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Use when running multi-mission Kamino (or campaign) research via the deterministic coordinator — plan, delegate, cycle, journal.
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Based on SOC occupation classification
Practitioner-elevated asset tracing patterns from SlowMist Crypto-Asset-Tracing-Handbook. Provides canonical property tables for obfuscation patterns (peel chains, mixers, bridge hops), address behavior clustering/risk profiling, cross-chain reconstruction workflows. Hard-first on behavioral + flow invariants. Integrates with codegraph-x-ray, deep-dive-handoff, and bounty forensics. Includes reference implementation for mixer deposit/withdrawal clustering. Trigger: alpha-miner on tracing handbook or similar.
Drop-in skill adapted from Glider query engine for static analysis, taint tracking, CFG/DFG traversal in bug bounty. Supports lazy reference loading, creativity layer for audited targets, integrates with bounty-loop. EVM priority with Solana compatibility.
Use for mining validator/runtime/cache/storage-key coherence bugs in blockchain clients, VMs, program loaders, bridge runtimes, and execution harnesses. Hard-first on stale cache, recycled identifier, partial flush, epoch/generation drift, mempool/order-dependent runtime state, and storage-key confusion. Trigger on runtime cache invariant, stale cache bug, VM storage confusion, client cache coherence, Aptos-style hijack, validator-local state, epoch-boundary bug, or when alpha-miner extracts runtime/client exploit engineering.
Cross-references a graphified target codebase (codegraph-x-ray output) against the local AuditVault corpus to surface structurally analogous historical vulnerabilities. Run after codegraph-x-ray has completed and produced invariants.md and property_candidates.md. Produces ranked pattern-match hits with per-finding graph anchor evidence. Pattern matches are advisory signals only — never evidence of exploitability and never a substitute for live reproduction or submission-gate validation.
Combines structured codegraph intelligence with rigorous invariant synthesis. Enforces high-quality usage of codegraph to identify the Primary Target Subsystem, then applies ordered structural invariant discovery with strict verification gates. Produces categorized invariants and high-quality property candidates ready for ultrafuzz-discovery. Trigger on codegraph-x-ray, x-ray with codegraph, invariant synthesis, primary subsystem invariants.
Optional accelerator and scaffolder for ultrafuzz-discovery. Brings parallel specialized invariant discovery agents, automated harness/handler scaffolding, coverage-driven refinement loops, property tagging, and reproducible test generation helpers from high-quality EVM fuzz patterns. Designed to speed up property fan-in and executable attempts phases while preserving NSS rigor, fresh-context pass@k, adjudication, and Crucible usage. Trigger on fuzz-scaffolder, accelerate invariant discovery, generate fuzz harness, scaffold handlers.
| name | coordinator-cycle |
| description | Use when running multi-mission Kamino (or campaign) research via the deterministic coordinator — plan, delegate, cycle, journal. |
Deterministic mission orchestration: one narrow template per cycle. Creative work stays in delegate_task; prioritization and debrief are Python coordinator only.
Cron role (v2.0.10): weekly nss-investigate-queue (Sun 05:00) — Kamino depth. Daily cross-platform hunt is bounty-loop, not this skill.
cd /home/kt/projects/rtp/night-shift-security
git pull --ff-only
Initialize once per campaign:
.venv/bin/python -m night_shift_security.cli.main \
--config src/night_shift_security/config/kamino_shoestring.json \
coordinator init
# 1. Plan next mission (machine-readable)
.venv/bin/python -m night_shift_security.cli.main \
--config src/night_shift_security/config/kamino_shoestring.json \
coordinator plan --top 1
# 2. Scoped proposals — either:
# a) hypothesis-expansion skill (delegate_task) for mission.template_id only, OR
# b) parametric refinement: .venv/bin/python hermes/scripts/nss-write-proposals.py
# c) or RSI hints: cat data/security_results/loop/refinement_hints.json
# 3. Execute one cycle (pipeline + debrief + state update)
.venv/bin/python -m night_shift_security.cli.main \
--config src/night_shift_security/config/kamino_shoestring.json \
--proposals data/security_results/hermes_proposals/latest.json \
coordinator cycle
# 4. Status + lab notebook
.venv/bin/python -m night_shift_security.cli.main coordinator status
# Follow lab-notebook skill — cite debrief JSON under knowledge/debriefs/
delegate_task proposals remain metadata.trusted=false.coordinator cycle; do not reuse mission_id.coordinator init before first plan/cycle — state lives at data/security_results/knowledge/coordinator_state.json.coordinator plan after a cycle should surface a different template unless refinement (lineage_expansion) fired.data/security_results/knowledge/debriefs/<mission_id>.json — use for Same vs different notebook entries.cycle can take minutes; cron should use plan + expansion + cycle as separate steps if needed.data/security_results/day_shift/current.md Night Shift handoff before cron — skip targets Day Shift marked complete.