Shark Autotune

Shark Autotune

Analyse recorded shark timing data and recommend optimal settings.

Instructions

1. Read $SKILL_DIR/../state/timings.jsonl — each line is a JSON object:

``json {"ts":1710000000,"loop":1,"elapsed_s":12.3,"timeout_s":25,"result":"ok|timeout|done","task_hash":"abc123"} ``

1. If the file doesn't exist or is empty, report "No timing data yet. Run tasks with /shark first to collect data."

1. Compute and report:

• Total runs (unique task_hash values) and total loops
• Median turn time (p50) and p95 turn time
• Timeout rate — % of turns with result "timeout"
• Loops to completion — median and max (count loops per task_hash that has a "done" entry)
• Wasted headroom — sum of (timeouts - elapseds) for result "ok" turns
• Optimal timeout — p95 turn time + 3s buffer, rounded up to nearest 5s
• Optimal max_loops — p95 loops-to-completion + 2

1. Show recommendations:

``` Current: SHARKLOOPTIMEOUT=25 SHARKMAXLOOPS=50 Recommended: SHARKLOOPTIMEOUT=N SHARKMAXLOOPS=M

Rationale:

• p95 turn time is Xs, so timeout of Ns covers 95% with buffer
• p95 completion is N loops, so max_loops of M gives safe margin
• Timeout rate is X% — [>15%: consider splitting tasks | healthy]
• Wasted headroom: Xs total

```

1. If timeout rate > 30%: "Consider breaking tasks into smaller steps."
2. If median turn time < 5s: "Most turns complete fast. Consider lowering timeout."

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: keugenek
• Source: keugenek/shark
• License: Apache-2.0

Install and usage instructions live in the source repository linked above.