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Orient at the start of a coding session, review what recently changed in a repository, and self-audit after completing work.
Passed 15 of 19 test cases with the skill, and 8 of 19 without it.
and 10 more in the full report
8 fixed9 passed either way5 still failing0 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 7 test cases the skill fixed. Same model, same prompt, with and without the skill.
After running a session audit on repository 'pricing-engine', the status is review. What must be done with each row listed in top_changes before declaring the task done?
| Checked | Without skill | With skill |
|---|---|---|
| States that positive deltas in top_changes must be confirmed as intentional feature additions. | Missed | Passed |
| Demands justifying each complexity increase. | Missed | Passed |
The instructions your model reads when the skill loads.
Session bookends: orient before you start, audit before you finish. All three tools read the bi-temporal version history — every save is a change event with complexity deltas, so "what happened" includes "did it make the code better or worse".
| Tool | Purpose |
|---|---|
get_daily_briefing | Last 24h diffed at graph level: changed functions with complexity deltas, new functions, new endpoints, per-module distribution |
find_hotspots | Functions ranked by complexity × recent changes — the ordered refactor priority list |
review_agent_sessions | Editing sessions clustered by actor, judged clean / review / risky |
Parameter types: MCP parameters are strictly typed. Numbers
Full parameter spec for every Memtrace tool: references/mcp-parameters.md (bundled at the memtrace-skills plugin root).
(
window_hours,window_days,top_n) must be JSON numbers.
get_daily_briefing with repo_id. Read the summary first:
net_cyclomatic_delta positive → recent work added complexity; check
whether your task touches the same area before piling on.changed rows with large positive deltas → recently-destabilised code;
prefer preflight_check before editing anything in that list.find_hotspots — work the list top-down. A hotspot you simplify pays off
on every future change; a complex-but-untouched function can wait.
review_agent_sessions and find YOUR session (newest, your agent id):
clean — done; state the verdict in your summary.review — re-read your top_changes rows; justify each complexitytest cases passed, vs 8 / 19 without
More skills from the same source and nearby in the registry, with their headline benchmark lift.