codegrid
CodeGrid is a native macOS canvas where multiple coding agents (Claude, Codex, Gemini, Cursor, Grok, shells) run side by side in panes and collaborate via a local agent bus — no tmux, no cloud, no account, no stored API keys. Install this skill when an agent should know how to operate inside a CodeGrid pane, drive the workspace from outside (control socket or codegrid:// deep links), spawn or message sibling agents, or coordinate multi-agent work (delegate, review, pipeline, parallel fan-out, monitor, debate). The differentiator: multiple coding agents collaborating on one canvas, addressable by stable session_id, with a read → message → read protocol built for orchestration.
What this skill does
# CodeGrid — agent operating manual
CodeGrid runs many coding agents side by side on an infinite 2D canvas. Each
pane is one process (`claude`, `codex`, `gemini`, `cursor-agent`, `grok`, or a
shell) with its own working directory, git branch, and stable `session_id`. The
agent bus lets any pane discover, read, and message any other pane locally.
This skill is the umbrella entry point. Two references cover the full surface:
- **[Operating CodeGrid](./references/using-codegrid.md)** — the mental model
(canvas / pane / workspace / agent / bus), MCP tools (`list_agents`,
`read_pane`, `message_agent`), the control-socket JSON-RPC API
(`agent_list`, `agent_read`, `agent_send`, `open_folder`, `new_session`,
`new_workspace`), the `codegrid://` deep-link scheme, and an operating
playbook with recipes.
- **[Agent-bus collaboration](./references/codegrid-agent-bus.md)** — the
`read → message → read` protocol, identifying agents by role and
`session_id`, orchestration patterns (delegate, review, pipeline, parallel
fan-out, monitor, debate), etiquette and scope safety, loop/runaway
prevention, failure recovery, and worked end-to-end examples.
## When to load which reference
| You're about to… | Load |
|---|---|
| Discover what's running, open a folder, spawn a session, or drive CodeGrid from outside | `using-codegrid` |
| Hand work to / consult / review with another agent already running in CodeGrid | `codegrid-agent-bus` |
| Both | Load `using-codegrid` first, then `codegrid-agent-bus` |
## Quickstart (inside a CodeGrid pane)
```text
list_agents() # discover panes (session_id, role, status)
read_pane(<session_id>) # see what they're doing — always safe
message_agent(<session_id>, "[from <you>] <self-contained request>")
… wait, then read_pane(<session_id>) for the reply.
```
## Quickstart (from outside CodeGrid, e.g. another tool)
```bash
SOCK="$(cat ~/.codegrid/socket-path 2>/dev/null || echo ~/.codegrid/socket)"
printf '%s\n' '{"jsonrpc":"2.0","id":1,"method":"agent_list"}' | nc -U "$SOCK"
# Or deep-link the installed app:
open "codegrid://open?path=/abs/path/to/repo&type=codex"
open "codegrid://new"
```
## Install
```text
install the codegrid skill from https://github.com/BankrBot/skills/tree/main/codegrid
```
## Source
- Canonical source of truth lives in the CodeGrid repo under `skills/`
(`skills/using-codegrid/SKILL.md`, `skills/codegrid-agent-bus/SKILL.md`).
This Bankr Skills provider is a published copy kept in sync.
- App download + full docs: <https://codegrid.app>.
- Token (Base): `0x6B456E66524aEC1792013eF9DFE87e3F84311ba3` —
see <https://codegrid.app/token>.
## Safety defaults an agent should respect
- **Read before you write.** `read_pane` is free; never message a busy agent.
- **Address by `session_id`**, never pane number — pane numbers shift.
- **Be explicit about scope.** Other agents may be in YOLO/autonomous mode and
will act on whatever you send. Say "propose only, do not edit files" when you
want analysis.
- **One message, then wait.** Multiple messages interleave into the target's
input box and corrupt it.
- **Bound debates and don't spawn unbounded helpers.** Converge and report to
the user.
- **Stay local.** Everything is same-machine IPC; there is no remote bus.
Full guidance: see the two reference files.
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.