conduxt
Orchestrate full-duplex coding agent sessions via ACPX (preferred) or tmux (fallback), composing OpenClaw native tools and community Skills. Handles any coding task: requirements, bug fixes, refactoring, investigations. Use when: "implement feature X", "fix this bug", "refactor the API layer", "start agent", "open a session", "code this", "fix issue #N".
What this skill does
# CLI Coding Orchestrator
> You are the orchestrator. Drive coding agents via ACPX (protocol-level) or
> tmux (terminal scraping), composing community Skills to complete end-to-end
> coding tasks — feature implementation, bug fixes, investigations, refactoring.
---
## 1. Your Role
You are the OpenClaw Main model. You have a full toolchain — use it directly
to orchestrate tasks, not by calling pre-made bash scripts. Scripts in the
`scripts/` directory exist only as optional helpers.
## 2. Dual-Backend Architecture: ACPX vs tmux
This Skill supports two agent communication backends. **Prefer ACPX**, use tmux as fallback.
### Why ACPX First
| Dimension | ACPX (Protocol) | tmux (Terminal Scraping) |
|-----------|-----------------|------------------------|
| Communication | Full-duplex JSON-RPC over stdio | Half-duplex PTY scraping |
| Output | Typed ndjson (tool_call/text/done) | Raw ANSI text (burns 30-40% Context) |
| Mid-task instructions | Prompt queue: submit anytime, queued | send-keys: timing issues, may be treated as user input |
| Completion detection | Native `[done]` signal | Regex matching or Callback injection |
| Cancellation | Cooperative `session/cancel` (preserves state) | `C-c` (unreliable, may corrupt state) |
| Crash recovery | Auto-restart + load serialized session | Session survives but agent death goes unnoticed |
| Permissions | `--approve-all` / `--deny-all` policy-based | Interactive TTY popups (block unattended flows) |
| Visual monitoring | ndjson pipe to external tools | tmux split-pane (advantage) |
**ACPX is strictly superior for communication, observation, and mid-task instructions. tmux only wins on maturity and visual monitoring.**
### When to Use Which
| Scenario | Backend |
|----------|---------|
| Default / new tasks | **ACPX** |
| ACPX unavailable or unstable | tmux (fallback) |
| Need visual split-pane monitoring | tmux (or ACPX + external dashboard) |
| Agent doesn't support ACP | tmux |
---
## 3. Toolbox
### Native Tools
| Tool | Purpose | Key Usage |
|------|---------|-----------|
| `exec` | Run shell commands | `acpx prompt`, `tmux send-keys`, `git worktree`, `gh` |
| `exec pty:true` | Interactive terminal | Simple one-off tasks (do NOT nest tmux inside PTY) |
| `process` | Background processes | `background:true` for long tasks, `process action:log limit:20` |
| `read`/`write`/`edit` | File operations | MEMORY.md, active-tasks.json |
| `gh` | GitHub CLI | `gh issue view`, `gh pr create` |
| `git` | Version control | `git worktree add/remove`, `git branch`, `git push` |
### ACPX Commands
| Command | Purpose |
|---------|---------|
| `acpx prompt -s <session> "<instruction>"` | Send prompt (creates session if new, appends if existing) |
| `acpx prompt -s <session> --no-wait "<msg>"` | Fire-and-forget (returns immediately) |
| `acpx prompt -s <session> --format json "<msg>"` | Structured ndjson output |
| `acpx sessions list` | List all active sessions |
| `acpx sessions show -s <session>` | Show session details |
| `acpx cancel -s <session>` | Cooperative cancel of current task |
| `acpx prompt -s <session> --approve-all "<msg>"` | Auto-approve all permission requests |
### Community Skills (Composable)
| Skill | When to Use | Core Capability |
|-------|-------------|-----------------|
| `coding-agent` | Agent lifecycle management (tmux backend) | tmux session + Callback wakeup + worktree |
| `tmux` | Low-level tmux operations | Socket management, send-keys, wait-for-text |
| `tmux-agents` | Multi-agent types (tmux backend) | Codex, Gemini, local models |
| `gemini` | Gemini CLI coding | Long-context tasks |
| `resilient-coding-agent` | Gateway restart recovery | tmux session persistence |
> **Composition principle**: Use Skills when available (they encapsulate best practices).
