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 UCSC-VLAA/VisualClaw --skill semantic-precision-matchinggit clone --depth 1 https://github.com/UCSC-VLAA/VisualClawWrote 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/ucsc-vlaa/visualclaw/semantic-precision-matching)<a href="https://agentmods.dev/skills/ucsc-vlaa/visualclaw/semantic-precision-matching"><img src="https://agentmods.dev/badge/skills/ucsc-vlaa/visualclaw/semantic-precision-matching.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 5.1 | $0.00035 | $0.00273 |
| Opus 5 | $0.00017 | $0.00137 |
| Sonnet 5 | $0.00007 | $0.00055 |
| Haiku 4.5 | $0.00003 | $0.00027 |
Grade A, and why
semantic-precision-matching 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.
What it actually says
Match Precise Semantic Details to Video Content
- Identify key semantic distinctions between similar options (e.g., "grind and polish" vs. "sharpen" vs. "cut").
- For each option, extract the core claim: What specific process or action is being described?
- Cross-reference each claim against what the video actually demonstrates—look for exact verb and object matches.
- Pay attention to frequency markers: "repeatedly," "once," "consistently" must align with observed behavior.
- Reject options where the semantic content (the actual process described) does not match the video, even if the option is well-constructed.
Example: Options describe: (A) grind and polish, (C) sharpen, (E) cut Video shows: grinding and polishing motions Correct: (A) — exact semantic match Incorrect: (C) or (E) — different processes not observed
Anti-pattern: Selecting an option because it uses similar keywords without verifying the core semantic claim (the actual process or action) is demonstrated in the video.
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 · 22 lines · 35 tokens per session scan A f22ea8d9eb6d
semantic-precision-matching is a skill published in the GitHub repository UCSC-VLAA/VisualClaw (55 stars, last pushed 2mo ago), licensed MIT. It adds 35 tokens to every session and 273 once invoked, about $0.0002 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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