debug

A troubleshooting guide for finding why Symphony and Codex runs become stuck, repeatedly retry, or fail unexpectedly by tracing their logs.

In plain words
What is it for?
Investigating stalled runs, correlating issue and session identifiers, and checking runtime logs from start through completion or failure.
Why use it?
It provides a consistent way to connect a project issue to its run and identify whether the problem is a timeout, startup failure, turn failure, or another execution error.

Skill for Claude CodeCodex

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add skills/openai/symphony/debug
Any agent
npx skills add openai/symphony --skill debug
Clone the repo
git clone --depth 1 https://github.com/openai/symphony

Made for: Claude Code, Codex.

Per session 33 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,073 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5 $0.00033 $0.01073
Opus 5 $0.00016 $0.00536
Sonnet 5 $0.00007 $0.00215
Haiku 4.5 $0.00003 $0.00107

Measured yesterday against content hash f043ad96235e, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

debug 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 yesterday.

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.

Origin

Copies of this mod

2 near-identical copies found in the catalogue:

  • debug — 100% identical, 0 lines differ
  • debug — 100% identical, 0 lines differ
.codex/skills/debug/SKILL.md · 119 lines

How it starts

The opening of the file, as written. The whole thing — 119 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Debug

Goals

  • Find why a run is stuck, retrying, or failing.
  • Correlate Linear issue identity to a Codex session quickly.
  • Read the right logs in the right order to isolate root cause.

Log Sources

  • Primary runtime log: log/symphony.log
    • Default comes from SymphonyElixir.LogFile (log/symphony.log).
    • Includes orchestrator, agent runner, and Codex app-server lifecycle logs.
  • Rotated runtime logs: log/symphony.log*
    • Check these when the relevant run is older.

Correlation Keys

  • issue_identifier: human ticket key (example: MT-625)
  • issue_id: Linear UUID (stable internal ID)
  • session_id: Codex thread-turn pair (<thread_id>-<turn_id>)

elixir/docs/logging.md requires these fields for issue/session lifecycle logs. Use them as your join keys during debugging.

Quick Triage (Stuck Run)

  1. Confirm scheduler/worker symptoms for the ticket.
  2. Find recent lines for the ticket (issue_identifier first).
  3. Extract session_id from matching lines.
  4. Trace that session_id across start, stream, completion/failure, and stall handling logs.
  5. Decide class of failure: timeout/stall, app-server startup failure, turn failure, or orchestrator retry loop.

Commands

# 1) Narrow by ticket key (fastest entry point)
rg -n "issue_identifier=MT-625" log/symphony.log*

# 2) If needed, narrow by Linear UUID
rg -n "issue_id=<linear-uuid>" log/symphony.log*

# 3) Pull session IDs seen for that ticket
rg -o "session_id=[^ ;]+" log/symphony.log* | sort -u

# 4) Trace one session end-to-end
rg -n "session_id=<thread>-<turn>" log/symphony.log*

# 5) Focus on stuck/retry signals
rg -n "Issue stalled|scheduling retry|turn_timeout|turn_failed|Codex session failed|Codex session ended with error" log/symphony.log*

Investigation Flow

  1. Locate the ticket slice:
    • Search by issue_identifier=<KEY>.
    • If noise is high, add issue_id=<UUID>.
  2. Establish timeline:
    • Identify first Codex session started ... session_id=....
    • Follow with Codex session completed, ended with error, or worker exit lines.
  3. Classify the problem:
    • Stall loop: Issue stalled ... restarting with backoff.
    • App-server startup: Codex session failed ....
    • Turn execution failure: turn_failed, turn_cancelled, turn_timeout, or ended with error.
    • Worker crash: Agent task exited ... reason=....
  4. Validate scope:
    • Check whether failures are isolated to one issue/session or repeating across multiple tickets.
  5. Capture evidence:
    • Save key log lines with timestamps, issue_identifier, issue_id, and session_id.
    • Record probable root cause and the exact failing stage.

Read the full file on GitHub · 119 lines

Changes

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.

  1. yesterday First seen · 119 lines · 33 tokens per session scan A f043ad96235e

Subscribe to this mod's changes

debug is a skill published in the GitHub repository openai/symphony (26,941 stars, last pushed 12d ago), licensed Apache-2.0. It adds 33 tokens to every session and 1,073 once invoked, about $0.0002 per session on Opus 5. 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.

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