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/review-prnpx skills add etr/groundwork --skill review-prgit 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 | $0.00040 | $0.04238 |
| Opus 5 | $0.00020 | $0.02119 |
| Sonnet 5 | $0.00008 | $0.00848 |
| Haiku 4.5 | $0.00004 | $0.00424 |
Grade A, and why
review-pr 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 — 421 lines — stays where its author put it; the contents beside it link to each section on GitHub.
PR Review Skill
Multi-agent PR review that runs specialized agents against PR changes and posts structured feedback to GitHub.
Non-Interactive Mode Detection
Check for non-interactive mode from either source:
- If the argument string contains
--no-interactive→ strip it from the argument before PR number parsing, setnon_interactive = true - If session context contains
GROUNDWORK_BATCH_MODE=true→ setnon_interactive = true
If either condition is met, all AskUserQuestion prompts below are replaced with their documented auto-decision — except the pre-flight model check, which always prompts.
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 → always prompt (even in non-interactive mode) using AskUserQuestion:
{
"questions": [{
"question": "Do you want to switch? Multi-agent orchestration and deduplication judgment across 6-8 agents benefits from consistent reasoning.\n\nTo switch: cancel, run `/effort high` (and `/model sonnet` if on Haiku), then re-invoke this skill.",
"header": "Recommended: Sonnet or Opus at high effort",
"options": [
{ "label": "Continue" },
{ "label": "Cancel — I'll switch first" }
],
"multiSelect": false
}]
}
If the user selects "Cancel — I'll switch first": output the switching commands above and stop. Do not proceed with the skill.
Step 1: Parse PR Identifier
Extract the PR number from the user's input. Accept any of these formats:
- Numeric:
42 - With hash:
#42 - Full URL:
https://github.com/owner/repo/pull/42
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 · 421 lines · 40 tokens per session scan A 41b518aab8c5
review-pr is a skill published in the GitHub repository etr/groundwork (42 stars, last pushed 21d ago), licensed MIT. It adds 40 tokens to every session and 4,238 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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