge/instructions/goodfire-ai/param-decomp/claude-md.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 | $0.00504 | $0.00504 |
| Opus 5 | $0.00252 | $0.00252 |
| Sonnet 5 | $0.00101 | $0.00101 |
| Haiku 4.5 | $0.00050 | $0.00050 |
Grade A, and why
param-decomp CLAUDE.md 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.
How it starts
The opening of the file, as written. The whole thing — 35 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Repository guidance
Before planning, running, or interpreting a decomposition experiment, read both guides:
docs/handbook.md— the science, evidence standards, failure modes, and interpretation limits.docs/skill.md— the repository-specific recipe for targets, objectives, sweeps, validation, and analysis.
For other work, start with the smallest relevant source of truth:
README.md— installation, runnable entry points, datasets, packaging, and development commands.CONFIGS.md— which configurations belong in the repository and how they stay portable.param_decomp/core/SPEC.md— the trainer's normative numerical contract.- The nearest module-level
CLAUDE.md— local architecture and interfaces. These exist undercore,experiments,experiments/lm/pretrain, andclustering;targetscarries aREADME.md.
Repository-wide constraints
param_decomp/must not know where it runs: no scheduler, submission, code-shipping, cluster path, mount, partition, or team namespace. Paths are explicit required inputs, and configs identify external resources by portable names. Deployment adapters may depend on the library; the library may never depend on them.- Current training is JAX. The retired Torch implementation is only a semantic oracle at git tag
torch-oracle;nano_param_decomp/is a standalone Torch reference and is not imported by either package. - Keep the functional core pure and put I/O at entry points. Encode invariants in types when possible and assert the rest. Fail closed rather than adding fallbacks, compatibility shims, or degraded modes.
- Import public names from the modules that define them; package-level re-exports are exceptional. Update the nearest guide or specification when changing a documented structure or interface.
Development
Use the root uv project; make install-dev installs the library and development tools into one environment. Run commands with uv run or activate .venv.
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 · 35 lines · 504 tokens per session scan A bdce27798360
param-decomp CLAUDE.md is an instructions file published in the GitHub repository goodfire-ai/param-decomp (140 stars, last pushed 10d ago), licensed MIT. It adds 504 tokens to every session, about $0.0025 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 instructions, from other repositories
OmicsClaw AGENTS.md
Instructions for TianGzlab/OmicsClaw, covering agents.md — omicsclaw guide for ai coding agents, repository working contract, project overview, setup and pip install -e .
Score-CAM CLAUDE.md
Instructions for tabayashi0117/Score-CAM, covering claude.md, what this repository is, layout, non-negotiable rules and paper fidelity.
oci-agent CLAUDE.md
Instructions for Netflix-Skunkworks/oci-agent, covering observational causal inference (oci) agent and rules.
ai4science-studio CLAUDE.md
Instructions for AMDResearch/ai4science-studio, covering claude.md, repository purpose, agent entry points (read these first), directory layout and model slug rule.
gpu-ai-skills CLAUDE.md
Instructions for intel/gpu-ai-skills, covering claude.md, what this repository is, repository structure, commands and validation (required before any skill change).
biomedical-agent-kg CLAUDE.md
Claude Code instructions for seandavi/biomedical-agent-kg, covering claude.md, what this is, running, core invariant — fix the generator, never the record and architecture.