acp-debug
Debug an ACP agent CLI by spawning it, speaking raw JSON-RPC, and capturing every frame to a JSONL file. Use when the user asks to probe an agent's capabilities, compare agents, test a prompt against an agent, inspect raw ACP wire frames, or investigate why an agent fails to initialize.
Best use case
acp-debug is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Debug an ACP agent CLI by spawning it, speaking raw JSON-RPC, and capturing every frame to a JSONL file. Use when the user asks to probe an agent's capabilities, compare agents, test a prompt against an agent, inspect raw ACP wire frames, or investigate why an agent fails to initialize.
Teams using acp-debug should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/acp-debug/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How acp-debug Compares
| Feature / Agent | acp-debug | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Debug an ACP agent CLI by spawning it, speaking raw JSON-RPC, and capturing every frame to a JSONL file. Use when the user asks to probe an agent's capabilities, compare agents, test a prompt against an agent, inspect raw ACP wire frames, or investigate why an agent fails to initialize.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
Related Guides
SKILL.md Source
# ACP Debug Run a headless ACP JSON-RPC session against any registered kandev agent (or an arbitrary command), record every wire frame to a JSONL file, and summarize the handshake. Backed by the `acpdbg` binary at `apps/backend/bin/acpdbg`. ## When to use this skill - "what models does auggie advertise" - "probe claude-acp" / "probe all agents" / "run the matrix" - "debug why copilot-acp isn't starting" - "what does a `session/new` response actually look like for X" - "try this prompt against auggie with mode=ask" - "reproduce session/load for session <id> against claude-acp" - User mentions inspecting raw ACP wire payloads or a JSONL file from an earlier run ## Before anything else: build the binary if missing ```bash test -x apps/backend/bin/acpdbg || make -C apps/backend build-acpdbg ``` ## Sub-commands ```text acpdbg list # enumerate registered ACP agents acpdbg probe <agent> # initialize + session/new + close acpdbg probe --exec "<cmd> [args...]" # probe an arbitrary binary not in the registry acpdbg prompt <agent> --prompt "..." [--model M] [--mode M] acpdbg session-load <agent> --session-id <id> acpdbg matrix # probe every ACP agent in parallel ``` **Shared flags** (apply to every sub-command): - `--out DIR` — JSONL output directory (default `./acp-debug/`) - `--file PATH` — exact JSONL path, overrides `--out` - `--timeout DUR` — overall run timeout (default `30s`) - `--workdir PATH` — child cwd (default: fresh `/tmp/kandev-acpdbg-<pid>-*`) - `--verbose` — mirror frames to stderr - `--stderr` — capture child stderr into the JSONL ## Steps **Create a task for each step below and mark them as completed as you go.** ### 1. Pick the sub-command that matches the user's intent - "what models does X have" / "does X support modes" → `acpdbg probe X` - "test a prompt against X" → `acpdbg prompt X --prompt "..."` - "compare all agents" / "run the matrix" → `acpdbg matrix` - "resume session <id>" → `acpdbg session-load X --session-id <id>` - "try this random binary" → `acpdbg probe --exec "path/to/bin --acp"` ### 2. Run the command Always capture stdout — it contains the JSONL file path (and for `matrix`, the summary table and `matrix-summary.json` path). ```bash apps/backend/bin/acpdbg probe auggie --timeout 45s ``` For `matrix`, prefer `--timeout 60s` so `npx`-spawned agents have time to cold-start. ### 3. Read the JSONL file The JSONL schema is: | `direction` | Meaning | Extra fields | |---|---|---| | `meta` | acpdbg-generated marker | `event` (`start` / `close`), `meta` (map with `agent`, `command`, `workdir` for start; `exit_code`, `reason` for close) | | `sent` | Frame written to child's stdin | `frame` (JSON-RPC request or reply) | | `received` | Frame read from child's stdout | `frame` (JSON-RPC request, response, or notification) | | `stderr` | Child stderr line (only when `--stderr`) | `line` | Entries are strictly chronological. Each line is a single JSON object terminated by `\n`. Useful `jq` recipes: ```bash # Full initialize response jq -c 'select(.direction == "received" and .frame.id == 1)' acp-debug/<file>.jsonl # Full session/new response (models, modes, auth) jq '.frame.result' acp-debug/<file>.jsonl | head -50 # Just the models advertised jq -r 'select(.direction == "received") | .frame.result.models.availableModels[]?.modelId' acp-debug/<file>.jsonl # Close event (exit code + reason) jq -c 'select(.direction == "meta" and .event == "close")' acp-debug/<file>.jsonl # All agent-initiated requests we auto-replied to jq -c 'select(.direction == "received" and .frame.method and .frame.id)' acp-debug/<file>.jsonl ``` ### 4. Summarize for the user Give a concise markdown summary: agent, protocol version, models found (with currentModelId), modes found (with currentModeId), auth methods, any errors, and the JSONL path for deeper inspection. Example: ```text **auggie** (protocol v1, auggie 0.20.1) Models (11): claude-sonnet-4-6 (current), claude-opus-4-6, gpt-5-4, … Modes (2): default (current), ask Auth methods: (none advertised) JSONL: acp-debug/auggie-probe-20260409-183104.jsonl ``` ### 5. If something looks wrong, walk the frames Common failure modes: | Symptom | Likely cause | Next step | |---|---|---| | meta close `exit_code: 127` immediately | child binary not installed | Check `which <cmd>`; suggest install command | | meta close before any `received` frame | child crashed on startup | Re-run with `--stderr` to capture the error | | `initialize` response has populated `authMethods` but session/new fails | auth required | Surface the auth method ids; suggest setting env var / running CLI login | | `session/new` hangs (context deadline exceeded) | agent waiting on an unanswered agent-initiated request | Check JSONL for `received` frames with `method` + `id` that we auto-replied to with `method not found` — the agent may be retrying | | Response has no `models` / `modes` fields | agent doesn't expose them over ACP | Not a bug — document the gap | Re-run with `--stderr` whenever the child exits before the handshake completes; the stderr lines land in the JSONL and usually contain the root cause. ### 6. For `matrix`, read `matrix-summary.json` too ```bash jq '.' acp-debug/matrix-summary.json ``` One entry per agent with `status`, `models_count`, `current_model_id`, `auth_methods_count`, `duration_ms`, and `jsonl` (the full per-agent JSONL file). Useful for answering "which agents succeeded / failed / need auth" without re-reading every JSONL. ## What this skill does NOT do - No interactive UI. For ad-hoc side-by-side comparison, read the `matrix-summary.json` output or build a separate visualization tool on top of the JSONL. - No permission-request handling beyond canned replies. Agent-initiated requests (`fs/read_text_file`, `session/request_permission`, etc.) are answered with `-32601 method not found` so the session doesn't hang. If you need to exercise a real permission flow, use the full kandev backend. - No automatic credential bootstrap. The child inherits the parent shell's env; if an auth check fails the skill reports which methods were advertised and lets the user fix their local credentials. - No Docker / remote executor support. Standalone subprocess only — same as a manual `auggie --acp` invocation.
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