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/codex-vertical-workflows

Execute John's high-volume vertical phases with native Codex subagents over the durable `.john/runs` and event contracts. Use for per-chunk extraction, coverage, grounding, per-entry generation, large uniform fan-out, retries, reconciliation, cancellation, or whenever shared

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joharnessburg
928 skills5 agents5 commands
Install
$ npx -y skills add kitchen-engineer42/joharnessburg --skill codex-vertical-workflows --agent claude-code

How it fires

How this skill gets triggered: by you, by Claude, or both.

  • Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
  • You can call itInvoke it directly when you want it.
  • Slash command/codex-vertical-workflows

Context preview

The summary Claude sees to decide when to auto-load this skill.

Execute John's high-volume vertical phases with native Codex subagents over the durable `.john/runs` and event contracts. Use for per-chunk extraction, coverage, grounding, per-entry generation, large uniform fan-out, retries, reconciliation, cancellation, or whenever shared

SKILL.md

codex-vertical-workflows.SKILL.md
name: codex-vertical-workflows
description: Execute John's high-volume vertical phases with native Codex subagents over the durable `.john/runs` and event contracts. Use for per-chunk extraction, coverage, grounding, per-entry generation, large uniform fan-out, retries, reconciliation, cancellation, or whenever shared John guidance mentions Claude dynamic workflows but the active provider is Codex.

Codex vertical workflows

Keep the phase contract provider-neutral: immutable indexed inputs, stable item IDs, typed receipts, unique events, audit barriers, and a verified checkpoint. Use `john_run.py` to create and reconcile the durable ledger before treating agent returns as completion.

1. Create the run from a deterministic work CSV. 2. Dispatch ordinary native subagents in bounded waves; default concurrency is 6 and workers remain leaves (`max_depth=1`). 3. Use experimental `spawn_agents_on_csv` only when capability detection says it is available and the rows are uniform. Both engines consume the same manifest and receipt contract. 4. Record every attempt receipt, then reconcile. A successful thread is not a completed item until its referenced events exist, parse, match run/item identity, and contain the required terminal event. 5. Run extraction verification, coverage, grounding, adjudication, and typed reduction as separate stages with barriers. For extraction, invoke `reduce_events.py --require-extraction-audits` before phase advancement.

Use `john_run.py status`, `retry-csv`, and `cancel` for recovery. Never assign deterministic indexing to one agent, and never let parallel code workers edit the same files without disjoint ownership or an isolated worktree.

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Ships withjoharnessburg

中文版: README_ZH.md John turns unstructured source material into a working knowledge-dense app. It keeps knowledge engineering and app building in one durable run, coordinates large per-entry fan-outs, and leaves auditable events and checkpoints on disk.

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