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-context-detectiongit 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-context-detection)<a href="https://agentmods.dev/skills/nyldn/claude-octopus/skill-context-detection"><img src="https://agentmods.dev/badge/skills/nyldn/claude-octopus/skill-context-detection/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-context-detection"><img src="https://agentmods.dev/badge/skills/nyldn/claude-octopus/skill-context-detection.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 | $0.00025 | $0.02365 |
| Opus 5.5 | $0.00010 | $0.00946 |
| Sonnet 5.5 | $0.00005 | $0.00473 |
| Haiku 4.5 | $0.00003 | $0.00236 |
Grade A, and why
skill-context-detection scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 276 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Context Detection - Internal Skill
Purpose
This skill provides automatic context detection to determine whether the user is working in a Development context (code-focused) or Knowledge context (research/strategy-focused). This replaces the manual /octo:km toggle with intelligent auto-detection.
Detection Algorithm
When a workflow skill activates, detect context using these signals:
Step 1: Check for Explicit Override
If user has explicitly set mode via /octo:km on or /octo:km off, respect that setting.
# Check if knowledge mode is explicitly set
if [[ -f ~/.claude-octopus/config/knowledge-mode ]]; then
EXPLICIT_MODE=$(cat ~/.claude-octopus/config/knowledge-mode)
if [[ "$EXPLICIT_MODE" == "on" ]]; then
echo "knowledge"
exit 0
elif [[ "$EXPLICIT_MODE" == "off" ]]; then
echo "dev"
exit 0
fi
fi
# If "auto" or not set, proceed with auto-detection
Step 2: Analyze Prompt Content (Strongest Signal)
Knowledge Context Indicators (check prompt for these terms):
- Business/strategy: "market", "ROI", "stakeholders", "strategy", "business case", "competitive"
- Research: "literature", "synthesis", "academic", "papers", "research question"
- UX: "personas", "user research", "journey map", "pain points", "interviews"
- Deliverables: "presentation", "report", "PRD", "proposal", "executive summary"
Dev Context Indicators (check prompt for these terms):
- Technical: "API", "endpoint", "database", "function", "class", "module"
- Actions: "implement", "debug", "refactor", "test", "deploy", "build"
- Artifacts: "code", "tests", "migration", "schema", "controller"
Scoring:
- Count knowledge indicators in prompt
- Count dev indicators in prompt
- Higher count wins
- If tied, check project context (Step 3)
Step 3: Analyze Project Context (Secondary Signal)
Dev Project Indicators:
- Has
package.json,Cargo.toml,go.mod,pyproject.toml,pom.xml - Has
src/,lib/,app/directories with code files - Recent files are
.ts,.js,.py,.go,.rs,.java
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.
- 1mo ago First seen · 276 lines · 25 tokens per session scan A 4aca867a6a46
skill-context-detection is a skill published in the GitHub repository nyldn/claude-octopus (4,295 stars, last pushed yesterday), licensed MIT. It adds 25 tokens to every session and 2,365 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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