Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/SteveGJones/ai-first-sdlc-practicesnpx agentmods add skills/stevegjones/ai-first-sdlc-practices/author-workflowWrote 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/stevegjones/ai-first-sdlc-practices/author-workflow)<a href="https://agentmods.dev/skills/stevegjones/ai-first-sdlc-practices/author-workflow"><img src="https://agentmods.dev/badge/skills/stevegjones/ai-first-sdlc-practices/author-workflow/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/stevegjones/ai-first-sdlc-practices/author-workflow"><img src="https://agentmods.dev/badge/skills/stevegjones/ai-first-sdlc-practices/author-workflow.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00036 | $0.02868 |
| Opus 5.5 | $0.00014 | $0.01147 |
| Sonnet 5.5 | $0.00007 | $0.00574 |
| Haiku 4.5 | $0.00004 | $0.00287 |
Grade A, and why
author-workflow 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 1mo 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.
How it starts
The opening of the file, as written. The whole thing — 299 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Author Workflow
This skill owns workflow + team-formation decisions for a task. Use it both when the user has a concrete task ("add OAuth2 auth") and needs a recommendation, and when they want to build a brand-new workflow.
Modes (pass exactly one flag)
Argument autocomplete does not show flag variants, so the mode menu is always in the body — pick one before proceeding.
--for-task "<description>"— start here when the user describes a task, not a pipeline. Recommend which existing workflow fits and which teams should be on which nodes; fall back to--newonly if nothing fits. No files written unless you actually author a workflow.--new [description]— start here when the user wants a pipeline that does not yet exist. Guided authoring: DAG design, workflow YAML, command prompts, validation, commit.
If invoked with no flag and no clear signal, ask the user which mode
fits. Defaulting silently to --new has the user author a workflow
when an existing one would have done the job.
Context
Load CLAUDE-CONTEXT-workflows.md before either mode proceeds — it
contains the workflow YAML schema, team manifest schema, command
prompt format, and all validation rules.
Mode: --for-task "<description>"
Step 1: Analyse the task
Read the task description. List current teams and workflows:
ls .archon/teams/*.yaml 2>/dev/null
ls .archon/workflows/*.yaml 2>/dev/null
For each existing workflow, extract its purpose (the description:
field) and its node list (the nodes: array). For each team, extract
its agent roster.
Step 2: Recommend a formation
Recommend a workflow + team-to-node mapping. Worked example:
Task: "Add OAuth2 authentication to the API"
Recommended formation:
Workflow: sdlc-feature-development
Node Team Notes
──────────── ──────────────────────── ────────────────────
plan general-purpose architecture + planning
implement dev-team-python primary dev team
validate dev-team-python validation pipeline
security security-review-team auth = security-sensitive
architecture general-purpose architecture review
quality general-purpose code quality review
synthesise general-purpose unified summary
This task involves authentication, so I've included the
security-review-team on the security review node.
(a) Accept and run this now
(b) Modify — change team assignments
(c) Show alternative workflow templates
(d) No existing workflow fits — switch to --new mode
(e) Skip — I'll assign teams myself
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.
- 1mo ago First seen · 299 lines · 36 tokens per session scan A 9856bdc16b4c
author-workflow is a skill published in the GitHub repository SteveGJones/ai-first-sdlc-practices (41 stars, last pushed 15d ago), licensed MIT. It adds 36 tokens to every session and 2,868 once invoked, about $0.0001 per session on Opus 5.5. 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 2026-09-03.
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