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4a55145bb1
* fix: reset stale sandbox creation sentinel Co-authored-by: Johannes du Plessis <51395795+johannes117@users.noreply.github.com> * fix: treat SANDBOX_CREATING as a timestamped cross-process lock Only reset the sentinel when proven stale (older than the creation timeout); otherwise wait for the worker that holds the lock so a concurrent run does not create a duplicate sandbox. * feat(analyzer): outcomes dataset + bootstrap/continual split via skills Rename the review_style_analyzer graph to `analyzer` and split it into two modes, plus capture reviewer finding outcomes for continual learning. - Outcomes dataset: upsert resolved-by-commit (positive), dismissed (false positive), and GitHub/Slack thumbs findings into a single LangSmith dataset (openswe-reviewer-outcomes), keyed deterministically per finding+source. Emit points wired into update_finding, resolve_finding_thread, and the GitHub/Slack reaction handlers. - Two playbooks delivered as deepagents skills (bootstrap-repo-analysis, continual-learning), served as virtual files via a CompositeBackend /skills/ route + StateBackend (seeded into the run files channel at invoke time, never written to the sandbox). Mode is set by the launcher; continual runs fall back to the GitHub App installation token. - Split launcher into start_bootstrap_analysis + start_continual_run; register a per-repo nightly continual-learning cron when bootstrap completes. - New read_finding_outcomes tool feeds confirmed/dismissed findings back to the continual playbook. Tests for outcome label mapping, skills helper, and cron idempotency. * fix(analyzer): anchor continual cron runs to a real thread_id The nightly continual-learning cron is threadless, and get_analyzer early-returns an empty agent when configurable.thread_id is missing — so every cron-launched run no-op'd before reading outcomes or saving a refined prompt. Include the repo's deterministic analyzer thread_id in the continual run configurable so the run executes; the threadless run carries no message history, so nightly runs don't accumulate context. * refactor(analyzer): move cron lifecycle calls out of the review-styles store Drop the inline `analyzer_cron` imports from review_styles.py (added only to dodge a circular import) by relocating the cron-trigger calls to the layer above the store: registration to the save_review_style tool (after a prompt is saved) and removal to the dashboard delete route. review_styles.py is now a pure store again with top-level imports only. * refactor: hoist reviewer_outcomes imports to module level Move the two inline emit_finding_status_outcome imports introduced in this PR (update_finding, resolve_finding_thread) to top-level imports. reviewer_outcomes only depends on langsmith, so there is no circular import to avoid. --------- Co-authored-by: open-swe[bot] <open-swe@users.noreply.github.com>
49 lines
1.9 KiB
Python
49 lines
1.9 KiB
Python
"""Repo-bundled analyzer skills, served to the agent as virtual files.
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The two analyzer playbooks live as ``SKILL.md`` files under ``agent/skills/``.
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They are surfaced to the deepagents ``SkillsMiddleware`` via a ``StateBackend``
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mounted at ``/skills/`` in a ``CompositeBackend`` — so the agent reads them with
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``read_file`` without anything ever being written to the execution sandbox.
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The ``files`` channel is seeded at invoke time (see the launchers). Because
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``CompositeBackend`` strips the ``/skills/`` route prefix before delegating to the
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``StateBackend``, the seeded keys are the *stripped* paths (e.g.
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``/bootstrap-repo-analysis/SKILL.md``), while the agent and ``SkillsMiddleware``
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address them under ``/skills/...``.
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"""
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from __future__ import annotations
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from pathlib import Path
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from typing import Any
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from deepagents.backends.utils import create_file_data
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SKILLS_DIR = Path(__file__).resolve().parent.parent / "skills"
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SKILLS_ROUTE = "/skills/"
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BOOTSTRAP_SKILL = "bootstrap-repo-analysis"
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CONTINUAL_SKILL = "continual-learning"
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ANALYZER_MODES = {"bootstrap": BOOTSTRAP_SKILL, "continual": CONTINUAL_SKILL}
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def skill_path_for_mode(mode: str) -> str:
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"""Return the agent-facing ``/skills/<name>/SKILL.md`` path for a run mode."""
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skill = ANALYZER_MODES.get(mode, BOOTSTRAP_SKILL)
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return f"{SKILLS_ROUTE}{skill}/SKILL.md"
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def build_skill_files() -> dict[str, Any]:
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"""Return ``{stripped_path: FileData}`` for every bundled analyzer skill.
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Seed this into the run input's ``files`` so the ``/skills/`` StateBackend route
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can serve them. Keys omit the ``/skills`` prefix (stripped by the composite
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route); values are ``FileData`` v2 entries.
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"""
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files: dict[str, Any] = {}
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for skill in ANALYZER_MODES.values():
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skill_md = SKILLS_DIR / skill / "SKILL.md"
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text = skill_md.read_text(encoding="utf-8")
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files[f"/{skill}/SKILL.md"] = create_file_data(text)
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return files
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