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-coverage-auditgit 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-coverage-audit)<a href="https://agentmods.dev/skills/nyldn/claude-octopus/skill-coverage-audit"><img src="https://agentmods.dev/badge/skills/nyldn/claude-octopus/skill-coverage-audit/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-coverage-audit"><img src="https://agentmods.dev/badge/skills/nyldn/claude-octopus/skill-coverage-audit.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 250 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 | $0.00028 | $0.02244 |
| Opus 5 | $0.00014 | $0.01122 |
| Sonnet 5 | $0.00006 | $0.00449 |
| Haiku 4.5 | $0.00003 | $0.00224 |
Grade A, and why
skill-coverage-audit 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 9d 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 — 269 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Test Coverage Audit
Overview
Trace every codepath in a diff, map each path against existing tests, visualize coverage gaps, and auto-generate tests for uncovered paths.
Core principle: Trace codepaths in changed files -> Map against existing tests -> Score coverage quality -> Generate tests for gaps -> Report before/after counts.
Caps and Limits
These hard limits prevent runaway analysis:
- 30 code paths max per audit. If a diff yields more than 30, prioritize by complexity and risk (error paths, security-sensitive branches, public API surfaces first).
- 20 tests generated max per audit. Focus on highest-impact gaps first.
- 2-minute per-test exploration cap. If understanding a single test path takes longer than 2 minutes, mark it as "needs manual review" and move on.
Phase 1: Codepath Tracing
Step 1: Identify Changed Files
Determine the diff scope. Use the most relevant source:
# PR diff
git diff --name-only main...HEAD
# Staged changes
git diff --name-only --cached
# Last commit
git diff --name-only HEAD~1..HEAD
Filter to source code files only (exclude configs, docs, generated files).
Step 2: Trace Data Flow Through Every Branch
For each changed file, you MUST trace:
- Conditionals -- Every
if/else,switch/case, ternary, and pattern match. Each branch is a separate codepath. - Error paths -- Every
catch,throw, error return, validation failure, and early return with error. WHY: Error paths are the most common source of untested bugs. - Function calls -- Every function invoked from changed code. Trace one level deep into callees to identify integration boundaries.
- Loop boundaries -- Empty collection, single item, and multi-item paths through loops.
- Guard clauses -- Every early return, null check, and permission gate.
Step 3: Build the Codepath Inventory
Produce a structured inventory:
## Codepath Inventory: [filename]
| # | Path Description | Type | Risk |
|---|-----------------|------|------|
| 1 | validateUser() happy path | conditional | low |
| 2 | validateUser() missing email | error | medium |
| 3 | validateUser() invalid format | error | medium |
| 4 | processOrder() empty cart guard | guard | high |
| 5 | processOrder() payment timeout | error | high |
| 6 | processOrder() success | conditional | low |
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.
- 9d ago First seen · 269 lines · 28 tokens per session scan A 3533330f8482
skill-coverage-audit is a skill published in the GitHub repository nyldn/claude-octopus (4,056 stars, last pushed today), licensed MIT. It adds 28 tokens to every session and 2,244 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
go-testing
Trigger: Go tests, go test coverage, Bubbletea teatest, golden files. Apply focused Go testing patterns.
brooks-test
Test quality review drawing on twelve classic engineering books — with primary focus on xUnit Test Patterns, The Art of Unit Testing, How Google Tests Software, and Working Effectively with Legacy Code — that diagnoses structural problems in an existing test suite: brittleness, mock abuse, coverage illusions, slow…
pre-pr-audit
Pre-PR confidence audit with 5-dimension scoring. Use when: final check before commit/push/PR, evaluating PR readiness, assessing test quality + risk + coverage holistically. Triggers: pre-pr, readiness check, confidence audit, final verification, ready to PR, how confident. Not for: code review (use…
crap-analyzer
Use to produce a risk-based refactor + test plan for recently-changed code on a diff/branch/PR by computing CRAP (complexity × untested) on changed methods. Multi-language — TypeScript, JavaScript, Python, Java, Kotlin, Go, Ruby, C#, Rust, PHP — auto-discovers how the repo generates coverage. Triggers …
onboard-repo
Index an unfamiliar codebase into the knowledge graph, then produce a first orientation map -- entry points, most-depended-upon modules, hotspots, test topology.
spike-consumer-adversarial
OI-3 spike harness — heavy consumer, ADVERSARIAL arm. Worst-case early-exit test: the mid-workflow Skill call has no continuation guardrail and the guidance skill ends with a final-sounding anchor. Use only when explicitly invoked by the spike harness with a TRIALID and data path.