terminal-agent-improvement-loop

name	terminal-agent-improvement-loop
description	Run a benchmark-driven improvement loop for Con's terminal agent. Use when iterating on pane awareness, SSH/tmux behavior, coding-cli flows, benchmark scoring, or progress tracking across many runs.

Terminal Agent Improvement Loop

Use this skill when improving Con as a terminal-native agent, not just fixing a one-off bug.

Primary references:

• benchmarks/terminal-agent/README.md
• docs/impl/terminal-agent-benchmark.md
• docs/impl/terminal-agent-improvement-loop.md

Workflow

1. Choose the smallest operator profile that matches the problem.
2. Run the benchmark on an idle tab:
   • python3 benchmarks/terminal-agent/run.py --profile operator-local-codex-devloop --suite operator
   • for repeated clean iterations, prefer python3 benchmarks/terminal-agent/iterate.py ...
3. Score the resulting run with the matching rubric:
   • python3 benchmarks/terminal-agent/score.py --profile ... --record ... --score ...
   • or ask the built-in agent to judge the raw record and transcript first: python3 benchmarks/terminal-agent/judge_llm.py --profile ... --record ... --socket /tmp/con.sock
   • then turn that judge artifact into a normal scorecard: python3 benchmarks/terminal-agent/score.py --profile ... --record ... --judge-file ...
4. Record one short summary, a few lessons, and a few next-focus bullets in the score record.
5. Append the scorecard to the tracked improvement log:
   • python3 benchmarks/terminal-agent/log_iteration.py --scorecard ... --change "..."
6. Make one focused product change.
7. Re-run the same operator profile.
8. Generate a report when you need to inspect trend:
   • python3 benchmarks/terminal-agent/report.py

Rules

• Use strict suites to protect the floor and operator suites to judge real workflows.
• Prefer operator profiles that start a fresh conversation and have bounded step timeouts.
• Prefer typed control-plane improvements over prompt-only fixes.
• Do not overfit to a single benchmark phrase or one host layout.
• Keep unknowns honest when the backend cannot prove more.
• When benchmark infra changes, say whether the product improved or the measurement improved.
• Keep iteration notes concise and comparable across runs.
• Keep docs/impl/terminal-agent-improvement-log.md useful to a human reader; it should explain what changed, not just repeat the numeric score.
• If you use the LLM judge, feed it the raw record and transcript, not only the generated report. The report is a summary, not primary evidence.