v0.5.0 — efficiency suite

v0.5.0 — efficiency suite

Three new tools to make your agent stack cheaper and more reliable.

av loops — doom-loop detection (content-aware, v0.5.1 fix)

Scans every session for repeated identical tool calls and file edits.

• Tool loops: exact same Bash command string repeated 20+ times.
• File loops: exact same Edit (same old_string + new_string) applied 10+ times.
• Polling commands excluded: ps, pgrep, pidof, /proc checks are not flagged. Waiting for a process to exit is legitimate, not a loop.
• Content-aware: file edits are compared by actual content, not just count. Progressive changes to the same file are not flagged.

Verified against your actual session data: 0 false positives. Previously flagged sessions (pipeline.py, sterling_auto.py, chatly-server.js) were all progressive edits, not loops. The highest tool_repeat was 28 ps polls of a mempalace repair process — correctly excluded.

av unused — registered-but-unused MCP tool detector

Every registered MCP tool ships its JSON schema in every agent turn. GitHub measured 8–12KB of pure overhead per turn from tools the agent never uses. Cross-references registered tools against session data to find the dead weight.

av cost now reports Effective Tokens (ET)

Using GitHub's formula from their token-efficiency post:

ET = m * (1.0*I + 0.1*C + 1.0*W + 4.0*O)

Where m is model multiplier (Haiku 0.25x, Sonnet 1x, Opus 5x), I is input, C is cache_read, W is cache_write, O is output. Output is weighted 4x (most expensive across all providers); cache_read is 0.1x (served from cache at fraction of the cost).

Stats

• 14 CLI commands
• 5 MCP tools
• ~2700 LOC
• 203 tests across 8 modules
• MIT