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/ingest-source/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/ingest-source)<a href="https://agentmods.dev/skills/vlasenkoalexey/tpu_performance_autoresearch_wiki/ingest-source"><img src="https://agentmods.dev/badge/skills/vlasenkoalexey/tpu_performance_autoresearch_wiki/ingest-source/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/vlasenkoalexey/tpu_performance_autoresearch_wiki/ingest-source"><img src="https://agentmods.dev/badge/skills/vlasenkoalexey/tpu_performance_autoresearch_wiki/ingest-source.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 2 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 210 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 211 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 | $0.00173 | $0.02600 |
| Opus 5 | $0.00086 | $0.01300 |
| Sonnet 5 | $0.00035 | $0.00520 |
| Haiku 4.5 | $0.00017 | $0.00260 |
Grade A, and why
ingest-source 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 12d 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 — 212 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/ingest-source — ingest a paper/article/talk/docs into the wiki
Encodes SCHEMA's INGEST-SOURCE operation. The discipline matters: ingest is the front door for the wiki's hypothesis-generation pipeline, and getting emphasis wrong (or skipping connection updates) propagates downstream into bad hypothesis proposals.
When to invoke
- User says "ingest this paper / blog post / talk / docs page" and provides a URL or
raw/sources/path. - A paper or talk just landed that touches TPU perf-relevant topics (Pallas kernels, sharding partitioners, FP8 training, attention kernels, scheduler/compiler passes, etc.).
- An existing source needs re-ingestion because a meaningful update was published.
- NOT for codebases — those go through INGEST-CODEBASE (separate skill, TBD).
- NOT for per-experiment narrative — that's
wiki/experiments/and a different operations contract.
Cardinal rules
- Discuss before writing. The human owns emphasis decisions. Do NOT write the source page until you've surfaced the key claims and confirmed which ones matter.
- Update connections. Every concept, model, or codebase that the source touches gets its
## Sourcesupdated and (where appropriate) prose extended. Skipping this step is the canonical failure — orphan source pages with no incoming links. - Generate hypothesis candidates. If the source suggests optimizations applicable to an active model, file them as
wiki/hypotheses/<slug>.mdwithorigin: <source-slug>. Add to the model page's ranked list. - Update
index.mdandlog.md. Mandatory per SCHEMA's operations contract. - Never modify
raw/. The source file is immutable. If the URL was given, save toraw/sources/<year>-<slug>.<ext>first.
Step 1 — Acquire the source
The user supplies one of:
- A
raw/sources/<year>-<slug>.<ext>path (already ingested locally) - A URL (fetch + save)
- A direct file paste
If URL: fetch and save to raw/sources/<year>-<slug>.<ext>. Choose <slug> to be short, lowercase, hyphen-separated, descriptive. Choose <year> from the source's publication date.
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.
- 12d ago First seen · 212 lines · 173 tokens per session scan A 82f9eb00e0d4
ingest-source is a skill published in the GitHub repository vlasenkoalexey/tpu_performance_autoresearch_wiki (55 stars, last pushed 8d ago), licensed MIT. It adds 173 tokens to every session and 2,600 once invoked, about $0.0009 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
customer_support_agent
You have access to Leeroopedia, a curated ML/AI knowledge base, via MCP tools. These are real MCP tools registered in your environment -- call them directly like any other tool. They contain framework-specific docs, code examples, API references, and best practices.
ml_inference_optimization
This document describes the Leeroopedia MCP tools available during the with-KB benchmark run. It is a standalone reference and is not fed to the agents automatically.
leeroopedia-mcp
Use Leeroopedia MCP to fetch grounded ML/AI best practices, build and review ML plans, debug failures, verify code/math correctness, and expand KB citations via getpage.
self_evolve_rag
This document describes how to use the Leeroopedia MCP tools for the Self-Evolving RAG task. It is kept as a reference and is NOT included in the agent prompt.
llm_post_training
You have access to the Leeroopedia MCP tools. Use them throughout this pipeline to make informed decisions. Specifically.
trulens-evaluation-setup
Configure feedback functions and selectors for TruLens evaluations.