praxen

Baseline — v1.2-opus5 (current)

The v1.2 fresh baseline. Supersedes v1.1-claude48 (now archival). This is a genuine re-scan, not a re-tag: model, knowledge bases, and remits all changed, so it is not directly comparable to v1.1 — the v1.1 numbers are context, not a pass/fail gate. Roster prominence + development-activity data lives in ../ROSTER_HEALTH.md.

What changed from v1.1

Freeze method — median-of-3

Each target was scanned 3× on identical inputs (remit, source, scope byte-identical), and is frozen on its median-weighted run — one real scan, never an aggregate. The 3× also characterized run-to-run variance (bands below).

Target Median run Weighted RAISE Band (3× spread)
helperbot run2 0.60 0.15
finbot run1 0.90 0.00 (re-scan)
craftbot run1 1.30 0.15
autogen-code-executor run2 1.55 0.55 (wide)
salesforce-help-agent-accelerator run3 1.70 0.00 (re-scan)
uagents run1 1.70 0.45 (wide)
aider run1 1.70 0.30
openai-customer-service run2 1.75 0.30
openhands run2 2.00 0.10 (re-scan)
deepagents-cli run1 2.15 0.15
yaah run1 2.15 0.00
hermes-agent-desktop run1 2.55 0.30

Mean median RAISE 1.67. Variance sits inside the documented envelope (docs/understanding-variability.md): 10/12 within ±0.30 of median, the exceptions being autogen (0.55) and uagents (0.45), the judgment-sensitive cases; none beyond. Notably, finbot’s over-reach fix collapsed its band from 0.55 (previously the widest target) to 0.00 — the ambiguous stay-in-lane clause was itself a variance driver, and removing it stabilized all three runs at 0.90. Themes reproduced every run for every target.

Sources & scope

Pinned SHAs recorded in ../../../local/v1.2-owasp2026-baseline/SOURCES.md. OpenHands is scanned at its pre-migration commit (the Agent-Canvas rewrite cleared the Python tree). deepagents re-scoped libs/cli → libs/code: the runtime moved to the sibling package; the subject follows the code (tests/scan_instructions/deepagents-cli.md).

Remit provenance & known caveats

Remits were re-authored by the tool (blind, docs-only) and then hardened. A post-freeze FP sweep (one independent cross-model reviewer per target, verifying every finding against source) found zero detector false positives across ~130 findings — but a recurring class of generator authoring defects:

The durable fix is the generator, not per-remit hand-editing — see RFE #198 (emit well-formed, docs-grounded, non-over-reaching statements). The heading-as-rule pattern (closure obligations left implicit) is systemic across remits and is part of #198.

All 12 pass python3 tests/render/test_render.py (schema-valid · HTML/TXT byte-identical re-render · every policy_rule_text quoted verbatim from tests/remits/<slug>.md).