work-on

A task-execution workflow that uses separate worktrees, TDD, validation, and merging. TDD means writing tests before or alongside the code they check; a worktree is an isolated copy of a Git project.

In plain words
What is it for?
Use it to execute a numbered task or implement a task from start to finish with tests and validation.
Why use it?
It organizes implementation work into planning, coding, checking, and merge steps while keeping changes isolated from other work.

Skill for Claude CodeCodex

Install

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.

agentmods
npx agentmods add skills/etr/groundwork/work-on
Any agent
npx skills add etr/groundwork --skill work-on
Clone the repo
git clone --depth 1 https://github.com/etr/groundwork

Made for: Claude Code, Codex.

Per session 39 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 3,197 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5 $0.00039 $0.03197
Opus 5 $0.00019 $0.01598
Sonnet 5 $0.00008 $0.00639
Haiku 4.5 $0.00004 $0.00320

Measured yesterday against content hash 603cf6e3fb9c, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

work-on 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 yesterday.

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.

skills/work-on/SKILL.md · 302 lines

How it starts

The opening of the file, as written. The whole thing — 302 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Execute Task Skill

Thin orchestrator that delegates to plan-task, implement-task, and validate skills for the three phases of task execution.

Token Discipline

This skill orchestrates long multi-agent workflows. Every turn re-reads the full context window, so unnecessary turns are expensive.

  1. No narration turns. Do not output text-only turns like "Let me load the task" or "Now I'll launch the plan agent." Combine text with a tool call in the same turn, or skip the text entirely.
  2. Batch tool calls. When multiple tool calls are independent, issue them all in one turn.
  3. No waiting updates. Do not output "Waiting for results..." turns. Wait silently until results arrive.
  4. Keep context lean. Do not read file contents you won't use directly. Pass file paths to subagents and let them read in their own context windows.

Pre-flight: Model Recommendation

Your current effort level is {{effort_level}}.

Skip this step silently if effort is high, xhigh, or max (the scale is low < medium < high < xhigh < max, so xhigh and max are already above high) AND you are Sonnet or Opus. If effort is low or medium (i.e. below high), you MUST show the recommendation prompt — regardless of model. If you are not Sonnet or Opus, you MUST show the recommendation prompt — regardless of effort level.

Otherwise → use AskUserQuestion:

{
  "questions": [{
    "question": "Task execution benefits from consistent multi-domain reasoning.\n\nRecommended: Sonnet or Opus at high effort.\n\nTo switch: cancel, run `/effort high` (and `/model sonnet` if on Haiku), then re-invoke.",
    "header": "Effort check",
    "options": [
      { "label": "Continue anyway" },
      { "label": "Cancel — I'll switch first" }
    ],
    "multiSelect": false
  }]
}

If the user selects "Cancel — I'll switch first": output the switching commands and stop. Do not proceed with the skill.

Plan Mode Handling

  1. Execute Steps 0-2 below as normal (they are read-only — planning produces a plan file).
  2. After Step 2 produces the plan file, write the plan mode output:

Read the full file on GitHub · 302 lines

Changes

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.

  1. yesterday First seen · 302 lines · 39 tokens per session scan A 603cf6e3fb9c

Subscribe to this mod's changes

work-on is a skill published in the GitHub repository etr/groundwork (42 stars, last pushed 20d ago), licensed MIT. It adds 39 tokens to every session and 3,197 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 2026-08-30.

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