Automating Your Automations

SkillMonitoring & ops

Mine atuin history and shell logs to find repetitive workflows, build Rust CLI or bash automations. Use when analyzing command patterns or finding automation opportunities.

Available today. Use it from your connected AI after setup.

Connect ahel once, and every AI you use reads what you have installed.

Then ask your AI: use the Automating Your Automations skill

What this skill tells your AI

The instructions your AI receives, as published by lev-os/agents in skills-db/_todo/automating-your-automations/SKILL.md and read by ahel’s review.

Core Insight: Your command history is a fossil record of automation opportunities. Every repeated multi-step workflow is a Rust CLI waiting to be born.

PrincipleImplication
Frequency x Pain = Priority3-command sequence 50x/week > 10-command sequence 1x/month
Complex → Rust; simple → bashState, error handling, SQLite → Rust CLI. Glue → bash

The Loop (Mandatory)

1. MINE      → atuin DB, zsh_history, systemd timers, journalctl
2. CLUSTER   → semantic similarity + temporal adjacency
3. SCORE     → frequency × time_saved × error_rate
4. PROPOSE   → Rust CLI vs bash script vs alias vs systemd timer
5. BUILD     → /rust-cli-with-sqlite or bash in ~/.local/bin/
6. VALIDATE  → 5x run, --dry-run, --robot JSON, ≥3x faster
7. INSTALL   → systemd timer, shell alias, or PATH binary

Never skip 1-3. Intuition about what's repetitive is unreliable; data wins.


Step 1: Mine

Data Sources

SourceLocationUnique Value
Atuin DB~/.atuin/history.dbDuration, exit codes, cwd, session grouping
zsh_history~/.zsh_historyTimestamps in extended format
Systemd~/.config/systemd/user/*.timerExisting automation to audit
Journalctljournalctl --userFailure patterns
bash/git/cron~/.bash_history, git log, crontab -lFallback sources

If ~/.atuin/history.db is missing, check XDG-style paths such as ~/.local/share/atuin/history.db before concluding Atuin is unavailable. If Atuin truly is unavailable, fall back to ~/.zsh_history / ~/.bash_history and downgrade confidence on timing/failure-rate scoring.

Essential Atuin Queries

Open read-only: sqlite3 -readonly ~/.atuin/history.db

If that path does not exist, discover the DB first:

find ~ ~/.local/share -maxdepth 3 \( -name 'history.db' -o -name '*atuin*' \) 2>/dev/null

Atuin stores timestamp and duration in nanoseconds. Exit 0 = success.

-- Top repeated commands
SELECT command, COUNT(*) as cnt, AVG(duration)/1e9 as avg_sec,
       SUM(CASE WHEN exit != 0 THEN 1 ELSE 0 END) as fails
FROM history GROUP BY command HAVING cnt > 5 ORDER BY cnt DESC LIMIT 30;

-- Multi-step workflows (commands within 10s in same session/cwd)
SELECT h1.command as step1, h2.command as step2, COUNT(*) as pair_cnt
FROM history h1 JOIN history h2
  ON h2.timestamp BETWEEN h1.timestamp AND h1.timestamp + 10000000000
  AND h1.cwd = h2.cwd AND h1.session = h2.session AND h1.id != h2.id
GROUP BY step1, step2 HAVING pair_cnt > 3
ORDER BY pair_cnt DESC LIMIT 20;

-- High-failure commands (retry/guard candidates)
SELECT command, COUNT(*) as total,
       ROUND(100.0 * SUM(CASE WHEN exit != 0 THEN 1 ELSE 0 END) / COUNT(*), 1) as fail_pct
FROM history GROUP BY command HAVING total > 5 AND fail_pct > 20
ORDER BY total DESC LIMIT 20;

Full cookbook (10 queries + export + analysis): ATUIN-QUERIES.md

THE EXACT PROMPT

Analyze my command history to find automation opportunities.

1. Query atuin DB: repeated commands, multi-step workflows,
   high-failure commands, time-of-day patterns, retry patterns
2. Inspect systemd user timers for gaps and silent failures
3. Score each pattern: frequency × time_saved × error_rate
4. Output top 10 candidates with: pattern, frequency, time estimate,
   recommended implementation (Rust/bash/alias/timer), complexity (S/M/L)

Step 2: Score

FactorWeightSource
Frequency40%COUNT(*) from atuin
Time saved30%AVG(duration) × frequency
Error rate20%fail_pct from atuin
Simplicity10%Inverse of implementation complexity
Score = freq_norm×0.4 + time_norm×0.3 + fail_norm×0.2 + simplicity_norm×0.1

Only automate if Score >= 0.3. Expanded 5-factor formula with normalization: PATTERN-DETECTION.md


Step 3: Propose

Stateful? (remembers across runs)
├─ YES → Rust CLI + SQLite (/rust-cli-with-sqlite)
│  ├─ Scheduled? → systemd timer
│  └─ Interactive? → clap subcommands + --robot JSON
└─ NO
   ├─ >3 steps? → bash script → ~/.local/bin/
   ├─ Retry/guard? → bash wrapper + set -euo pipefail
   └─ Single command? → shell alias → ~/.zshrc.local

Step 4: Build

Rust CLIs — every automation CLI must have:

  • --robot / --json for machine output
  • --dry-run for safe testing
  • Non-zero exit codes on failure

Scaffold template: RUST-SCAFFOLD.md

Bash scripts — always set -euo pipefail, install to ~/.local/bin/

Systemd timers — creation, activation, monitoring: SYSTEMD.md


Step 5: Validate

  • Run 5x, compare output to manual execution
  • --dry-run produces no side effects
  • --robot output parses as valid JSON
  • Automated ≥ 3x faster than manual
  • Force failure → verify graceful behavior

Anti-Patterns

Don'tInstead
Automate before measuringRun atuin queries first
Rust CLI for 2-line glueBash script or alias
Skip --dry-runAlways add dry-run mode
Ignore exit codesNon-zero exit + logging
Automate rare commands (Score < 0.3)Skip — maintenance > benefit
One giant scriptUnix philosophy: small composable tools

Integration

NeedSkill
Rust CLI + SQLite patterns/rust-cli-with-sqlite
Search prior agent sessions/cass
Turn automation into a skill/sc + /sw
Track automation tasks/br
Optimize slow commands/extreme-software-optimization

Reference Index

NeedRead
Full atuin query cookbook (10 queries)ATUIN-QUERIES.md
Shell history parsing (zsh/bash/fish)SHELL-HISTORY.md
Systemd timers: create, audit, monitorSYSTEMD.md
Rust CLI scaffold + Cargo.tomlRUST-SCAFFOLD.md
Real-world examples with scoresEXAMPLES.md
Clustering + scoring algorithmsPATTERN-DETECTION.md

Done When

  • Atuin DB queried for all pattern types
  • Top candidates scored and ranked
  • At least one automation built and validated
  • Automation installed (PATH, alias, or systemd timer)
  • Time savings measured

Signals

GitHub stars
22
Forks
2
Last commit
Sep 2026
Advanced
Catalog kind
skill
Gateway key
automating-your-automations
Source
github.com/lev-os/agents