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SCIO v0.1.0

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@github-actions github-actions released this 10 Oct 22:21
· 173 commits to main since this release
v0.1.0
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Learn about vigilant mode.

Added

  • Observability: per-run report (scio report), persisted cost ledger per thread, RunReport with cost by role, escalations and timeline.
  • A/B role configurations (evals/configs/*.yaml, SCIO_ROLE_OVERRIDES_PATH); eval runner --config; capability suite with scenario tasks (fixture, scenario, expect_pass); nightly evals workflow posting per-config tables.
  • evals/tune.py escalation threshold sweep; EscalationConfig enforces max_stall >= repeat_threshold - 1; ADR 0006.
  • Release workflow and scripts/release_notes.py; SCIO_FAKE_SCRIPT=1 offline demo mode for the CLI; the CLI runs the unified graph (router picks the tier).
  • README acceptance-status table; first real GitHub end-to-end run recorded.

Fixed

  • prepare_repo writes repo-local git excludes so test caches are never committed (I-028).
  • T1 and T2 tiers in one graph builder (scio/graph/build.py): scope, parallel explore (LangGraph Send, read-only), localize, plan (plan-owned test filters), post_plan (issue checklist), implement (one step per cycle), debug, advise, review; generated t1/t2 diagrams.
  • Escalation policy (scio/loop/escalation.py, scio/loop/impact.py): six signals, never-escalate list, per-step and per-run caps, advisor_cap exit with human interrupt; TriageDecision in state; EscalationRecord with trigger, brief, verdict, tokens, $ and outcome; advisor guidance injected into the next worker turn; ADR generation in the target repository.
  • ToolBox(role=...) on spans; WorkerOutput.sweep surfaced in the ledger; PR body carries the escalation log and reviewer notes.
  • Five real regression tasks (t001–t005) with fixture repositories, hidden tests and a data-driven ScriptedResponder; the runner reports advisor call and resolution rates from the graph.
  • Postgres checkpointer built with the serializer allow-list; the gate runs the docs check directly.
  • ADR 0005: escalation policy implementation.
  • Context layer (scio/context): artifact store, per-role prompt projections, size-triggered transcript trimming.
  • Loop layer (scio/loop): AgentLoop (tools until done, tool/model caps, typed WorkerOutput summary) and exit conditions.
  • T0 graph (scio/graph/tiers/t0.py): intake, route, prepare_repo, t0_loop, verify, git_checkpoint, document (docs commit, push, draft PR), gate with ground-truth override, pr_ready, merge_wait interrupt, halt. scio run / scio resume CLI.
  • WorkerOutput.tests_added; verify requires changes, feature tests, a passing suite and clean lint.
  • Eval runner drives the real graph for grader: tests tasks and grades with hidden tests; t001 fixture repository; ScriptedResponder for deterministic Fake runs.
  • Generated t0 Mermaid section in docs/ARCHITECTURE.md; LangGraph serializer allow-list for state types.
  • ADR 0004: loop and verification design.
  • State backbone (scio/state): the M0 schema as Pydantic models with reducers, WorkerOutput.sweep, error signatures stable across line numbers and paths, progress fingerprints and stall counting.
  • Graph runtime (scio/graph): checkpointer selection by DSN (memory, SQLite, Postgres), idempotent side effects keyed by hash(thread_id, node, intent) with a durable effect log and world probes, Mermaid rendering with doc sections checked in CI, graph registry.
  • Agent-computer interface (scio/aci): Sandbox protocol with local, Docker and E2B backends; repo_search, view_file, edit_file (parse- and lint-gated), run_tests (failures only), git_checkpoint; output truncation with hints; capability tags and the compile-time trifecta check; ToolBox dispatcher with tracing and a call cap.
  • GitHubClient (scio/harness/github): idempotent branch, issue-comment and draft-PR writes through gh, default-branch guard, write log.
  • Durability test: a 3-node graph with file and git side effects killed mid-run and resumed with zero duplicated effects.
  • scripts/gen_mermaid.py; check_docs.py now verifies generated graph sections.
  • ADR 0003: idempotent side effects and sandbox tiers.
  • Harness layer (scio/harness): ModelProvider protocol; Anthropic, OpenAI and deterministic Fake adapters; single retry layer with backoff and retry-after; BudgetGuard checked before every call; per-call cost ledger; prompt-cache breakpoint placement; OpenTelemetry GenAI spans with opt-in content capture.
  • Prompt layer (scio/prompt): versioned per-role templates (12 roles at v1); Pydantic → provider-safe wire schema; structured-output validate-and-repair loop (max 2 repairs).
  • configs/models.yaml (the only place model IDs live) and configs/budgets.yaml (per-tier budgets).
  • Eval skeleton (evals/): task loader, n-trial runner, pass@1 / pass^k / tokens / $ / latency / advisor-rate metrics, Markdown and JSON reports, three placeholder regression tasks.
  • CI (.github/workflows/ci.yml): ruff, mypy, pytest on the Fake provider, docs checks, regression evals, API docs artifact. scripts/check_docs.py, scripts/gen_api_docs.py.
  • ADR 0002: harness conventions.
  • Continuous-improvement protocols: CLAUDE.md, docs/ISSUE_LEDGER.md (I-001 to I-010), docs/LESSONS_LEARNED.md (M0 and M1 retrospectives), Debugger template v2 with systemic sweeps, PR template checklist.
  • Build spec and generation prompt (docs/PROMPT.md).
  • Research basis with sources (docs/RESEARCH.md).
  • ADR 0001: single writer, Sonnet 5 worker, Opus 5.5 planner/advisor.
  • Repository docs: README, CONTRIBUTING, issue and PR templates.
  • Milestone 0 architecture (docs/ARCHITECTURE.md): graphs per tier, State schema, agent contracts, directory tree, milestone plan, assumptions and open questions.
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