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ChatStore = groups + append-only messages + per-member cursors; agent posts wake mentions only, user posts wake everyone; post_chat(record_on_item) also lands the answer as an item comment. Leads name workers (the callname is the handle everywhere); worker digests auto-carry the roster; gate checkbox is the user's call; canceling an assigned item now interrupts an in-flight worker.
366 lines
13 KiB
Python
366 lines
13 KiB
Python
"""Board and journal verbs as agent tools.
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The verbs are generic on purpose (the connector-dialect play): the local TeamStore is
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the default backing, and a Jira/Linear-backed dialect can implement the same tool
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surface later. Registration is gated by the persona's `team:` trait — a lead gets the
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full set, a worker gets the worker set, solo personas get none of this.
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The engine decides `taint` (whether this agent touched untrusted content this
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session) and passes it at construction — the model never self-reports provenance.
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"""
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from __future__ import annotations
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from typing import Callable, Optional
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import aisuite as ai
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from .journal import JournalStore
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from .model import Actor, BoardError, Role
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from .store import TeamStore
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LEAD_VERBS = ("create_item", "list_items", "transition", "comment", "assign", "link")
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# Workers file items too (a bug spotted in passing, a follow-up) — new items land
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# `open` and unassigned; nothing runs until the lead/user assigns them.
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WORKER_VERBS = ("create_item", "list_items", "transition", "comment")
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JOURNAL_VERBS = ("journal_append", "journal_read")
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# Explicit schema: the auto-generator's normalizer strips every `title` key to drop
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# pydantic metadata, which also deletes a PARAMETER named `title` from properties.
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# Registered via `__coworker_schema__` (same escape hatch as todo_write).
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_CREATE_ITEM_SCHEMA = {
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"type": "function",
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"function": {
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"name": "create_item",
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"description": (
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"Create a work item (open, unassigned — work starts when it is"
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" assigned). `criteria` is the acceptance criteria — what gets verified"
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" before the item can be done; required. `parent` links it under"
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" another item; `case` names its journal case (children inherit the"
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" parent's case by default)."
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),
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"parameters": {
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"type": "object",
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"properties": {
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"title": {"type": "string"},
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"criteria": {"type": "string"},
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"description": {"type": "string"},
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"parent": {"type": "integer"},
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"case": {"type": "string"},
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},
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"required": ["title", "criteria"],
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},
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},
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}
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def board_tools(
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store: TeamStore,
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*,
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space: str,
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actor: Actor,
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taint: Callable[[], bool] = lambda: False,
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) -> list:
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"""The board verbs for one agent, pre-bound to its space and identity.
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Authority is enforced twice on purpose: the returned set is role-filtered
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(a worker never even sees `assign`), and the store re-checks every call —
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the tool layer is convenience, the store is the gate.
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"""
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def create_item(
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title: str,
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criteria: str,
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description: str = "",
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parent: Optional[int] = None,
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case: str = "",
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) -> dict:
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"""Create a work item (open, unassigned — work starts when it is
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assigned). `criteria` is the acceptance criteria — what gets verified
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before the item can be done; required. `parent` links it under another
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item; `case` names its journal case (children inherit the parent's case
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by default)."""
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return _call(
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store.create_item,
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space,
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actor,
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title=title,
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criteria=criteria,
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description=description,
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parent=parent,
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case=case or None,
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)
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def list_items(state: str = "", assignee: str = "") -> dict:
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"""List work items on the board, optionally filtered by state
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(open/in_progress/blocked/review/done/canceled) or assignee."""
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try:
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return {"items": store.list_items(space, actor, state=state or None, assignee=assignee or None)}
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except (BoardError, ValueError) as error:
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return {"error": str(error)}
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def transition(
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item: int, to: str, comment: str = "", refs: Optional[list] = None
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) -> dict:
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"""Move a work item to a new state. Workers move their own item to
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in_progress, blocked, or review (attach the blocker or a hand-off summary
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as `comment`, and artifact pointers — branch, report, session — as
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`refs`); done requires review verification first."""
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return _call(
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store.transition,
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space,
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actor,
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item,
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to,
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comment=comment,
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refs=[str(ref) for ref in refs or []],
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taint=taint(),
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)
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def comment(item: int, body: str, refs: Optional[list] = None) -> dict:
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"""Add a comment to a work item. Comments are durable and attributed —
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answers that matter belong here, not in chat. `refs` attach artifact
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pointers (branch, PR, report, file:line) to the item."""
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return _call(
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store.comment,
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space,
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actor,
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item,
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body,
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refs=[str(ref) for ref in refs or []],
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taint=taint(),
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)
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def assign(item: int, assignee: str) -> dict:
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"""Assign a work item to a worker coworker. The item itself becomes the
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worker's assignment — write the description and criteria accordingly."""
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return _call(store.assign, space, actor, item, assignee)
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def link(src: int, kind: str, dst: int) -> dict:
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"""Link two work items: kind `parent` (dst becomes src's parent) or
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`blocks` (src blocks dst)."""
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return _call(store.link, space, actor, src, kind, dst)
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verbs = LEAD_VERBS if actor.role in (Role.USER, Role.LEAD) else WORKER_VERBS
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local = locals()
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out = []
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for name in verbs:
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wrapped = _wrap(local[name])
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if name == "create_item":
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wrapped.__coworker_schema__ = _CREATE_ITEM_SCHEMA
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out.append(wrapped)
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return out
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def journal_tools(
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journal: "JournalStore",
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*,
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actor: Actor,
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space: str = "",
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taint: Callable[[], bool] = lambda: False,
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) -> list:
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def journal_append(
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case: str,
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body: str,
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kind: str = "note",
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item: Optional[int] = None,
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entities: Optional[list] = None,
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refs: Optional[list] = None,
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) -> dict:
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"""Append an entry to a journal case as you work: kind is finding,
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evidence, decision, note (any observation), or raw (a capture like a log
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excerpt — for large captures, save the full output to a file and journal
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an excerpt that references it). `entities` are the concrete things it is
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about (file paths, resource names, CVE ids) — they power later recall;
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`refs` are pointers (file:line, commit, url)."""
