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.
npx agentmods add skills/dmatut7/codex-flow/codex-skillnpx skills add Dmatut7/codex-flow --skill codex-skillgit clone --depth 1 https://github.com/Dmatut7/codex-flowWhat 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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00139 | $0.00990 |
| Opus 5 | $0.00069 | $0.00495 |
| Sonnet 5 | $0.00028 | $0.00198 |
| Haiku 4.5 | $0.00014 | $0.00099 |
Grade B, and why
dynamic-workflow scanned grade B with 1 finding 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 2d ago.
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.
Subtle steeringmediumPrompt injection
Instructions that bias recommendations or shape behaviour without the user noticing.
- **Membership, not API key.** Default backend is `codex-sdk` (the logged-in Codex/ChatGPT account). Never tell the user to set `OPENAI_API_KEY` unless they explicitly ask for the `openai-responses` backend. How it starts
The opening of the file, as written. The whole thing — 49 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Dynamic Workflow
You are the natural-language driver for the codex-flow workflow engine. When this skill triggers, you turn the user's request into a small workflow that fans work out across parallel Codex sub-agents, runs it, and reports a summary. The user should NEVER have to hand-write a workflow file or remember commands.
When to actually use it
Use a dynamic workflow when the task is multi-step, parallelizable, or repeated over many items, e.g.:
- "排查这个 bug" over several files/hypotheses
- "review/analyze these N things" (one sub-agent per thing, in parallel)
- "do X then verify X for each item" (a per-item pipeline)
- anything long enough that resume-after-interrupt matters
If it's a single trivial question or one tiny edit, just answer/do it directly — do not spin up a workflow.
Steps
-
Preflight if needed. If this is the first workflow in the project or the CLI availability is unclear, run
codex-flow doctor. Ifcodex-flowis missing, tell the user to install it withnpm install -g codex-flowand stop. If npm is unavailable, usenpm install -g github:Dmatut7/codex-flowas a fallback. -
Restate the goal in one line. If the input material or output shape is genuinely missing, ask at most ONE necessary question. Otherwise infer and proceed.
-
Generate a workflow file at
.codex-flow/generated/<slug>.workflow.ts(create dirs as needed). Followreferences/engine-api.mdEXACTLY:export default async function workflow(ctx) { ... }- Import-free: no
importlines. Use plain JSON Schema objects for structured output (NOT zod), because the file runs from the user's project where extra deps may not exist. - Express the work with
ctx.parallel(independent fan-out),ctx.pipeline(per-item multi-stage),ctx.phase,ctx.agent,ctx.log. Onectx.agent(...)call = one sub-task. Don't write one giant single-agent prompt that bypasses the engine.
-
Run it (this uses the user's Codex/ChatGPT membership login — no API key needed):
codex-flow run .codex-flow/generated/<slug>.workflow.tsJournal is written to
.codex-flow/journal/<slug>.jsonlautomatically. If the run is interrupted, the same command resumes and replays completed work for free.
What ships with it
3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 2d ago First seen · 49 lines · 0 tokens per session scan B 5d597c1cbd75
dynamic-workflow is a skill published in the GitHub repository Dmatut7/codex-flow (9 stars, last pushed 2mo ago), licensed MIT. It adds 139 tokens to every session and 990 once invoked, about $0.0007 per session on Opus 5. A static security scan graded it B with 1 finding (subtle steering). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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