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/etr/groundwork/plan-tasknpx skills add etr/groundwork --skill plan-taskgit clone --depth 1 https://github.com/etr/groundworkWhat 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 | $0.00031 | $0.02693 |
| Opus 5 | $0.00015 | $0.01347 |
| Sonnet 5 | $0.00006 | $0.00539 |
| Haiku 4.5 | $0.00003 | $0.00269 |
Grade A, and why
plan-task 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.
How it starts
The opening of the file, as written. The whole thing — 248 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Task Planning Skill
Plans a task or feature by loading context, optionally clarifying requirements, spawning a Plan agent, and persisting the validated plan to .groundwork-plans/.
Token Discipline
This skill orchestrates planning workflows. Every turn re-reads the full context window, so unnecessary turns are expensive.
- No narration turns. Do not output text-only turns like "Let me load the task" or "Now I'll spawn the plan agent." Combine text with a tool call in the same turn, or skip the text entirely.
- Batch tool calls. When multiple tool calls are independent, issue them all in one turn.
- No waiting updates. Do not output "Waiting for results..." turns. Wait silently until results arrive.
- 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": "Planning benefits from consistent multi-domain reasoning.\n\nTo switch: cancel, run `/effort high` (and `/model sonnet` if on Haiku), then re-invoke.",
"header": "Effort check",
"options": [
{ "label": "Continue" },
{ "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.
Step 0: Resolve Project Context
Before loading specs, ensure project context is resolved:
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.
- yesterday First seen · 248 lines · 31 tokens per session scan A bd74ab03aac2
plan-task is a skill published in the GitHub repository etr/groundwork (42 stars, last pushed 20d ago), licensed MIT. It adds 31 tokens to every session and 2,693 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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