tatb-corpus

name	tatb-corpus
description	Score all architectures in /report/ across the four TATB rubrics and print a visual corpus map — bar table, heatmap, and pipeline stage attribution. Shows which architectures are Excellent/Solid/Weak/Draft on each dimension and identifies which pipeline stages to fix for the largest corpus-wide gain. No UI needed. ~10-15 seconds (MITRE fetch batched once).
allowed-tools	Bash(python3:*) Bash(source:*)

TATB Corpus — All Architectures

Scores every architecture in report/ on the four TATB rubrics and produces three views: a sortable bar table, a compact heatmap, and a pipeline stage attribution table. Architectures with partial data (GT only, no MoE/SM) are scored on available signals and marked N/A for the rest.

Run

# Default — sort by overall, colour output
cd "$(git rev-parse --show-toplevel)" && source .venv/bin/activate && python3 .claude/skills/tatb-corpus/scripts/tatb-corpus.py

# Sort by specific rubric
cd "$(git rev-parse --show-toplevel)" && source .venv/bin/activate && python3 .claude/skills/tatb-corpus/scripts/tatb-corpus.py --sort ttp

# JSON output (for agent consumption or piping)
cd "$(git rev-parse --show-toplevel)" && source .venv/bin/activate && python3 .claude/skills/tatb-corpus/scripts/tatb-corpus.py --json > tatb-corpus.json

# No colour (for CI / log files)
cd "$(git rev-parse --show-toplevel)" && source .venv/bin/activate && python3 .claude/skills/tatb-corpus/scripts/tatb-corpus.py --no-color

What it shows

View	Contents
Bar table	One row per architecture · four rubric bars (0-100) + overall · sorted
Heatmap	One character per arch per dimension · worst→best left→right · patterns visible instantly
Stage attribution	Corpus average per sub-signal · maps each signal to the pipeline stage responsible · three ▼ signals = three stages to fix
Corpus stats	min/max/avg overall across all scored architectures

Interpreting the output

• TTP row mostly ▼ → most architectures ran api_only without full MoE — no cross-critic data
• Risk hop_pct avg <60% → ADR generator not assigning all four dir_category layers — fix in threat_report.py
• Plan closure avg <50% → SM prompt not referencing AP-IDs — add instruction to SM prompt
• Threat row mostly ■ → AnalysisStage is working correctly; structural inference is consistent

Sort options

--sort threat | ttp | risk | plan | overall

Feedback loop

The stage attribution table at the bottom maps every weak corpus signal to its pipeline stage. Fix in order of corpus impact:

1. Run full_moe on key architectures → raises TTP cross-critic from avg 13% to ~60%
2. Fix ADR dir_category assignment in ReportStage → raises hop layer coverage from avg 44%
3. Add AP-ID instruction to SM prompt → raises plan closure from avg 40%
4. Tighten self_validation.py thresholds → raises TTP validation from avg 65%

Re-run corpus after each fix to measure improvement.

Failure fixes

Error	Fix
No architectures found	Run at least one analysis from dashboard or CLI first
MITRE fetch fails (all alignment 50%)	Start API server: ./scripts/api/api_start.sh
Plan shows N/A for all	No architectures have SM data — run full_moe from dashboard Expert Review

Related skills

• /tatb-score — deep-dive on a single architecture
• /aivss-score — AIVSS-specific breakdown per architecture
• /backfill-aivss — generate governance_signals.json for architectures that lack it