Create a new automated routine (ADW) for the scheduler. Guides the user through defining what the routine does, the type (AI or systematic), the schedule, and generates the Python script + Makefile target.
SkillScore86Maintained
Automatic
◆DevOps & SRE pack
Last verified 8/19/2026
What it changes
Passed 18 of 21 test cases with the skill, and 4 of 21 without it.
Without skill18%
With skill86%
Checks it passed that the model alone missed
Records cost equal to 0.
Executes make run R=log-cleanup.
Calls run_claude from runner.py.
and 19 more in the full report
15 fixed4 passed either way3 still failing0 broke
Pass-rate lift
+68 pp
Tokens
+55%
Agent turns
0%
Response time
-58%
Model gemini-3.6-flashMeasured 8/19/2026v119 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 15 test cases the skill fixed. Same model, same prompt, with and without the skill.
1 of 15
Prompt
I just finished writing a custom routine with script identifier `log-cleanup`. What command line call runs this specific routine manually using the project automation helper?
What was checked, without and with the skill
Checked
Without skill
With skill
Executes make run R=log-cleanup.
Missed
Passed
case-13 · gemini-3.6-flash · 8/19/2026 · answers are capped and redacted
What's inside
The instructions your model reads when the skill loads.
SKILL.md209 linessingle file
Create Custom Routine
Guide the user through creating a new automated routine that runs on schedule via the EvoNexus scheduler.
What You're Building
A routine is a Python script in ADWs/routines/custom/ that runs on a schedule (daily, weekly, monthly, or interval). There are two types:
AI routines — invoke Claude Code CLI with an agent to perform reasoning tasks (reports, analysis, decisions). Cost tokens and ~30-120s per run.
Systematic routines — pure Python scripts that perform deterministic operations (API calls, file ops, data transforms). No AI, no tokens, no cost, ~1-5s per run.
Step 1: Understand the Task
Ask the user:
What should this routine do? (e.g., "check my GitHub repos every morning", "ping API endpoints every 5 minutes")
AI or systematic? Help the user decide:
Use AI when: the task needs reasoning, analysis, writing, or decisions (generate a report, analyze sentiment, summarize data, make recommendations)
Use systematic when: the task is deterministic and repeatable (HTTP health checks, file cleanup, data snapshots, metric logging, backups, CSV exports)
When should it run? (daily at X, every N minutes, weekly on day, monthly on day 1)
What output? (HTML report, markdown file, CSV, JSON, Telegram notification, log entry, or just action)
Pesquisa multi-fonte sobre pergunta ou tópico de cliente com atribuição de fontes. Use quando um cliente pergunta algo que precisa ser verificado, investigando se um bug foi reportado antes, verificando o que foi dito anteriormente a uma conta específica, ou coletando contexto antes de redigir uma resposta. / Multi-source research on a customer question or topic with source attribution. Use when a customer asks something you need to look up, investigating whether a bug has been reported before,