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{"domain":"agent-runtime","tool_count":12,"guide_count":2,"mitre":"N/A — meta-skill (runtime engineering, not a specific ATT&CK technique)","last_reviewed":"2026-07-26"}
Multi-Agent Runtime Engineering Skill
"Harness engineering > base-model parameters. MopMonk took MiniMax M3 (smaller base) to 73.1% on CyberGym — beating Claude Opus 4.6 (66.6%) and matching GPT-5.4 (79.0%) within a stone's throw. The harness is the moat." — adapted from 36kr's coverage of MopMonk Agent (Chinese press nickname 扫地僧), 2026-06-30
Summary
This skill is the runtime engineering layer for agent systems. It treats memory schemas, sync protocols, convergence rules, anti-pattern prevention, and topology selection as first-class engineering artifacts — the things that decide whether a multi-agent system actually converges on truth or spirals into hallucinated prose.
This skill solidifies the engineering patterns of validation/scenarios/SCEN-007.md (Shared-Memory Multi-Agent Exploit Dev) and validation/scenarios/SCEN-MEMORY-SCHEMA.md (Structured Memory foundation) into a reusable knowledge base. SCEN-007 is the live scenario that proves the pattern — three parallel agents (patch-diff, harness-entry, sanitizer) converging on CVE-2019-7317 in ~45 minutes wall-clock vs. ~2 hours serially. SCEN-MEMORY-SCHEMA is the underlying schema library. This skill abstracts both into the runtime discipline that kali-claw (or any agent harness) uses whenever it needs to coordinate memory across multiple workers, multiple phases, or multiple independent explorations of the same target.
Why this skill exists as a distinct domain. Three converging trends in 2024-2026 agent engineering make a dedicated runtime-engineering skill necessary. First, MopMonk Agent (Chinese press nickname 扫地僧) demonstrated that a smaller base model (MiniMax M3) with disciplined harness engineering beats larger models with naive harnesses — 73.1% on CyberGym vs. Claude Opus 4.6's 66.6%. The "harness" in question is precisely the three layers this skill codifies: structured memory (招一 / Layer 1), memory-driven convergence (招二 / Layer 2), and shared-memory multi-agent coordination (招三 / Layer 3). Second, Anthropic's multi-agent research system blog series (2024-2026) and the parallel work on LangGraph, AutoGen, and Magentic-One all converged on the same core primitives — atomic state writes, version vectors, convergence detection, and explicit topology choice — but each frames them in vendor-specific terms. Third, kali-claw's own SCEN-007 showed that filesystem-native coordination (POSIX flock + jq + mv) is sufficient for multi-agent exploit dev with no DB, no message broker, and no framework dependency — a deliberately low-tech, high-reliability stack.
What this skill does NOT cover. It is not generic task decomposition (multi-agent-collaboration), not multi-perspective analysis (council), not pentest-framework deployment (agentic-pentest), and not knowledge persistence primitives (continuous-learning, chronicle). It is the engineering substrate underneath all of those — the schema design, sync protocol, and convergence rulebook that any of those higher-level skills may invoke when they need persistent structured memory across parallel workers.
