Borrowing it
Nothing to install: this file belongs to ChenZiHong-Gavin/llm-tech-report. 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/ChenZiHong-Gavin/llm-tech-report/main/.claude/skills/llm-tech-report-maintenance/SKILL.mdgit clone --depth 1 https://github.com/ChenZiHong-Gavin/llm-tech-reportWrote 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/chenzihong-gavin/llm-tech-report/llm-tech-report-maintenance)<a href="https://agentmods.dev/skills/chenzihong-gavin/llm-tech-report/llm-tech-report-maintenance"><img src="https://agentmods.dev/badge/skills/chenzihong-gavin/llm-tech-report/llm-tech-report-maintenance/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/chenzihong-gavin/llm-tech-report/llm-tech-report-maintenance"><img src="https://agentmods.dev/badge/skills/chenzihong-gavin/llm-tech-report/llm-tech-report-maintenance.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, 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 Rogue Agent · line 101 Skill establishes unauthorized persistence across sessions via cron jobs, startup scripts, or state files. Session persistence allows an attacker to maintain access beyond the current interaction.Fix: Remove any persistence mechanisms (cron jobs, startup scripts, state files). Skills should not maintain state across sessions without explicit user consent.
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 | $0.00084 | $0.01489 |
| Opus 5 | $0.00042 | $0.00745 |
| Sonnet 5 | $0.00017 | $0.00298 |
| Haiku 4.5 | $0.00008 | $0.00149 |
Grade A, and why
llm-tech-report-maintenance 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 11d 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 — 183 lines — stays where its author put it; the contents beside it link to each section on GitHub.
LLM Tech Report Maintenance
For AI coding assistants (Claude Code, Cursor, Copilot, etc.). Load this file as context when you need to add, update, or validate entries in this repo.
1. Repo Overview
llm-tech-report/
├── README.md ← The only data file. All reports live here.
├── CONTRIBUTING.md ← Human-readable contribution guide
├── LICENSE
└── .claude/skills/… ← Skill files (this one + logo-generation)
Core rule: links only, never download PDFs, never read PDF content.
2. README.md Schema
2.1 Company Section
Each company is an ## H2 heading. Inside it sits one Markdown table:
## Company Name
| Date | Model | Type | Link |
|:-----|:------|:-----|:-----|
| YYYY-MM | Model Name | Type | [Display Text](URL) |
2.2 Field Definitions
| Field | Format | Example |
|---|---|---|
| Date | YYYY-MM |
2025-01 |
| Model | Official model name / version | DeepSeek-R1 |
| Type | One of the values below | Paper |
| Link | [Display Text](URL) |
[Technical Report](https://arxiv.org/abs/…) |
2.3 Allowed Type Values
| Type | When to use |
|---|---|
Paper |
Peer-reviewed or arXiv publication |
Technical Report |
Official technical document (not peer-reviewed) |
System Card |
Safety / capability evaluation doc (OpenAI, Anthropic style) |
Model Card |
Model spec / limitations doc (Google, HuggingFace style) |
Blog |
Official company blog post (when no paper exists) |
GitHub |
Official repo (when no paper or blog exists) |
2.4 Link Priority
When multiple sources exist for the same model, prefer (highest → lowest):
- arXiv (
arxiv.org/abs/…) — most stable - Official CDN (
cdn.openai.com,assets.anthropic.com,storage.googleapis.com) - Official blog (
openai.com/blog,qwen.ai/blog) - GitHub repo (
github.com/org/repo) - Web Archive (
web.archive.org) — only when original is dead
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.
- 11d ago First seen · 183 lines · 84 tokens per session scan A fcc032a4d6d0
llm-tech-report-maintenance is a skill published in the GitHub repository ChenZiHong-Gavin/llm-tech-report (37 stars, last pushed 23d ago), licensed MIT. It adds 84 tokens to every session and 1,489 once invoked, about $0.0004 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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