> Fall back to native tools when Skills don't cover your needs.
> coding-agent / tmux-agents use tmux backend — if using ACPX backend, use `acpx` commands directly.
---
## 4. Full-Duplex Communication Model
### ACPX Path (Preferred)
```
User ←→ You (Main) ←→ acpx ←→ ACP Adapter ←→ Coding Agent
↕ ↕
MEMORY.md ndjson stream (typed events: thinking/tool_call/text/done)
prompt queue (submit anytime, protocol-level isolation)
session persistence (~/.acpx/sessions/*.json)
```
- **User → Agent**: `acpx prompt -s <session> "<instruction>"` enters the prompt queue
- **Agent → User**: `[done]` event in ndjson stream → you are woken up → notify user
- **True full-duplex**: Submit new instructions while previous task is running, queued without timing issues
### tmux Path (Fallback)
```
User ←→ You (Main) ←→ tmux session ←→ Coding Agent
↕ ↕
MEMORY.md send-keys (inject instructions)
capture-pane (read output)
Callback event (completion notification)
```
- **User → Agent**: `tmux send-keys -t <session> "<text>" Enter`
- **Agent → User**: Callback JSON or capture-pane polling
- **Timing caveat**: When agent is busy, send-keys may be treated as user input. Send `Escape` first and wait for idle.
---
## 5. Scenario Playbook
Each scenario provides both ACPX (preferred) and tmux (fallback) paths.
### Scenario A: Execute Coding Task
**Triggers** (task source is flexible):
- "Implement pagination for the users API"
- "Investigate this performance issue"
- "Refactor the API layer to RESTful"
- "Fix issue #78" (optional, low priority)
```
1. Understand the Task
Task sources are diverse — handle flexibly:
• User describes requirement → use description text as prompt directly
• Link to external doc/wiki → fetch content and extract requirements
• GitHub issue → exec: gh issue view <N> --json title,body
• Code review comments → extract action items
2. Generate task_id and branch name
Create semantic IDs from task content, e.g.:
• "add pagination" → task_id: add-pagination, branch: feat/add-pagination
• "perf issue" → task_id: perf-analysis, branch: fix/perf-analysis
• issue #78 → task_id: issue-78, branch: fix/issue-78
3. Create isolated workspace
→ exec: git worktree add ../worktrees/<task_id> -b <branch> main
4. Start Coding Agent
┌─ ACPX path (preferred) ────────────────────────────────────┐
│ exec: cd ../worktrees/<task_id> && acpx prompt \ │
│ -s <task_id> \ │
│ --approve-all \ │
│ --no-wait \ │
│ "<task description + callback instructions (see §6)>" │
│ │
│ • --no-wait: returns immediately, doesn't block you │
│ • --approve-all: auto-approve permissions for unattended │
│ • session auto-persisted to ~/.acpx/sessions/<task_id>.json│
└────────────────────────────────────────────────────────────┘
┌─ tmux path (fallback) ─────────────────────────────────────┐
│ a) Use coding-agent Skill (recommended) │
│ b) Use tmux-agents Skill (for Gemini/Codex) │
│ c) Direct exec: │
│ tmux new-session -d -s <task_id> -c ../worktrees/<id> │
│ tmux send-keys -t <task_id> "claude" Enter │
│ tmux send-keys -t <task_id> "<prompt + callback>" Enter │
└────────────────────────────────────────────────────────────┘
5. Write MEMORY.md task entry (see §7)
6. Inform user
→ "Session <task_id> started, agent is working. Will notify on completion."
7. Wait for completion
ACPX: [done] in ndjson stream → read result → route
tmux: Callback arrives or 30min timeout → capture-pane → notify
```
**Parallel tasks**: Repeat the above for each task. ACPX natively supports named parallel sessions.
Before creating PRs, check for file conflicts between branches with `git diff --name-only`.
