Claude Octopus is an orchestration project that sends research, design, and coding tasks to Claude Code and other AI model providers so their results can be compared. Developers use it for multi-model work, disagreement detection, reviews, persistent context, and an optional workflow that moves from discovery through delivery. The catalogue entries are its commands, skills, agents, instructions, hooks, plugins, and settings.
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 nyldn/claude-octopus --skill skill-visual-feedbackgit clone --depth 1 https://github.com/nyldn/claude-octopusWrote 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/nyldn/claude-octopus/skill-visual-feedback)<a href="https://agentmods.dev/skills/nyldn/claude-octopus/skill-visual-feedback"><img src="https://agentmods.dev/badge/skills/nyldn/claude-octopus/skill-visual-feedback/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/nyldn/claude-octopus/skill-visual-feedback"><img src="https://agentmods.dev/badge/skills/nyldn/claude-octopus/skill-visual-feedback.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 2 findings, 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 Excessive Agency · line 451 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
- medium Excessive Agency · line 450 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
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 | $0.00024 | $0.02635 |
| Opus 5 | $0.00012 | $0.01318 |
| Sonnet 5 | $0.00005 | $0.00527 |
| Haiku 4.5 | $0.00002 | $0.00264 |
Grade A, and why
skill-visual-feedback 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.
How it starts
The opening of the file, as written. The whole thing — 474 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Visual Feedback Processing
Overview
Systematic approach to processing image-based UI/UX feedback, identifying visual issues, and implementing fixes.
Core principle: Analyze image → Identify issues → Locate code → Fix systematically → Verify visually.
When to Use
Use this skill when user provides:
- Screenshots with UI/UX problems
- "[Image]" prefix with description of visual issues
- Complaints about "messy UI" or "hot mess UX"
- Button styling or layout issues with visual examples
- "This should look like X but shows as Y" with images
Do NOT use for:
- Pure code issues without visual context
- Feature requests without UI mockups
- Performance or functional bugs
- Backend issues
The Process
Phase 1: Visual Analysis
When user provides image feedback:
Step 1: Acknowledge and Examine
I can see the screenshot showing [describe what you observe].
Let me analyze the visual issues:
**Observed Problems:**
1. [Issue 1: e.g., Button styles inconsistent]
2. [Issue 2: e.g., Layout misaligned]
3. [Issue 3: e.g., Colors don't match design system]
**Expected Behavior (from description):**
- [What user said it should be]
**Actual Behavior (from image):**
- [What the image shows]
Step 2: Categorize Issues
| Issue Type | Examples |
|---|---|
| Styling | Colors, fonts, spacing, borders |
| Layout | Alignment, positioning, responsive behavior |
| Component | Wrong component used, missing component |
| State | Hover states, active states, disabled states |
| Consistency | Inconsistent patterns across UI |
**Issue Categories:**
- Styling: [list specific styling issues]
- Layout: [list layout issues]
- Component: [list component issues]
- State: [list state-related issues]
- Consistency: [list inconsistency issues]
Phase 2: Code Investigation
Step 1: Locate Relevant Components
# Search for component files related to the issue
# Example: For settings page issues
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 · 474 lines · 24 tokens per session scan A eb1b4c2fda95
skill-visual-feedback is a skill published in the GitHub repository nyldn/claude-octopus (4,062 stars, last pushed today), licensed MIT. It adds 24 tokens to every session and 2,635 once invoked, about $0.0001 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 2026-09-03.
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