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 video-action-purpose-alignmentgit 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/video-action-purpose-alignment)<a href="https://agentmods.dev/skills/ucsc-vlaa/visualclaw/video-action-purpose-alignment"><img src="https://agentmods.dev/badge/skills/ucsc-vlaa/visualclaw/video-action-purpose-alignment.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 | $0.00040 | $0.00277 |
| Opus 5 | $0.00020 | $0.00138 |
| Sonnet 5 | $0.00008 | $0.00055 |
| Haiku 4.5 | $0.00004 | $0.00028 |
Grade A, and why
video-action-purpose-alignment 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
Align Answer to Primary Purpose, Not Secondary Details
- Identify the main goal: Before selecting an answer, determine what the person in the video is fundamentally trying to accomplish (e.g., fixing something, cleaning, planting, building).
- Distinguish primary from secondary: Separate the core objective from intermediate steps or side effects (e.g., holding a bag is secondary if the primary goal is painting).
- Match the answer to the overarching theme: Select the option that describes the ultimate purpose, not just one action or tool interaction.
- Verify with context: Ask yourself: "If I had to explain this video in one sentence, what would it be?" The correct answer should align with that sentence.
Example: In Failure 4, the assistant chose "C interacts with the black bag to conceal it" when the actual primary purpose was painting (the bag was just a grip aid). The correct answer focuses on the true objective.
Anti-pattern: Selecting an answer that describes only one action or tool without considering the broader activity or end goal.
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 · 17 lines · 40 tokens per session scan A bf9f282f1fd8
video-action-purpose-alignment is a skill published in the GitHub repository UCSC-VLAA/VisualClaw (55 stars, last pushed 2mo ago), licensed MIT. It adds 40 tokens to every session and 277 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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