Those persist prose; this skill persists structured JSON — the two layers compose, they do not compete
verification-loop
Independent re-run of agent claims
Verification reads from the structured memory this skill defines; the memory's evidence_for / evidence_against fields are how verification results land
Loops are the control flow; this skill is the state layer the loops read and write
engagement-manager
Kill-chain phase orchestration (human-readable)
Engagement manager decides what phase to run; this skill decides how multiple parallel workers inside a phase share state
Use Cases
Schema Design
Design a structured memory JSON for a multi-phase pentest engagement (recon → intrusion → privilege escalation → lateral → exfil) where each phase reads the prior phase's structured findings and writes a delta
Define an exploit-attempt memory schema for parallel-exploit dev — multiple agents writing hypotheses, evidence, failed attempts, and candidate PoCs to one shared file
Define a patch-diff reproduction memory schema — schema for "given a patch, reproduce the underlying bug and generate a differentially-verified PoC" (CyberGym-style)
Retrofit a prose-only memory system with structured fields — convert legacy MEMORY.md Chinese prose-only into a JSON companion without losing the curated distilled knowledge
Federate engagement memory with long-term knowledge — schema design where per-engagement JSON distills back into the root MEMORY.md at engagement close, same pattern kali-claw's daily logs follow
Atomic-Write Sync
Coordinate 3 parallel exploit-dev agents against one shared memory file (SCEN-007 case: patch-diff + harness-entry + sanitizer)
Prevent lost-update when two agents write different fields simultaneously — version-vector guard detects conflict and retries
Implement path-claim coordination — agents claim a unique exploration path (no duplicate active_paths values) using flock + atomic write
Detect and recover from path-claim deadlock — schema validation rejects any write where active_paths has duplicates
Build a coordinator script that bootstraps memory, dispatches agents, and aggregates final state — the literal harness from SCEN-007 Phase 0
Tolerate agent crash mid-write — mktemp + mv pattern ensures the memory file is never half-written; the temp file is GC'd on next coordinator pass
Convergence Detection
Detect when 2+ agents independently arrive at the same hypothesis — same path field, different claimed_by → promote both to CONFIRMED and emit convergence event
Promote a hypothesis from LIKELY to CONFIRMED on third independent evidence vector (triangulation principle)
Demote a hypothesis to INVALIDATED after 3 failed attempts with no new evidence — path-switch trigger fires
Run a periodic sync-point convergence sweep — every N iterations, an external observer scans the memory for convergence events and emits decisions
Detect premature stop — agent tried to mark stop_condition_met = true without filling verification_results; schema validation blocks the write
Generate a convergence timeline for post-engagement analysis — "at t=25min, agents A∩B converged on path X; at t=40min, differential verification passed"
Anti-Pattern Prevention
Detect free-form exploration — agent ran commands without reading memory first; memory_lock.last_read_at is null when write attempted
Detect memory drift — agent wrote prose to decision_log that references a finding not present in findings[]
Detect repeat-without-delta — same hypothesis tested 3+ times with no new evidence in evidence_for or evidence_against
Detect path-claim deadlock — two agents both wrote the same value to active_paths
Detect premature stop — stop condition claimed without differential verification
Build a checker script that runs after every memory write — schema validation + anti-pattern checks in one jq pipeline
Topology Selection
Choose between parallel-explorers / pipeline / council / hierarchical topology based on task shape (bug-class exploration vs. phase-sequential vs. multi-perspective judgment vs. coordinator-fanout)
Decide when NOT to use multi-agent — single-target linear attack chains (use autonomous-loops Sequential Pipeline instead)
Decide when to use council-style multi-perspective analysis (one question, three lenses) vs. parallel-explorers (one target, three directions)
Mix topologies within one engagement — parallel-explorers for recon, pipeline for exploit chain, council for the final risk judgment
Decide coordinator-vs-peer protocol — coordinator simplifies reasoning but adds a bottleneck; peer-to-peer is robust but harder to reason about
Engagement-close: distill JSON memory back into prose MEMORY.md
Shell Idioms
Idiom
Use
tmp=$(mktemp); jq ... mem > "$tmp"; mv "$tmp" mem
Atomic write without lock (single-agent case)
grep -c '"decision":' mem.json
Quick decision-log length check
`git diff --no-index mem.json.old mem.json
jq -R '...'`
sha256sum evidence/*.txt > evidence_index.sha256
Evidence integrity (paired with evidence_index in schema)
Methodology — The 5-Layer Runtime Stack
This skill organizes agent runtime engineering into five layers, each building on the one below. A mature harness implements all five; a naive harness may only implement the first one or two (and pays for it in hallucination, deadlock, and lost work).
Every engagement maintains a machine-queryable memory JSON. No prose memory. The agent must be able to query "what did we learn about X?" and get a deterministic answer. Every phase MUST: read the current memory file, execute its phase task, write a delta (fields added / updated / invalidated), and update next_constraints so downstream phases know the boundaries.
