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.
git 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/commands/nyldn/claude-octopus/octo-history)<a href="https://agentmods.dev/commands/nyldn/claude-octopus/octo-history"><img src="https://agentmods.dev/badge/commands/nyldn/claude-octopus/octo-history/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/commands/nyldn/claude-octopus/octo-history"><img src="https://agentmods.dev/badge/commands/nyldn/claude-octopus/octo-history.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 | $0.00017 | $0.00985 |
| Opus 5 | $0.00009 | $0.00492 |
| Sonnet 5 | $0.00003 | $0.00197 |
| Haiku 4.5 | $0.00002 | $0.00098 |
Grade A, and why
octo-history scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 8d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
Copies of this mod
2 near-identical copies found in the catalogue:
- history — 94% identical, 3 lines differ
- octo-history — 88% identical, 9 lines differ
How it starts
The opening of the file, as written. The whole thing — 118 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Workflow History (/octo:history)
Your first output line MUST be: 🐙 Octopus History
Query structured records of past Claude Octopus workflow runs.
EXECUTION CONTRACT (Mandatory)
When the user invokes /octo:history, follow these steps in order.
STEP 1: Locate Run Store
Check for the run store file:
RUN_STORE="${HOME}/.claude-octopus/runs/run-log.jsonl"
if [[ -f "$RUN_STORE" ]]; then
TOTAL=$(wc -l < "$RUN_STORE" | tr -d ' ')
echo "Run store: $TOTAL entries"
else
echo "No run store found at $RUN_STORE"
echo ""
echo "The run store records results from multi-AI workflows:"
echo " /octo:discover - Multi-AI research"
echo " /octo:develop - Multi-AI implementation"
echo " /octo:review - Multi-AI code review"
echo " /octo:debate - Multi-AI deliberation"
echo " /octo:embrace - Full 4-phase lifecycle"
echo ""
echo "Run any multi-AI workflow to start recording history."
fi
If no run store exists, show the guidance above and stop.
STEP 2: Parse Arguments
Accept optional arguments for filtering:
| Argument | Effect | Example |
|---|---|---|
| (none) | Show last 10 runs | /octo:history |
N (number) |
Show last N runs | /octo:history 20 |
| workflow name | Filter by workflow | /octo:history discover |
| date (YYYY-MM-DD) | Filter by date | /octo:history 2026-03-21 |
stats |
Show summary statistics | /octo:history stats |
experiments |
Show experiment logs | /octo:history experiments |
Multiple arguments can be combined: /octo:history discover 2026-03-21
STEP 3: Display Results
For each matching run, display as a table row:
Workflow History (last N runs)
═══════════════════════════════════════════════════════════════════
Date Workflow Providers Findings Status Duration
─────────────────────────────────────────────────────────────────
2026-03-21 discover codex,agy,claude 12 success 45s
2026-03-21 review codex,agy,claude 8 success 62s
2026-03-20 develop codex,claude 3 success 120s
2026-03-20 debate codex,agy,claude — success 95s
═══════════════════════════════════════════════════════════════════
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 · 118 lines · 17 tokens per session scan A 63dcac30a729
octo-history is a command published in the GitHub repository nyldn/claude-octopus (4,062 stars, last pushed today), licensed MIT. It adds 17 tokens to every session and 985 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.
Other commands, from other repositories
ai-context
Generate, update, or audit AI IDE context files with AGENTS.md as the canonical shared context and tool-specific bridge files. Signal Gate principle — only what agents cannot discover: $ARGUMENTS.
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.