cj-ab
Skill1 starsChanged 32 days agoMITView on GitHub
Agent bridge, hand one task (plus an optional file) to another coding agent (claude, codex, copilot) via the bundled `agent-bridge` CLI and get its answer back. Use when the user says "send this to codex/claude/copilot", "ask codex", "/cj-ab"; to delegate a self-contained subtask to an agent better suited for it; to get a second opinion or cross-check your own answer, review or diff; to translate code between languages or syntaxes; or to get an alternative draft to compare against yours.
In this repository19 other files it gives its agents.
- AGENTS.mdAGENTS.md
- cj-ambitionSkill
- cj-automateSkill
- cj-cloudflare-workerSkill
- cj-code-reviewSkill
- cj-dailySkill
- cj-meetingsSkill
- cj-notesSkill
- cj-nowSkill
- cj-owa-toolsSkill
- cj-thingsSkill
- cj-todoSkill
- cj-uxloopSkill
- cj-video-visionSkill
- cj-voice-dnaSkill
- repo-deepSkill
- repo-depsSkill
- repo-groomSkill
- repo-healthSkill
The file
We split this skill into the rules it gives an agent
3 rules. About 0 are common, 1 is distinctive, and 2 are very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈1
Distinctive · estimate
This author's own choices. Worth a look.
2
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Very specific: line 14
If agent-bridge is not on PATH, call it by path from this skill folder.
- Very specific: line 32
If the user named a model, pass it with -m exactly as given.
- Distinctive: line 35
light: only for trivially mechanical work with one right answer: reformatting, a quick lookup, a yes/no sanity check.
≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
cj-ab (agent bridge)
agent-bridge runs one headless prompt against another agent CLI and streams that agent's answer to stdout:
agent-bridge [-m light|medium|heavy|<model>] <claude|codex|copilot> "<prompt>"|- [file_path]If agent-bridge is not on PATH, call it by path from this skill folder.
Writing the prompt
The target agent starts cold: it sees only the prompt and the file, never your conversation. Write the prompt as a complete brief, with the goal, the constraints that matter, and the output format you want back ("output only the code", "list findings as bullets", "reply with a verdict and three reasons"). Pass a single file_path and let the bridge inline it. For several files, name their paths in the prompt; the target runs in your working directory and can read them itself.
Pass the prompt as - and feed it through a heredoc with a quoted delimiter, so the shell leaves quotes, $() and backticks in it alone:
agent-bridge codex - src/auth.py <<'EOF'
Convert this Python file to idiomatic Rust. Output only the code.
EOFAny target is valid, including your own kind. agent-bridge claude from Claude gives a fresh-context second opinion.
Choosing the model
If the user named a model, pass it with -m exactly as given. Otherwise pick the tier from the task's complexity:
- medium: the usual choice, including hard work: a focused or cross-cutting review, a hard bug, a multi-file change, an alternative draft, a translation with real logic in it.
- light: only for trivially mechanical work with one right answer: reformatting, a quick lookup, a yes/no sanity check.
- heavy: expensive (Fable on claude), so reach for it only when the user asks for it, or when the problem is extraordinarily difficult and
mediumhas already fallen short on it.
Torn between light and medium, take medium; torn between medium and heavy, stay on medium. Omitting -m runs the CLI's configured default model. The tier names map to concrete models in the script's TIERS table.
Running it
- Timeout. A call takes 1 to 5 minutes, longer on
heavy. Set the shell tool's timeout to 10 minutes (600000 ms), or use the shell tool's own background mode, which keeps the output and reports completion. With a plain&, redirect stdout and stderr to a file and wait on the process before reading it. - Full access. The target runs with no sandbox and no approval prompts: it can edit files, run commands and reach the network in your working directory. Say in the prompt whether you want edits made or only an answer back, and review
git diffafter a call that edits. - One hop. The bridge refuses a call made from inside a bridged agent. The guard is an inherited env var, so it stops accidental ping-pong only.
- Cancellation. Killing the bridge (timeout, Ctrl-C) takes the target's whole process tree down with it.
- Failures. Errors start with
[agent-bridge]on stderr: bad usage, an unreadable file, or a CLI missing from PATH. The bridge passes the target's own errors and exit code through as-is.
Using the answer
The answer is input to your judgement. Check it, and any edits it made, against the code before building on it or relaying it, and tell the user which agent and model it came from. When you are cross-checking, report where the two answers agree and where they differ.
Does it work?
Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.
No reviews yet
People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.
- It worked
- Partly
- It didn't
Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.
Your trail
Files you open will collect here, so you can find them again.
Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.
Now do this for your own.
Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.

