OpenBiliClaw is a local, open-source AI agent that learns a person's interests and discovers content across multiple social platforms and the open web. It is for people who want personalized content recommendations with their usage data kept on their own machine.
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 skills add whiteguo233/OpenBiliClaw --skill comment_analysisgit clone --depth 1 https://github.com/whiteguo233/OpenBiliClawWrote 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/whiteguo233/openbiliclaw/comment_analysis)<a href="https://agentmods.dev/skills/whiteguo233/openbiliclaw/comment_analysis"><img src="https://agentmods.dev/badge/skills/whiteguo233/openbiliclaw/comment_analysis/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/whiteguo233/openbiliclaw/comment_analysis"><img src="https://agentmods.dev/badge/skills/whiteguo233/openbiliclaw/comment_analysis.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 | $0.00016 | $0.00199 |
| Opus 5.5 | $0.00006 | $0.00080 |
| Sonnet 5.5 | $0.00003 | $0.00040 |
| Haiku 4.5 | $0.00002 | $0.00020 |
Grade A, and why
comment_analysis 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 1mo 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
Comment Analysis Skill
Mine video comment sections for content recommendations and quality signals.
When to Use
- As part of the content discovery cycle
- To evaluate the quality and reception of discovered content
- To find user-recommended content and UP主 from comments
How It Works
- Fetches comments from a video via API or agent-browser
- Uses LLM to identify:
- Other video/UP主 recommendations mentioned in comments
- Overall content quality sentiment
- Common viewer reactions and highlights
- Returns structured analysis
Parameters
bvid(str): Video BV ID to analyze comments formax_comments(int, optional): Maximum comments to analyze (default: 100)
Output
- Recommended content/UP主 found in comments
- Quality sentiment score
- Key highlights and reactions
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.
- 1mo ago First seen · 35 lines · 16 tokens per session scan A 310db176498b
comment_analysis is a skill published in the GitHub repository whiteguo233/OpenBiliClaw (3,559 stars, last pushed yesterday), licensed MIT. It adds 16 tokens to every session and 199 once invoked, about $0.0001 per session on Opus 5.5. 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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