RuView is a WiFi sensing platform that uses disturbances in radio signals, captured by low-cost ESP32 sensors, to detect presence, movement, breathing, and heart rate without cameras or wearables. It is intended for spatial monitoring and smart-home integrations. The catalogue add-ons support workflows for operating and integrating RuView.
Borrowing it
Nothing to install: this file belongs to ruvnet/RuView. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/ruvnet/RuView/main/.claude/commands/hooks/post-task.mdgit clone --depth 1 https://github.com/ruvnet/RuViewWrote 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/ruvnet/ruview/post-task)<a href="https://agentmods.dev/commands/ruvnet/ruview/post-task"><img src="https://agentmods.dev/badge/commands/ruvnet/ruview/post-task/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/ruvnet/ruview/post-task"><img src="https://agentmods.dev/badge/commands/ruvnet/ruview/post-task.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 | $0.00000 | $0.00545 |
| Opus 5.5 | $0.00000 | $0.00218 |
| Sonnet 5.5 | $0.00000 | $0.00109 |
| Haiku 4.5 | $0.00000 | $0.00055 |
Grade A, and why
post-task 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.
Copies of this mod
8 near-identical copies found in the catalogue:
- post-task — 100% identical, 0 lines differ
- post-task — 100% identical, 0 lines differ
- post-task — 100% identical, 0 lines differ
- post-task — 100% identical, 12 lines differ
- post-task — 100% identical, 0 lines differ
- post-task — 100% identical, 0 lines differ
- post-task — 100% identical, 0 lines differ
- post-task — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 113 lines — stays where its author put it; the contents beside it link to each section on GitHub.
hook post-task
Execute post-task cleanup, performance analysis, and memory storage.
Usage
npx claude-flow hook post-task [options]
Options
--task-id, -t <id>- Task identifier for tracking--analyze-performance- Generate performance metrics (default: true)--store-decisions- Save task decisions to memory--export-learnings- Export neural pattern learnings--generate-report- Create task completion report
Examples
Basic post-task hook
npx claude-flow hook post-task --task-id "auth-implementation"
With full analysis
npx claude-flow hook post-task -t "api-refactor" --analyze-performance --generate-report
Memory storage
npx claude-flow hook post-task -t "bug-fix-123" --store-decisions --export-learnings
Quick cleanup
npx claude-flow hook post-task -t "minor-update" --analyze-performance false
Features
Performance Analysis
- Measures execution time
- Tracks token usage
- Identifies bottlenecks
- Suggests optimizations
Decision Storage
- Saves key decisions made
- Records implementation choices
- Stores error resolutions
- Maintains knowledge base
Neural Learning
- Exports successful patterns
- Updates coordination models
- Improves future performance
- Trains on task outcomes
Report Generation
- Creates completion summary
- Documents changes made
- Lists files modified
- Tracks metrics achieved
Integration
This hook is automatically called by Claude Code when:
- Completing a task
- Switching to a new task
- Ending a work session
- After major milestones
Manual usage in agents:
# In agent coordination
npx claude-flow hook post-task --task-id "your-task-id" --analyze-performance true
Output
Returns JSON with:
{
"taskId": "auth-implementation",
"duration": 1800000,
"tokensUsed": 45000,
"filesModified": 12,
"performanceScore": 0.92,
"learningsExported": true,
"reportPath": "/reports/task-auth-implementation.md"
}
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 · 113 lines · 0 tokens per session scan A 1859b9ed7ae4
post-task is a command published in the GitHub repository ruvnet/RuView (98,353 stars, last pushed today), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 545 tokens. 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 commands, from other repositories
brain-setup
Set up Brain plugin — personal vault, auto-synthesize, surface hints, and optional team vault.
brain-team-setup
Initialize or update team vault settings — git-backed shared knowledge vault.
brain-reflect
Weekly pattern synthesis — reads recent session notes and extracts wins, blockers, themes, and suggested Polaris updates.
learn
Force claude-smart to extract learnings from this session now.
memory-store
Store an insight, decision, or pattern to memory.
lians-recall
Recall current (non-stale) facts from Lians memory, optionally as-of a past date.