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aivss-score

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更新日2026年6月27日 10:37

Run AIVSS v4 scorer on a report directory (ground_truth.json + governance_signals.json) and print the full inbound/internal/outbound breakdown, per-threat table, and a cached-vs-fresh delta if a prior score exists. Pass an architecture name as an argument, or omit for the most recent report. Read-only.

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BerdTan

BerdTan/threatassessor

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AIVSS Score Inspector

Recomputes AIVSS scores from disk and prints the full breakdown. No LLM calls. Takes ~1 second.

Run

# Most recent report
cd "$(git rev-parse --show-toplevel)" && source .venv/bin/activate && python3 .claude/skills/aivss-score/scripts/aivss-score.py

# Specific architecture
cd "$(git rev-parse --show-toplevel)" && source .venv/bin/activate && python3 .claude/skills/aivss-score/scripts/aivss-score.py 00_serviceentry_3

What it shows

Section	Contents
Summary	Inbound / Internal / Outbound composite + severity + coverage %; Overall composite + severity; industry profile
Flow Metric Breakdown	Per-flow: each AIVSS metric (CS, LL, MR, DS, GV, AA, DC, AD, EI…) with sub-scores and composite
Per-Threat Scores	Technique ID, name, composite, severity, top_metric, mitigation_multiplier — sorted by score descending
Cached vs Fresh	If governance_signals.json already has an `aivss` block, shows delta between cached and freshly computed
Coverage warnings	Flags any flow with <50% signal coverage — low coverage = sparse signals, not necessarily clean

Failure fixes

Error	Fix
`ground_truth.json not found`	Architecture has no analysis yet — run analysis first from dashboard or CLI
`AIVSSFlowScorer import fails`	Run `/quick-test` to verify harness_aivss module is importable
All composites show None	governance_signals.json missing or empty — run `/backfill-aivss <arch_name>`
Internal score = 0.0	Expected when no Expert Review has run — internal flow uses MoE/SM signals

Notes

Internal flow stays near zero until Expert Review (MoE) runs — it reads critic divergence and SM retrigger signals
Inbound / Outbound are fully deterministic from ground_truth.json alone
The script does not write governance_signals.json — use /backfill-aivss to persist scores

Related skills

/backfill-aivss — generate and persist governance_signals.json for past reports
/aivss-gate — show gate thresholds and last SIEM event without recomputing
/quick-test — broader integration sanity check

このリポジトリの他の Skills

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rerun-corpus

BerdTan/threatassessor

Audit and selectively re-run the deterministic analysis engine across all 26 corpus architectures. Default (no args) shows a staleness check — which reports are missing metadata, which arch types need engine fixes re-applied, and the current corpus score table. Pass --ai-only to re-run only AI/agentic architectures, --all to re-run everything, or a single arch name to re-run one. Use after engine changes (self_validation.py, ground_truth_generator.py, rapids_driven_controls.py) to confirm scores are consistent and no regressions landed.

2026-07-123

tatb-score

BerdTan/threatassessor

Run TATB (TA Test Benchmark) on a single architecture and print the four-rubric scorecard — Threat-Relevant, TTP-Accurate, Risk-Defensible, Plan-Actionable. Uses the same scoring logic as the dashboard TATB tab. Pass an architecture name as argument, or omit for the most recent report. Read-only. ~3 seconds (includes MITRE API fetch).

2026-07-123

tatb-corpus

BerdTan/threatassessor

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).

2026-07-113

run-er

BerdTan/threatassessor

Run Expert Review (MoE critics + ScrumMaster) on any architecture that has an existing analysis. Three modes — sequential (3 core critics), full_moe (5 critics + SM, generates Plan-Actionable data), or single-critic (re-run one critic by name). Streams live critic results with scores, gaps, and final confidence. ~60–120s for full_moe, ~30–60s for sequential.

2026-07-113

tatb-loop

BerdTan/threatassessor

TATB feedback loop — observe corpus or single-arch TATB scores, diagnose weak signals, propose targeted pipeline stage fixes, gate on human approval, apply changes, re-run TATB to verify improvement, and log the before/after delta to docs/DECISIONS.md. The agent does NOT autonomously commit code; every code change requires explicit human approval.

2026-07-043

quick-test

BerdTan/threatassessor

Runs a fast ~15 second integration sanity check — verifies dependencies, rate limiter, MITRE data load (≥835 techniques), OpenRouter API key, API connection, and one real embedding call. Use at session start or after installing dependencies to confirm everything is working before coding.

2026-07-023

name	aivss-score
description	Run AIVSS v4 scorer on a report directory (ground_truth.json + governance_signals.json) and print the full inbound/internal/outbound breakdown, per-threat table, and a cached-vs-fresh delta if a prior score exists. Pass an architecture name as an argument, or omit for the most recent report. Read-only.
allowed-tools	Bash(python3:) Bash(source:)

AIVSS Score Inspector

Recomputes AIVSS scores from disk and prints the full breakdown. No LLM calls. Takes ~1 second.

Run

# Most recent report
cd "$(git rev-parse --show-toplevel)" && source .venv/bin/activate && python3 .claude/skills/aivss-score/scripts/aivss-score.py

# Specific architecture
cd "$(git rev-parse --show-toplevel)" && source .venv/bin/activate && python3 .claude/skills/aivss-score/scripts/aivss-score.py 00_serviceentry_3

What it shows

Section	Contents
Summary	Inbound / Internal / Outbound composite + severity + coverage %; Overall composite + severity; industry profile
Flow Metric Breakdown	Per-flow: each AIVSS metric (CS, LL, MR, DS, GV, AA, DC, AD, EI…) with sub-scores and composite
Per-Threat Scores	Technique ID, name, composite, severity, top_metric, mitigation_multiplier — sorted by score descending
Cached vs Fresh	If governance_signals.json already has an `aivss` block, shows delta between cached and freshly computed
Coverage warnings	Flags any flow with <50% signal coverage — low coverage = sparse signals, not necessarily clean

Failure fixes

Error	Fix
`ground_truth.json not found`	Architecture has no analysis yet — run analysis first from dashboard or CLI
`AIVSSFlowScorer import fails`	Run `/quick-test` to verify harness_aivss module is importable
All composites show None	governance_signals.json missing or empty — run `/backfill-aivss <arch_name>`
Internal score = 0.0	Expected when no Expert Review has run — internal flow uses MoE/SM signals

Notes

Internal flow stays near zero until Expert Review (MoE) runs — it reads critic divergence and SM retrigger signals
Inbound / Outbound are fully deterministic from ground_truth.json alone
The script does not write governance_signals.json — use /backfill-aivss to persist scores

Related skills

/backfill-aivss — generate and persist governance_signals.json for past reports
/aivss-gate — show gate thresholds and last SIEM event without recomputing
/quick-test — broader integration sanity check