Turn a skill's recommendations into real, executed actions — open the tickets, file the issues, post the updates — safely: dry-run preview, risk-classified, approval-gated, then recorded back to the brain.
SkillScore91Maintained
Automatic
◆Project Management pack
Last verified 8/14/2026
What it changes
Passed 19 of 21 test cases with the skill, and 6 of 21 without it.
Without skill27%
With skill91%
Checks it passed that the model alone missed
Classifies merging PR #102 as high risk.
Classifies sending an email as high risk.
Classifies Jira status updates as medium risk.
and 18 more in the full report
15 fixed5 passed either way1 still failing1 broke
Pass-rate lift
+64 pp
Tokens
+57%
Agent turns
0%
Response time
-30%
Model gemini-3.6-flashMeasured 8/14/2026v120 of 22 cases passed with skill · 21 comparable
Tokens, turns and time describe resource use, not a dollar price.
See it in action
One of 14 test cases the skill fixed. Same model, same prompt, with and without the skill.
1 of 14
Prompt
Show the command-line invocation used to run the action preview script when dry-running a JSON action plan before any execution occurs.
What was checked, without and with the skill
Checked
Without skill
With skill
Uses python3 scripts/action_preview.py - to process the JSON action plan.
Missed
Passed
case-12 · gemini-3.6-flash · 8/14/2026 · answers are capped and redacted
What's inside
The instructions your model reads when the skill loads.
SKILL.md99 lines2 filesincludes scripts
Action Runner Skill
The library is great at recommending work. This skill executes it — the action layer of the
Professional Brain (Phase 2). A skill says "open a ticket per
checklist item"; this turns that into real GitHub/Linear/Slack actions, safely: previewed,
risk-rated, approved, then recorded. The cardinal rule: nothing acts silently.
What This Skill Produces
A dry-run actions plan — every proposed action with its target, operation, and risk.
After approval, the executed actions (via the connected action MCP) — outbound/destructive
ones gated individually.
A record back to the brain of what was actually done, with provenance.
Required Inputs
Ask for (if not already provided):
The recommendations to act on (a launch checklist, PRD requirements, postmortem follow-ups…).
The connected action MCP and targets — which GitHub repo / Linear project / Slack channel. Scope is limited to what the user names; never act outside it.
Approval posture — what may run with a single OK vs. what needs per-action confirmation.
How it works
recommend → build an actions plan (JSON) → preview + risk-gate → approve → execute → record
Build the plan — express each action as JSON: {"target","op","args","why","risk?"}.
Preview + gate — run the helper; it prints a dry-run, classifies risk (🟢 low / 🟡 medium /
🔴 high), and refuses to proceed while any 🔴 outbound/destructive action is unapproved:
echo '<plan json>' | python3 scripts/action_preview.py -
# after the user approves the risky ones:
echo '<plan json>' | python3 scripts/action_preview.py - --allow-high
Approve — low/medium can run on a single confirmation; every 🔴 (post, send, delete, deploy,
merge, charge…) needs explicit per-action approval. Default is do nothing until told.
Execute — only approved actions, only via the connected action MCP (e.g. Composio/GitHub
create_issue). One target at a time; stop and report on the first failure.
Record — append what was actually done to the brain so the loop closes: