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/agentculture/learn-cli/assign-to-workforcenpx skills add agentculture/learn-cli --skill assign-to-workforcegit clone --depth 1 https://github.com/agentculture/learn-cliWrote 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/agentculture/learn-cli/assign-to-workforce)<a href="https://agentmods.dev/skills/agentculture/learn-cli/assign-to-workforce"><img src="https://agentmods.dev/badge/skills/agentculture/learn-cli/assign-to-workforce.svg" alt="Measured on agentmods" 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 | $0.00188 | $0.02739 |
| Opus 5 | $0.00094 | $0.01370 |
| Sonnet 5 | $0.00038 | $0.00548 |
| Haiku 4.5 | $0.00019 | $0.00274 |
Grade A, and why
assign-to-workforce 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 4d 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 assign-to-workforce — 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 — 243 lines — stays where its author put it; the contents beside it link to each section on GitHub.
assign-to-workforce — fan out a converged plan's waves to parallel agents
The skill is named assign-to-workforce; the product/CLI it reads is the
devague plan waves command. (The prior leg — turning a spec into a plan —
is the sibling /spec-to-plan skill.)
assign-to-workforce takes a converged devague plan and fans out its
dependency waves to parallel agents (subagents, teammate agents, or generalist
agents) — one agent per task per wave — each working in an isolated git
worktree. The main agent merges each completed worktree gated by TDD. The
human owns exactly three gates: the exported spec, the implementation split
plan, and the final PR.
The devague CLI is never orchestrated by devague itself — devague plan waves describes the dependency graph (#20); it does not spawn agents, manage
worktrees, mark tasks done, or pick a backend. The fan-out is the operator's
job — this skill and the main agent perform it.
How to run
The entry point is scripts/assign-to-workforce.sh. Invoke it from the
repository whose plan you are implementing (plans persist under .devague/
in the current directory):
bash .claude/skills/assign-to-workforce/scripts/assign-to-workforce.sh split-plan [--plan <slug>]
bash .claude/skills/assign-to-workforce/scripts/assign-to-workforce.sh waves [--plan <slug>] [--json]
bash .claude/skills/assign-to-workforce/scripts/assign-to-workforce.sh help
It resolves the CLI portably — an installed devague on PATH (the normal
case), falling back to uv run devague when you are inside the devague
checkout, else an install hint. The split-plan subcommand reads
devague plan waves --json and renders the human-facing implementation split
plan: task map, proposed per-task agent + model assignment, and the go/no-go
question. The waves subcommand forwards to devague plan waves verbatim.
Usage
| Subcommand | What it does |
|---|---|
split-plan [--plan S] |
Read devague plan waves and print the implementation split plan — task map with per-task agent + model proposal — ready for human go/no-go review. |
waves [--plan S] [--json] |
Forward to devague plan waves [--json]. Read-only; lists wave batches. On a converged plan exits 0 listing the waves. |
help |
Print usage. |
What ships with it
1 file 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.
- 4d ago First seen · 243 lines · 188 tokens per session scan A b1be47f76664
assign-to-workforce is a skill published in the GitHub repository agentculture/learn-cli (1 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 188 tokens to every session and 2,739 once invoked, about $0.0009 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to assign-to-workforce, differing in 0 lines, and is treated as a copy.
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