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-review-responsegit 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-review-response)<a href="https://agentmods.dev/skills/nyldn/claude-octopus/skill-review-response"><img src="https://agentmods.dev/badge/skills/nyldn/claude-octopus/skill-review-response/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-review-response"><img src="https://agentmods.dev/badge/skills/nyldn/claude-octopus/skill-review-response.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, 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 39 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 | $0.00019 | $0.00936 |
| Opus 5 | $0.00010 | $0.00468 |
| Sonnet 5 | $0.00004 | $0.00187 |
| Haiku 4.5 | $0.00002 | $0.00094 |
Grade A, and why
skill-review-response 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 11d 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 — 117 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Receiving Code Review
Core Principle
Code review requires technical evaluation, not performative agreement.
Never blindly implement review feedback. Verify it's correct for THIS codebase before changing anything.
The Response Pattern
WHEN receiving code review feedback:
1. READ — Complete feedback without reacting
2. RESTATE — Summarize the requirement in your own words
3. VERIFY — Check against actual codebase state
4. EVALUATE — Is this technically sound for THIS context?
5. RESPOND — Technical acknowledgment OR reasoned pushback
6. IMPLEMENT — One item at a time, verify each change
Forbidden Responses
NEVER say:
- "You're absolutely right!" (without verification)
- "Great catch!" (before confirming it IS a catch)
- "I'll fix that right away!" (before evaluating whether it needs fixing)
- "Done!" (without running verification — see skill-verification-gate)
These are social performance, not technical evaluation. They lead to:
- Implementing wrong suggestions
- Introducing bugs to "fix" non-issues
- Wasting time on style preferences disguised as bugs
Evaluation Checklist
For each piece of feedback:
| Question | If YES | If NO |
|---|---|---|
| Is the issue real? (verify in code) | Continue evaluation | Push back with evidence |
| Does the suggested fix work here? | Continue evaluation | Propose alternative |
| Does fixing this break something else? | Fix both or push back | Implement the fix |
| Is this a style preference or a real problem? | Acknowledge, deprioritize | Fix it |
| Was this already considered and rejected? | Explain the trade-off | Implement |
How to Push Back
When feedback is wrong or doesn't apply:
> Reviewer: "This function should handle null input"
>
> Response: "Checked — this function is only called from `processUser()`
> (line 47) which validates non-null before dispatch. Adding null handling
> here would be dead code. The caller contract guarantees non-null."
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.
- 11d ago First seen · 117 lines · 19 tokens per session scan A c7de0e3b0f8d
skill-review-response is a skill published in the GitHub repository nyldn/claude-octopus (4,061 stars, last pushed today), licensed MIT. It adds 19 tokens to every session and 936 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 2026-08-30.
Other skills, from other repositories
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kieran-rails-reviewer
Use this agent when you need to review Rails code changes with an extremely high quality bar. Applies Kieran's strict Rails conventions and taste preferences. Use dhh-rails-reviewer for opinionated architectural critique; use this agent for strict convention and quality checks.
pattern-recognition-specialist
Use this agent when you need to analyze code for design patterns, anti-patterns, naming conventions, and code duplication. This agent excels at identifying architectural patterns, detecting code smells, and ensuring consistency across the codebase.
code-quality-analyst
Use this agent when you need a formal quality report with severity-scored findings and a prioritized refactoring roadmap. Use pattern-recognition-specialist for quick pattern checks; use this agent when you need a formal report to plan refactoring work.
code-simplicity-reviewer
Use this agent when you need a final review pass to ensure code changes are as simple and minimal as possible. Invoked after implementation to identify simplification opportunities and ensure YAGNI adherence.