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 multi-option-contextual-eliminationgit 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/multi-option-contextual-elimination)<a href="https://agentmods.dev/skills/ucsc-vlaa/visualclaw/multi-option-contextual-elimination"><img src="https://agentmods.dev/badge/skills/ucsc-vlaa/visualclaw/multi-option-contextual-elimination/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/ucsc-vlaa/visualclaw/multi-option-contextual-elimination"><img src="https://agentmods.dev/badge/skills/ucsc-vlaa/visualclaw/multi-option-contextual-elimination.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00307 |
| Opus 5 | $0.00020 | $0.00153 |
| Sonnet 5 | $0.00008 | $0.00061 |
| Haiku 4.5 | $0.00004 | $0.00031 |
Grade A, and why
multi-option-contextual-elimination 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 9d 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
Systematically Eliminate Implausible Options
- Scan all options first: Read all choices (A–E) before committing to an answer.
- Apply visual constraints: Eliminate options that contradict what is visibly happening in the video (e.g., if no cooking equipment is visible, eliminate "preparing dinner").
- Check action sequence logic: Eliminate options that don't align with the sequence of actions shown (e.g., if actions involve soil and seedlings, "searching for something" is less likely than "planting").
- Use thematic consistency: Eliminate options with contradictory or overly dramatic language if the video shows routine activity (e.g., "cleverly hiding" vs. "putting away").
- Select by best fit: Choose the option that accounts for the most observed actions and aligns with the visual evidence.
Example: In Failure 3, the assistant chose "getting ready for bed" when visual evidence (cleaning tools, house-related actions) clearly pointed to "cleaning the house." Eliminating implausible options first would have prevented this.
Anti-pattern: Selecting the first option that seems plausible without comparing it against all alternatives and visual evidence.
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.
- 9d ago First seen · 18 lines · 40 tokens per session scan A 8415509989ed
multi-option-contextual-elimination 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 307 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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