Borrowing it
Nothing to install: this file belongs to vlasenkoalexey/tpu_performance_autoresearch_wiki. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/vlasenkoalexey/tpu_performance_autoresearch_wiki/main/.claude/skills/edit-model-code/SKILL.mdgit clone --depth 1 https://github.com/vlasenkoalexey/tpu_performance_autoresearch_wikiWrote 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/vlasenkoalexey/tpu_performance_autoresearch_wiki/edit-model-code)<a href="https://agentmods.dev/skills/vlasenkoalexey/tpu_performance_autoresearch_wiki/edit-model-code"><img src="https://agentmods.dev/badge/skills/vlasenkoalexey/tpu_performance_autoresearch_wiki/edit-model-code.svg" alt="Measured on agentmods" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 4 findings, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Agent Snooping · line 8 Skill enumerates or reads other installed skills. Access to other skills' SKILL.md files or the skills directory reveals prompt instructions, capabilities, and secrets that should be invisible to peer skills.Fix: Remove all code or instructions that list or read other skills' files or directories. Skills should operate independently; cross-skill access is a privilege escalation.
- medium Agent Snooping · line 148 Skill enumerates or reads other installed skills. Access to other skills' SKILL.md files or the skills directory reveals prompt instructions, capabilities, and secrets that should be invisible to peer skills.Fix: Remove all code or instructions that list or read other skills' files or directories. Skills should operate independently; cross-skill access is a privilege escalation.
- medium MCP Rug Pull · line 115 Docker image references without a specific tag (:latest is implicit) or digest (@sha256:...) can be silently replaced by a malicious image.Fix: Pin the image: image:tag or image@sha256:abc123
- medium Agent Snooping · line 150 Skill enumerates or reads other installed skills. Access to other skills' SKILL.md files or the skills directory reveals prompt instructions, capabilities, and secrets that should be invisible to peer skills.Fix: Remove all code or instructions that list or read other skills' files or directories. Skills should operate independently; cross-skill access is a privilege escalation.
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 | $0.00146 | $0.02709 |
| Opus 5 | $0.00073 | $0.01354 |
| Sonnet 5 | $0.00029 | $0.00542 |
| Haiku 4.5 | $0.00015 | $0.00271 |
Grade A, and why
edit-model-code 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 8d 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 — 154 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are about to modify model code inside a per-experiment fork. Read these guidelines FIRST and keep them in scope while editing. The goal is one minimal, surgical change that tests exactly one hypothesis.
This skill adapts Andrej Karpathy's LLM-coding guidelines (MIT-licensed) to the autoresearch loop. Where the upstream conflicts with this project's invariants, the autoresearch version wins.
When to invoke this skill
- BEFORE step 4 ("Implement") of program.md's experiment loop — before opening any file in
wiki/experiments/<model>_autoresearch_optimization/<lane>/.repo/<exp-name>/<model-repo>/. - Re-invoke if the edit scope grows mid-session (you start touching files outside the original plan).
- Re-invoke for any cross-cutting refactor you're tempted to do "while you're in there".
NOT for: wiki page writes, infra scripts, the build step, the launch step. Those have their own discipline elsewhere.
The four principles (Karpathy, adapted)
1. Think before coding — and document, don't ask
Surface assumptions in the experiment page, not by interrupting the loop.
The gke-cluster-runner is autonomous; the master loop can't pause to interactively clarify with the user. So the adaptation is:
- State your assumptions in the experiment page's "Hypothesis under test" or "Setup" section before editing.
- If multiple interpretations of the hypothesis exist, pick the smaller-blast-radius one and document why in the experiment page. Don't make both changes.
- If something is genuinely unclear and the wrong choice would burn cluster time, refute the hypothesis cheaply with an inconclusive run and surface the ambiguity in the verdict — that's a valid outcome of the loop.
2. Simplicity first — one mechanism per experiment
Minimum code that tests this one hypothesis. Nothing speculative.
- No features beyond what the hypothesis tests.
- No "while I'm here" cleanups, no abstractions for a single-use code path.
- No error handling for impossible scenarios (the training job will surface real errors).
- If you find yourself writing 200 lines of plumbing to test a 5-line idea, the hypothesis is wrong — refute it cheaply or reformulate.
- One mechanism per experiment. Don't bundle "fix imports + tune block size + add a fallback path". The wiki's signal depends on one variable changing per run.
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.
- 8d ago First seen · 154 lines · 146 tokens per session scan A 576a5ffaeef0
edit-model-code is a skill published in the GitHub repository vlasenkoalexey/tpu_performance_autoresearch_wiki (54 stars, last pushed 4d ago), licensed MIT. It adds 146 tokens to every session and 2,709 once invoked, about $0.0007 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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