# Standup Bot — Scheduled Daily Standup Reporter
#
# An autonomous agent that runs on a recurring schedule to generate daily
# standup reports from git history. Ingests findings into the knowledge store
# and periodically consolidates daily entries into weekly summaries.
#
# ## Features Demonstrated
#
# - **Interval scheduling**: Runs every 24 hours (86400s), configurable
# - **Knowledge ingest/query/consolidate**: Full knowledge lifecycle
# - **Autonomous execution**: No user interaction needed per cycle
# - **Execution limits**: Bounded cost and steps per scheduled cycle
#
# ## Usage
#
#   # Start the dashboard (schedule is created via UI or API)
#   cargo run --example qmtcode --features dashboard -- confs/standup_bot.toml --dashboard
#
#   # Then create a schedule via the dashboard UI:
#   #   Session → Schedules → Create Schedule
#   #   Prompt: "Run a standup cycle: analyze recent git activity, ingest findings, and report."
#   #   Trigger: Interval, 86400 seconds (daily)
#   #   Max runs: 30 (one month trial)
#   #   Max steps: 20
#
#   # Or for testing with a short interval:
#   #   Trigger: Interval, 120 seconds (every 2 minutes)
#   #   Max runs: 5

[agent]
provider = "anthropic"
model = "claude-sonnet-4-5-20250929"
assume_mutating = false

# No mutating tools — this agent is strictly read-only + knowledge writes
mutating_tools = []

tools = [
  # Git & codebase analysis (read-only)
  "read_tool", "index", "glob", "search_text", "ls", "shell",

  # Knowledge lifecycle
  "knowledge_ingest",
  "knowledge_query",
  "knowledge_consolidate",
  "knowledge_list_unconsolidated",
  "knowledge_stats",

  # Task tracking
  "create_task", "read_task", "update_task", "complete_task", "todowrite", "todoread",
]

system = [
  { file = "../prompts/standup_bot.txt" },
  { file = "../prompts/code_meta.jinja2" }
]

# Snapshot: track what the agent reads (no writes expected)
[agent.execution.snapshot]
backend = "git"

# Compaction: tool output truncation (Layer 1)
[agent.execution.tool_output]
max_lines = 1000
max_bytes = 25600

# Compaction: pruning after every turn (Layer 2)
[agent.execution.pruning]
protect_tokens = 20000

# Compaction: AI summary on context overflow (Layer 3)
[agent.execution.compaction]
auto = true

# =============================================================================
# Middleware Stack
# =============================================================================

# Execution limits — keep scheduled cycles fast and cheap
[[middleware]]
type = "limits"
max_steps = 30
max_turns = 15

# Context management
[[middleware]]
type = "context"
warn_at_percent = 80
compact_at_percent = 90
fallback_max_tokens = 128000