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47 A/B experiments across 44 workflows in github/gh-aw were analysed. None reached a PROMOTE or REJECT core decision yet — 32 are EXTEND (mostly blocked by unsupported outcome-metric observations or low sample counts) and 15 are INCONCLUSIVE (declared 3+ variant designs that core analysis doesn't yet support). 23 experiments are READY on sample count but still lack usable outcome evidence.
insufficient_observations (20 experiments): sample counts satisfy min_samples, but the workflow's declared metric (e.g. token_count_per_run, custom quality scores) is not being written to per-run observation data that core analysis can consume. This is a data-pipeline gap, not evidence against any variant.
unsupported_multi_variant (15 experiments): core statistical analysis currently supports 2-variant A/B comparisons; experiments declaring 3+ variants (output_format, model_size, reasoning_depth families) cannot yet produce a PROMOTE/REJECT decision even with abundant samples.
guardrail_unsupported (3 experiments): cicoach, daily-safe-output-optimizer, daily-security-red-team are READY but blocked because a mandatory guardrail metric (e.g. run_success_rate) has no supported observation source.
insufficient_samples (9 experiments): still accumulating runs toward min_samples.
No interaction diagnostics were computed this run: no workflow currently runs two concurrent experiments with enough overlapping data for a meaningful K1×K2 cross-tab, so no SPARSE_CELL_RISK holds applied.
🛠️ Self-Tuning Continuation Plan
Top 3 experiment actions
Wire per-run outcome-metric observations (token_count_per_run, quality scores, run_success_rate) into the experiment state pipeline — this alone would unblock 23 insufficient_observations/guardrail_unsupported experiments that are already at full sample size.
Extend core analysis to support K≥3 variant designs (with Bonferroni correction, already partially modeled via bonferroni_alpha) — this unblocks 15 INCONCLUSIVE experiments, mostly output_format/model_size families.
Continue run accumulation for the 9 insufficient_samples experiments; no action needed beyond normal scheduling.
Top 3 eval actions
Add graders/evals that score output_format variants (structured, prose, ste, etc.) on verbosity and reader comprehension, feeding output_length_chars/output_token_count as secondary metrics — directly relevant to daily-code-metrics, daily-compiler-quality, daily-issues-report, deep-report, copilot-agent-analysis.
Add success/failure graders for model_size experiments (daily-cache-strategy-analyzer, daily-caveman-optimizer, daily-doc-healer, daily-doc-updater) to produce the run_success_rate guardrail observation these workflows already declare but cannot evaluate.
Add a lightweight quality grader for prompt_style experiments (ci-coach, daily-astrostylelite-markdown-spellcheck, daily-news, issue-arborist) so insufficient_observations converts into usable evidence once sample counts are already sufficient.
Decision-pipeline gaps for next PR
Normalize a "multi-variant chi-square / Kruskal-Wallis" decision path in core so 3+ variant experiments stop reporting unsupported_multi_variant once samples are sufficient.
Add a supported native metric source for run_success_rate and empty_output_rate guardrails so guardrail_unsupported experiments (ci-coach, daily-safe-output-optimizer, daily-security-red-team) can reach a real guardrail pass/fail.
Promote the factorial-interaction helper described in this reporting workflow into a first-class core analysis signal once two workflows run overlapping concurrent experiments with enough joint samples.
Descriptive window: gh aw experiments analyze state as of this run · Decision thresholds: from each experiment's decision_policy
Run: 32945795113
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🧪 Daily Experiment Report — 2026-08-26
47 A/B experiments across 44 workflows in
github/gh-awwere analysed. None reached aPROMOTEorREJECTcore decision yet — 32 areEXTEND(mostly blocked by unsupported outcome-metric observations or low sample counts) and 15 areINCONCLUSIVE(declared 3+ variant designs that core analysis doesn't yet support). 23 experiments areREADYon sample count but still lack usable outcome evidence.⚡ Quick Stats
📈 Full Experiments Table (all 47)
cavemansubagent_modelcavemansubagent_modelcavemansubagent_model🔍 Interpretation
insufficient_observations(20 experiments): sample counts satisfymin_samples, but the workflow's declaredmetric(e.g.token_count_per_run, custom quality scores) is not being written to per-run observation data that core analysis can consume. This is a data-pipeline gap, not evidence against any variant.unsupported_multi_variant(15 experiments): core statistical analysis currently supports 2-variant A/B comparisons; experiments declaring 3+ variants (output_format,model_size,reasoning_depthfamilies) cannot yet produce aPROMOTE/REJECTdecision even with abundant samples.guardrail_unsupported(3 experiments):cicoach,daily-safe-output-optimizer,daily-security-red-teamare READY but blocked because a mandatory guardrail metric (e.g.run_success_rate) has no supported observation source.insufficient_samples(9 experiments): still accumulating runs towardmin_samples.SPARSE_CELL_RISKholds applied.🛠️ Self-Tuning Continuation Plan
Top 3 experiment actions
token_count_per_run, quality scores,run_success_rate) into the experiment state pipeline — this alone would unblock 23insufficient_observations/guardrail_unsupportedexperiments that are already at full sample size.bonferroni_alpha) — this unblocks 15INCONCLUSIVEexperiments, mostlyoutput_format/model_sizefamilies.insufficient_samplesexperiments; no action needed beyond normal scheduling.Top 3 eval actions
output_formatvariants (structured,prose,ste, etc.) on verbosity and reader comprehension, feedingoutput_length_chars/output_token_countas secondary metrics — directly relevant todaily-code-metrics,daily-compiler-quality,daily-issues-report,deep-report,copilot-agent-analysis.model_sizeexperiments (daily-cache-strategy-analyzer,daily-caveman-optimizer,daily-doc-healer,daily-doc-updater) to produce therun_success_rateguardrail observation these workflows already declare but cannot evaluate.prompt_styleexperiments (ci-coach,daily-astrostylelite-markdown-spellcheck,daily-news,issue-arborist) soinsufficient_observationsconverts into usable evidence once sample counts are already sufficient.Decision-pipeline gaps for next PR
unsupported_multi_variantonce samples are sufficient.run_success_rateandempty_output_rateguardrails soguardrail_unsupportedexperiments (ci-coach,daily-safe-output-optimizer,daily-security-red-team) can reach a real guardrail pass/fail.All reactions