This skill ships three canonical schemas: Pentest Engagement Memory (Schema 1 — for cross-phase engagements), Exploit Attempt Memory (Schema 2 — for parallel exploit dev), and Patch-Diff Reproduction Memory (Schema 3 — for CyberGym-style PoC generation). Full templates are in payloads.md.
When multiple agents read/write the same memory file, every update goes through the canonical atomic-write pattern: take flock advisory lock on a sidecar .lock file → read current memory → apply jq transform to a mktemp temp file → validate (version-vector guard + anti-pattern checks) → mv temp to real path (POSIX atomic). No agent ever edits the JSON in place. Conflict resolution is optimistic concurrency: each write must increment memory_lock.version by exactly 1; a version mismatch aborts the write and the agent retries.
Open-ended trial-and-error is forbidden. Every action must either produce new evidence (update an evidence_for / evidence_against field, bump confidence) or be aborted and trigger a path switch. The rule is encoded in convergence_state.failed_attempts_on_active_path — when it hits path_switch_threshold (typically 3), the agent releases its current path and picks a new one from candidate_paths. Convergence events fire when 2+ hypotheses point at the same path field with different claimed_by — those get promoted to CONFIRMED and merged into a canonical hypothesis.
Layer 4 — Anti-Pattern Prevention
Five forbidden behaviors, each with a machine-checkable detection rule. The atomic-write pattern (Layer 2) runs the checks before mv; if any check fails, the write is rejected and the agent must fix its state. The five anti-patterns: free-form exploration (write without prior read), memory drift (decision log references finding not in findings[]), repeat-without-delta (3+ failed attempts on same hypothesis with no new evidence), path-claim deadlock (two agents grab same path), premature stop (stop claimed without differential verification). Detection rules in payloads.md §17-§21.
Layer 5 — Topology Selection
Not every task benefits from the same agent topology. Parallel-explorers (SCEN-007) fits bug-class exploration where multiple independent directions increase the chance of convergence. Pipeline fits phase-sequential work (recon → exploit → report). Council fits questions where the same input needs three analytical lenses (Attack / Defense / Audit). Hierarchical coordinator-worker fits dynamic engagements where task dependencies shift. The topology matrix in payloads.md §22 guides selection; the rule of thumb is "as parallel as possible, as coordinated as necessary."
MopMonk Three-招 (Three-Layer) Mapping
The MopMonk Agent (Chinese press nickname 扫地僧) three-招 (three layers) map directly onto this skill's 5-layer stack:
MopMonk 招 (layer)
kali-claw Layer
Implementation
招一 (Layer 1 — Structured Vulnerability Memory)
Layer 1 — Structured Memory
Three canonical JSON schemas (engagement / exploit-attempt / patch-diff-repro) with required fields, confidence taxonomy, evidence index, decision log
招二 (Layer 2 — Memory-Driven Convergence)
Layer 3 — Convergence Detection
Every action yields a delta or triggers path switch; failed_attempts_on_active_path threshold drives path switches; convergence events promote hypotheses to CONFIRMED
招三 (Layer 3 — Shared-Memory Multi-Agent)
Layers 2 + 5 — Atomic Sync + Topology
POSIX flock + atomic write + version vector across N parallel agents; topology choice (parallel-explorers for bug-class, pipeline for phase-sequential)
The fourth implicit 招 (layer) — the meta-principle — is "Harness > Parameters": the same base model with a disciplined harness dramatically outperforms the same model with a naive harness. MopMonk's MiniMax M3 (smaller base) at 73.1% beats Claude Opus 4.6 (larger base) at 66.6% — the gap is harness engineering, not model capacity.
