Codex stop-time review caught the previous commit (113bd55a) message
overstating apply.ts's behavior — I claimed "scripts/ab-corpus/apply
both catch per-shape and keep running the remaining tags" but the
loop at line 55 had no inner try/catch. A single throw from any
handleElementToolCall (e.g. the heading invalid-level reject 113bd55a
just added) would bubble up through the outer try at line 40 and
return early, dropping every remaining tag in a composite batch on
the floor — gpt-5.4's 13-tag team-people-page response would lose
tags 12-13 instead of just tag 12.
Wraps each handleElementToolCall in its own try/catch + accumulates
failures into a per-shape list. ELEMENT_TOOL_NAMES miss is also a
push-and-continue (was a return). When `failures.length > 0` we
return ok:false with a message listing every failed tag, AND the
partial PenDocument that DID land — so M3 (role coverage) can still
score the 11 tags that worked. M1 stays strict (any failure → false).
Mirrors apps/web/src/services/ai/element-tools-dispatcher::
dispatchElementToolCalls's "collect-errors-keep-going" semantics —
production already worked this way; ab-corpus now does too.
3772 vitest pass, format clean, tsc silent. Existing dry-run + live
sweeps exercise the path; a focused apply.ts unit test would need
pen-mcp setup that the harness's existing build-prompt test sidesteps,
so leaving that as a followup.
Codex stop-hook caught: ab-v3 introduced composite-difficulty prompts
that *expect* multi-tool emit (e.g. 5× member_row + 1× invite_row
for a team page), but `ParsedOutput.tool_call` was a single
{name, arguments} so the parser silently dropped every call after
the first. apply.ts only invoked one tool, M3 min_roles couldn't
pass on legitimately-routed multi-tool runs, and byTool stats
under-counted. The composite routing 'multi-tool' bucket was
correctly assigned in classifyRouting, but downstream the pipeline
behaved as if the model emitted a single call.
This commit replaces `kind: 'tool_call'` with
`kind: 'tool_calls'` (NON-EMPTY list) across every consumer:
- types.ts: ParsedOutput tagged union; new ParsedOpToolCall.
ScoreRow.toolName → toolNames: string[].
- output-parser.ts: collects ALL element-tool tags in emit order;
unknown-tool path also surfaces as single-element tool_calls so
routing keeps the same wrong-tool semantics.
- score-run.ts: classifyRouting uses Array.includes for obvious
prompts (right-tool when ANY emitted call matches expected_tool —
over-production isn't a routing miss). Composite stays multi-tool
on any non-empty list.
- aggregate.ts byTool: tallies EVERY name in toolNames, so a
composite row that emits 6× add_activity_log_v0 + 1×
add_section_header_v0 contributes 6+1 = 7 invocations across two
tools (with row-level m1_legal applied to both buckets — apply is
all-or-nothing).
- apply.ts: loops over parsed.calls and invokes
handleElementToolCall in emit order. Any single call failing
aborts the row (M1=false); we don't partial-apply.
- mock-llm.ts mockLlmParsed: collects all `<op_tool>` tags into the
list (composite-prompt mocks can carry multi-call raw strings).
- apps/web design-parser.tryParseElementToolOutput: maps tool_calls
→ its single-shape DesignOutputShape contract using the FIRST
call (the multi-tag path `tryParseAllElementToolOutputs` was
already correct).
Tests: 3746 → 3750 vitest. New cases:
- output-parser: surfaces ALL element-tool tags in emit order with
intermixed batch_design scaffolds dropped (3 element calls from
5 tags).
- score-run: right-tool when expected appears alongside extras;
composite multi-call captures every name in toolNames.
- aggregate: 6× activity_log + 1× section_header → byTool reports
6 and 1 invocations respectively.
dry-run on ab-v3 produces a 208-row report; tsc + format clean.
`/models` now returns only deepseek-v4-pro and deepseek-v4-flash;
deepseek-chat / deepseek-reasoner sunset 2026-07-24 and the
deepseek-v3.2 hard-coded in the ark-coding fallback list never
existed. Both v4 models default to thinking enabled and the API
toggles via `{"thinking":{"type":"disabled"}}` — keep
`thinkingMode: 'disabled'` so the app's fast/non-thinking default
stays intact (server reasoning paths honor it; the Zig openai-compat
path doesn't emit the toggle yet, so calls through that path still
get provider-default thinking until it's wired). v4-pro promoted to
full tier; legacy aliases pinned to an exact RegExp so future
deepseek-* variants don't inherit a forced disabled mode.
Bandaid for the unwired toggle: v4-pro gets `timeoutMultiplier: 2`
because its default-on reasoning blows past the orchestrator's
planning timeout on long system prompts (observed in dev: planning
phase falls back, sub-agent then succeeds — UX degraded but
functional). Drop the multiplier once the Zig path actually sends
`thinking:{type:disabled}`.
Don't add a BUILTIN_MODEL_LISTS.deepseek entry — DeepSeek exposes
/v1/models, so let `fetchProviderModels` pull the live catalog
through `/api/ai/provider-models` instead of pinning a snapshot
(the ark-coding `deepseek-v3.2` ghost above shows what those
snapshots drift into).
Harness at scripts/ab-corpus/ wires the pen-ai-skills corpus evaluator to
real model endpoints and pen-mcp handlers:
run.ts — CLI entry (--dry-run / --live / --models A,B,C / --only ID)
apply.ts — ApplyFn impl dispatching tool_call → element handler
and batch_design DSL → handleBatchDesign, against a
fresh tmp .op per run (isolated, auto-cleanup)
build-prompt.ts — B variant strips elements.md + appends batch_design
<op_tool> format instruction; T keeps elements + adds
element-tool PRIMARY / batch_design FALLBACK
instruction. Uniform <op_tool> wrapper in both arms
isolates "tool set width" as the only A/B variable.
stub-model.ts — fixture-based offline model for --dry-run
real-model.ts — router by model id (minimax* / gpt-*/o* / glm-5.1 /
glm-* / kimi-*)
clients/
openai-compat.ts — generic chat/completions POST
minimax.ts — api.minimax.io/v1, MINIMAX_API_KEY
codex-cli.ts — spawns `codex exec` (GPT-5.4 via Codex Pro sub)
bailian.ts — coding.dashscope.aliyuncs.com/v1 CP,
DASHSCOPE_BAILIAN_CODING_KEY (hosts glm-4.7, kimi-k2.5)
glm.ts — open.bigmodel.cn/api/coding/paas/v4 official CP,
GLM_OFFICIAL_CODING_KEY
write-report.ts — Report → report.md + report.json in out dir;
4-way routing breakdown table per model
Kept entirely outside packages/ — scripts are a local dev tool, not part
of the published SDK. API keys never hit disk or git.
v1 run results logged separately in openpencil-docs
superpowers/notes/2026-04-20-ab-v1-results.md (5 models × 24 prompts).
