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-debuggit 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-debug)<a href="https://agentmods.dev/skills/nyldn/claude-octopus/skill-debug"><img src="https://agentmods.dev/badge/skills/nyldn/claude-octopus/skill-debug/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-debug"><img src="https://agentmods.dev/badge/skills/nyldn/claude-octopus/skill-debug.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00018 | $0.01300 |
| Opus 5.5 | $0.00007 | $0.00520 |
| Sonnet 5.5 | $0.00004 | $0.00260 |
| Haiku 4.5 | $0.00002 | $0.00130 |
Grade A, and why
skill-debug 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 12d 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 — 126 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Debugging
Read skills/blocks/engineering-method-selection.md from the installed plugin
for review admission. Natural-language requests and --peer-review share that
policy. Honor host-only requests; risk alone does not authorize paid usage.
Run the investigation on the current host. Routine debugging makes zero
additional provider dispatches. Use a bounded external reviewer only for
--peer-review, an explicit independent-review request, or an existing risk
policy.
Read and apply skills/blocks/debug-feedback-loop.md from the installed plugin
root.
Start with the user's observable symptom. Reproduce it, retain its failure signature while minimizing the scenario, test one named hypothesis at a time, and verify both the minimal reproduction and the original scenario after the fix. Do not treat a nearby passing helper test as proof.
For a race, use a synchronization barrier and a fixed run or time budget. For an
unavailable production dependency, return inconclusive with the missing
evidence. Remove temporary instrumentation before completion and preserve a
stable reproduction as a regression test.
The final record is data, not an executable queue. Store commands as argument arrays and never evaluate provider-authored text.
Bounded recovery and strategy rotation
Use a 3-Strike Rule for failed fixes. After each failure, return to the evidence and test a materially different hypothesis. After two consecutive failures, a strategy rotation is mandatory: reconsider the root cause, the reproduction, and whether the test encodes the intended behavior. Do not attempt a 4th fix without explicit user approval.
Anti-rationalization check: “Should work now” means run the reproduction and the original scenario. Confidence is not verification.
For multi-attempt debugging, report a WTF score using the defaults in
~/.claude-octopus/loop-config.conf: +15% per revert and +20% for touching
unrelated files. If the score exceeds 20%, STOP and show the evidence before
continuing. Include the score with every retry, for example:
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.
- 12d ago Changed · +39 lines 013d468bb054
- 1mo ago Changed · -312 lines · -2 tokens per session a3c9cf170527
- 1mo ago First seen · 399 lines · 20 tokens per session scan A b1861bed5ba2
skill-debug is a skill published in the GitHub repository nyldn/claude-octopus (4,258 stars, last pushed today), licensed MIT. It adds 18 tokens to every session and 1,300 once invoked, about $0.0001 per session on Opus 5.5. 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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soleur-reproduce-bug
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soleur-test-fix-loop
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soleur-incident
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