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 skills add gabrielmoreira/agent-skills-mirror --skill writing-agent-relay-workflowsgit clone --depth 1 https://github.com/gabrielmoreira/agent-skills-mirrorWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/gabrielmoreira/agent-skills-mirror/writing-agent-relay-workflows)<a href="https://agentmods.dev/skills/gabrielmoreira/agent-skills-mirror/writing-agent-relay-workflows"><img src="https://agentmods.dev/badge/skills/gabrielmoreira/agent-skills-mirror/writing-agent-relay-workflows/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/gabrielmoreira/agent-skills-mirror/writing-agent-relay-workflows"><img src="https://agentmods.dev/badge/skills/gabrielmoreira/agent-skills-mirror/writing-agent-relay-workflows.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00092 | $0.16543 |
| Opus 5 | $0.00046 | $0.08271 |
| Sonnet 5 | $0.00018 | $0.03309 |
| Haiku 4.5 | $0.00009 | $0.01654 |
Grade A, and why
writing-agent-relay-workflows 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 7d 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.
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.
This is a copy
100% identical to writing-agent-relay-workflows — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 1,524 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Overview
The @relayflows/core workflow system orchestrates multiple AI agents (Claude, Codex, Gemini, Aider, Goose) through typed DAG-based workflows. Workflows can be written in TypeScript (preferred), Python, or YAML.
Language preference: TypeScript > Python > YAML. Use TypeScript unless the project is Python-only or a simple config-driven workflow suits YAML.
Pattern selection: Do not default to dag blindly. If the job needs a different swarm/workflow type, consult the choosing-swarm-patterns skill when available and select the pattern that best matches the coordination problem.
When to Use
- Building multi-agent workflows with step dependencies
- Orchestrating different AI CLIs (claude, codex, gemini, aider, goose)
- Creating DAG, pipeline, fan-out, or other swarm patterns
- Needing verification gates, retries, or step output chaining
- Designing product-contract workflows where failing checks should route to agents for repair instead of stopping the run
- Dynamic channel management: agents joining/leaving/muting channels mid-workflow
Non-Negotiable Workflow Checklist
Every generated workflow should satisfy this checklist before it is considered complete:
- Start with a deterministic, resumable preflight for repository state, credentials, and declared write scope.
- Pick the coordination shape deliberately: Conversation for non-trivial coordination, Pipeline only for linear one-shot handoffs.
- Use repairable validation gates: capture red output with
failOnError: false, hand it to a repair owner, then rerun the same check. - Run the mandatory fresh-eyes loops in order: Claude review/fix/final review/final fix, then Codex review/fix/final review/final fix.
- Require review fixers to add or update appropriate tests, fixtures, assertions, or deterministic proofs for testable findings.
- Run final deterministic acceptance after the Codex loop and before commit, PR creation, or handoff.
- If a real blocker remains, write
BLOCKED_NO_COMMITwith exact evidence and skip commit/PR creation instead of crashing the workflow. - If the workflow owns shipping, model branch, commit, push, PR creation, and PR URL verification as explicit deterministic steps.
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.
- 7d ago First seen · 1,524 lines · 92 tokens per session scan A 61b3cb5d500e
writing-agent-relay-workflows is a skill published in the GitHub repository gabrielmoreira/agent-skills-mirror (17 stars, last pushed yesterday), licensed MIT. It adds 92 tokens to every session and 16,543 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to writing-agent-relay-workflows, differing in 0 lines, and is treated as a copy.
Other skills, from other repositories
research-engineer
An uncompromising Academic Research Engineer. Operates with absolute scientific rigor, objective criticism, and zero flair. Focuses on theoretical correctness, formal verification, and optimal implementation across any required technology.
tika-eval-compare
Compare extracts from two Tika builds over a corpus to detect regressions in content, encoding, exceptions, and embedded-document handling. Use for "compare before/after extracts", "eval this change against the corpus".
neuron-evaluation-engineer
Create and run AI evaluations with datasets, assertions, and output drivers in Neuron AI. Use this skill whenever the user mentions evaluation, testing AI systems, creating evaluators, dataset-driven testing, assertion-based validation, or wants to measure AI system performance. Also trigger for tasks involving…
jetson-validate-image
Use after jetson-flash-image to run static BSP checks, on-target smoke/regression tests on a flashed DUT, or both. Not for build or flash steps. Triggers: validate bsp, on-target validation.
atmos-validation
Validate Atmos projects, components, arbitrary JSON Schema inputs, EditorConfig, and GitHub Actions; use affected-file selection and native CI annotations.
skill-benchmark
Benchmark AI skill effectiveness by measuring implementation quality against legacy constraints.