Related in Backend & APIs
jfrog
IncludedInteract with the JFrog Platform via the JFrog CLI and REST/GraphQL APIs. Use this skill when the user wants to manage Artifactory repositories, upload or download artifacts, manage builds, configure permissions, manage users and groups, work with access tokens, configure JFrog CLI servers, search artifacts, manage properties, set up replication, manage JFrog Projects, run security audits or scans, look up CVE details, query exposures scan results from JFrog Advanced Security, manage release bundles and lifecycle operations, aggregate or export platform data, or perform any JFrog Platform administration task. Also use when the user mentions jf, jfrog, artifactory, xray, distribution, evidence, apptrust, onemodel, graphql, workers, mission control, curation, advanced security, exposures, or any JFrog product name.
cupynumeric-migration-readiness
IncludedPre-migration readiness assessor for porting NumPy to cuPyNumeric. Use BEFORE substantial porting work begins when the user asks whether code will scale on GPU, whether they should migrate to cuPyNumeric, which NumPy patterns transfer cleanly, what must be refactored before porting, or mentions pre-port assessment, scaling analysis, or refactor planning. Inspect the user's source code, look up NumPy usage, cross-reference the cuPyNumeric API support manifest, and distinguish distributed-scaling-friendly patterns from blockers such as unsupported APIs, scalar synchronization, host round-trips, Python/object-heavy control flow, shape/data-dependent branching, and in-place mutation hazards. Produce a verdict of READY, LIGHT REFACTOR, SIGNIFICANT REFACTOR, or NOT RECOMMENDED, with concrete refactor pointers.
alibabacloud-data-agent-skill
IncludedInvoke Alibaba Cloud Apsara Data Agent for Analytics via CLI to perform natural language-driven data analysis on enterprise databases. Data Agent for Analytics is an intelligent data analysis agent developed by Alibaba Cloud Database team for enterprise users. It automatically completes requirement analysis, data understanding, analysis insights, and report generation based on natural language descriptions. This tool supports: discovering data resources (instances/databases/tables) managed in DMS, initiating query or deep analysis sessions, real-time progress tracking, and retrieving analysis conclusions and generated reports. Use this Skill when users need to query databases, analyze data trends, generate data reports, ask questions in natural language, or mention "Data Agent", "data analysis", "database query", "SQL analysis", "data insights".
token-optimizer
IncludedReduce OpenClaw token usage and API costs through smart model routing, heartbeat optimization, budget tracking, and native 2026.2.15 features (session pruning, bootstrap size limits, cache TTL alignment). Use when token costs are high, API rate limits are being hit, or hosting multiple agents at scale. The 4 executable scripts (context_optimizer, model_router, heartbeat_optimizer, token_tracker) are local-only — no network requests, no subprocess calls, no system modifications. Reference files (PROVIDERS.md, config-patches.json) document optional multi-provider strategies that require external API keys and network access if you choose to use them. See SECURITY.md for full breakdown.
resend-cli
IncludedUse this skill when the task is specifically about operating Resend from an AI agent, terminal session, or CI job via the official resend CLI: installing/authenticating the CLI, sending/listing/updating/cancelling emails, batch sends, domains and DNS, webhooks and local listeners, inbound receiving, contacts, topics, segments, broadcasts, templates, API keys, profiles, or debugging Resend CLI/API failures. Trigger on mentions of Resend CLI, `resend`, `resend doctor`, `resend emails send`, `resend domains`, `resend webhooks listen`, `resend emails receiving`, or agent-friendly terminal automation.
alibabacloud-odps-maxframe-coding
IncludedUse this skill for MaxFrame SDK development and documentation navigation on Alibaba Cloud MaxCompute (ODPS). Helps answer MaxFrame API, concept, official example, and supported pandas API questions; create data processing programs; read/write MaxCompute tables; debug jobs (remote or local); and build custom DPE runtime images. Trigger when users mention MaxFrame, MaxCompute with MaxFrame, ODPS table processing, DPE runtime, MaxFrame docs/examples, DataFrame/Tensor operations, or GPU runtime setup. Works for both English and Chinese queries about Alibaba Cloud data processing with MaxFrame.