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 commands/parkerm2/create-claude-workflow/agent-teamgit clone --depth 1 https://github.com/ParkerM2/create-claude-workflowWhat 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 | $0.00033 | $0.00588 |
| Opus 5 | $0.00016 | $0.00294 |
| Sonnet 5 | $0.00007 | $0.00118 |
| Haiku 4.5 | $0.00003 | $0.00059 |
Grade A, and why
agent-team 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 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.
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 — 63 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/agent-team — Execute Pre-Planned Feature
How This Works
Each step below is a skill invocation. The skill loads its own focused instructions when you call it. You follow those instructions, complete them, then return here and check the box before moving to the next step.
Rules:
- Invoke each skill with the
Skilltool before doing anything else in that step - Do NOT read
agents/team-leader.mdor anyprompts/implementing-features/*.mddirectly - Each skill verifies the previous step completed — if a gate fails, fix it before continuing
- Do NOT skip steps, combine steps, or substitute your own instructions
Execution Checklist
-
Step 1 — Pre-flight
Skill("claude-workflow:wf-preflight")Verifies infrastructure, git state, config, and task files. Writes the preflight stamp. -
Step 2 — Load Plan
Skill("claude-workflow:wf-plan")Reads task files, validates structure, builds wave plan. Requires preflight stamp. -
Step 3 — Team Setup
Skill("claude-workflow:wf-setup")TeamCreate, runtime values injected into task files, worktrees created, CLAUDE.md injected per worktree. Requires plan stamp. -
Step 4 — Execute Waves (repeat for each wave)
-
Skill("claude-workflow:wf-spawn")Spawns coder + QA pair for each task in current wave. -
Skill("claude-workflow:wf-qa-gate")Waits for verdicts, handles QA cycles, merges passing tasks. Repeat Steps 4a–4b until all waves complete.
-
-
Step 5 — Guardian
Skill("claude-workflow:wf-guardian")Structural integrity check on the feature branch. Requires all-waves-complete stamp. -
Step 6 — Finalize
Skill("claude-workflow:wf-finalize")Shutdown agents, cleanup worktrees, push branch, create PR, report to user. Requires guardian-passed stamp.
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 · 63 lines · 33 tokens per session scan A 7915f77aee17
agent-team is a command published in the GitHub repository ParkerM2/create-claude-workflow (4 stars, last pushed 5mo ago), licensed MIT. It adds 33 tokens to every session and 588 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 2026-08-31.
Other commands, from other repositories
git
Git operations with intelligent commit messages and workflow optimization.
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.