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return _call(
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journal.append,
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actor,
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case,
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body,
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kind=kind,
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space=space or None,
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item=item,
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entities=[str(entity) for entity in entities or []],
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refs=[str(ref) for ref in refs or []],
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taint=taint(),
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)
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def journal_read(
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case: str,
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item: Optional[int] = None,
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author: str = "",
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kind: str = "",
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entity: str = "",
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include_raw: bool = False,
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limit: int = 50,
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) -> dict:
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"""Read a journal case, filtered: by item, author, entry kind, or entity.
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Prefer narrow filtered reads over pulling the whole case. Raw captures
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are skipped unless you pass include_raw or kind="raw"."""
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try:
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return {
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"entries": journal.read(
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actor,
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case,
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item=item,
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author=author or None,
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kind=kind or None,
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entity=entity or None,
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include_raw=include_raw,
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limit=limit,
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)
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}
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except (BoardError, ValueError) as error:
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return {"error": str(error)}
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local = locals()
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return [_wrap(local[name]) for name in JOURNAL_VERBS]
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# The staffing gate's schema carrier. Like propose_plan, the real handling lives in
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# the TurnEngine (it needs the out-of-band approval round-trip): it emits
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# TEAM_PROPOSED and waits; approval PRE-SPAWNS the worker sessions and returns the
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# roster (actor ids) to the lead. This body only runs when no approver is wired.
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_PROPOSE_TEAM_SCHEMA = {
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"type": "function",
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"function": {
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"name": "propose_team",
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"description": (
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"Propose the worker coworkers you need for this board. Give EACH member"
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" a short unique callname (`name`, e.g. 'nia', 'webb', 'checks') — it"
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" becomes their handle for assignment and @mentions, and lets you staff"
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" two of the same coworker. The user sees the roster and approves it;"
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" approval creates the worker sessions and returns the handles. Only"
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" team-capable worker coworkers may be proposed."
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),
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"parameters": {
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"type": "object",
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"properties": {
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"members": {
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"type": "array",
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"items": {
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"type": "object",
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"properties": {
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"persona": {"type": "string"},
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"name": {"type": "string"},
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"model": {"type": "string"},
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"reason": {"type": "string"},
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},
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"required": ["persona", "name"],
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},
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},
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"enable_chat": {"type": "boolean"},
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"note": {"type": "string"},
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},
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"required": ["members"],
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},
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},
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}
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# The decomposition gate's schema carrier — the board-flavored sibling of
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# propose_plan, usable in ANY permission mode (proposing costs nothing; the board
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# only ever holds accepted work). The engine intercepts it; approval creates the
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# items and returns their ids.
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_PROPOSE_ITEMS_SCHEMA = {
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"type": "function",
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"function": {
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"name": "propose_work_items",
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"description": (
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"Present your decomposition to the user as proposed WORK ITEMS for the"
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" team board. Approval creates them on the board (ids come back in the"
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" result); rejection returns feedback to revise. Each item needs a"
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" title and acceptance criteria — what gets verified before it can be"
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" done. This is not propose_plan: it carries no implementation steps"
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" and works in any mode — it is how a lead plans and coordinates via"
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" the board."
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),
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"parameters": {
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"type": "object",
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"properties": {
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"items": {
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"type": "array",
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"items": {
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"type": "object",
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"properties": {
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"title": {"type": "string"},
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"criteria": {"type": "string"},
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"description": {"type": "string"},
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"case": {"type": "string"},
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},
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"required": ["title", "criteria"],
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},
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},
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"note": {"type": "string"},
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},
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"required": ["items"],
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},
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},
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}
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def propose_work_items_tool() -> object:
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def propose_work_items(items: Optional[list] = None, note: str = "") -> dict:
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"""Present proposed work items ({title, criteria, description?, case?})
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for the user's approval; approval creates them on the board."""
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return {
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"approved": False,
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"error": "item proposals aren't available in this surface",
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}
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wrapped = ai.tool(
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propose_work_items,
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metadata=ai.ToolMetadata(
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category="team",
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risk_level="low",
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capabilities=["team"],
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),
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)
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wrapped.__coworker_schema__ = _PROPOSE_ITEMS_SCHEMA
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return wrapped
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def propose_team_tool() -> object:
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def propose_team(
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members: Optional[list] = None, enable_chat: bool = False, note: str = ""
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) -> dict:
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"""Propose the worker roster for this board (the staffing gate). Each member
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is {persona, model?, reason?}. The user approves; approval creates the
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worker sessions and returns their actor ids for assignment."""
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return {
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"approved": False,
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"error": "team staffing isn't available in this surface",
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}
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wrapped = ai.tool(
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propose_team,
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metadata=ai.ToolMetadata(
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category="team",
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risk_level="medium",
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capabilities=["team"],
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),
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)
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wrapped.__coworker_schema__ = _PROPOSE_TEAM_SCHEMA
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return wrapped
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def _call(func, *args, **kwargs) -> dict:
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try:
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result = func(*args, **kwargs)
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return result if isinstance(result, dict) else {"ok": True}
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except (BoardError, ValueError) as error:
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return {"error": str(error)}
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def _wrap(func):
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risk = "medium" if func.__name__ == "assign" else "low"
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return ai.tool(
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func,
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metadata=ai.ToolMetadata(
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category="team",
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risk_level=risk,
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capabilities=["team"],
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),
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)
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