Practical Steps
Step A — Bootstrap an Exploit Attempt Memory (Schema 2)
Step D — Convergence Detection (run at each sync point)
# Detect: 2+ hypotheses pointing at the same path with different claimed_by
MEM=/runs/SCEN-007/mem/exploit-attempt-memory.json
jq -r '
[.vulnerability_hypotheses[] | select(.status != "INVALIDATED")] as $h
| ($h | group_by(.path) | map(select(length >= 2)) | map(map(.claimed_by) | unique | length >= 2) | any) as $converged
| if $converged then
"CONVERGENCE: " + (
$h | group_by(.path) | map(select(length >= 2)) | map(
.[0].path + " (agents: " + (map(.claimed_by) | unique | join(",")) + ")"
) | join("; ")
)
else "no convergence yet" end
' "$MEM"
Step E — Anti-Pattern Checker (run after every write)
MEM=/runs/SCEN-007/mem/exploit-attempt-memory.json
python3 - <<'PY' "$MEM"
import json, sys, datetime
mem = json.load(open(sys.argv[1]))
violations = []
# AP-1 free-form exploration: write attempted without prior read
if mem.get("memory_lock", {}).get("last_write_at") and not mem.get("memory_lock", {}).get("last_read_at"):
violations.append("AP-1 free-form exploration")
# AP-3 repeat-without-delta: 3+ failed attempts on same hypothesis with no new evidence
from collections import Counter
fails = Counter(f.get("hypothesis") for f in mem.get("failed_attempts", []))
for hyp, n in fails.items():
if n >= 3:
violations.append(f"AP-3 repeat-without-delta on {hyp}: {n} failed attempts")
# AP-4 path-claim deadlock: duplicate active_paths values
paths = list(mem.get("active_paths", {}).values())
if len(paths) != len(set(paths)):
violations.append(f"AP-4 path-claim deadlock: {paths}")
# AP-5 premature stop
cs = mem.get("convergence_state", {})
vr = mem.get("verification_results", {})
if cs.get("stop_condition_met") and (not vr.get("vulnerable") or not vr.get("patched")):
violations.append("AP-5 premature stop without differential verification")
if violations:
print("[ANTI-PATTERN]", "; ".join(violations))
sys.exit(1)
print("[OK] no anti-patterns")
PY
Step F — Differential Verification (the stop condition)
The same runtime engineering patterns that make offensive multi-agent systems converge are the patterns that make defensive multi-analyst teams effective. The skill is symmetric.
SOC playbook parallelism: multiple analysts can update a shared IOC memory in parallel using the same atomic-write protocol. No more "I overwrote your finding" or "we both ran the same hunt." Version vectors and path claims work identically for defensive teams.
Threat hunting coordination: structured hypothesis memory prevents the "tried that, didn't work" repetition that plagues ad-hoc hunting teams. Every hunt writes a hypothesis entry; every dead-end writes a failed_attempt; the next hunter sees what was already tried and pivots instead of repeating.
IOC triangulation: three analysts independently arriving at the same IOC = high-confidence promotion (same convergence logic as exploit-dev hypotheses). The claimed_by field becomes analyst_id; the path field becomes the IOC value.
Detection-as-code multi-author review: shared Sigma / YARA memory with version vector prevents rule-clobbering when two detection engineers edit the same rule in parallel. Same atomic-write pattern; same conflict-resolution rule.
What the defender takes away: the offensive team's shared-memory footprint is itself a detection signal. Multiple afl-fuzz instances, parallel jq writes to a shared JSON, flock contention on a sidecar .lock file — these are high-fidelity indicators that an offensive multi-agent tool is running. Build Sigma rules on the coordination artifacts, not just the individual tool invocations.
This skill embodies several of kali-claw's 12 Hacker Laws:
Law of Composition — multi-agent systems are compositional; the runtime layer (this skill) composes with the decomposition layer (multi-agent-collaboration) and the analysis layer (council). Each must be independent for the system to be reliable.
Law of Convergence — multiple independent evidence vectors arriving at the same conclusion is the strongest signal of truth. The convergence-detection layer codifies this.
Law of Evidence over Assertion — every claim must land in an evidence field; every assertion must be backed by a tool output indexed in evidence_index with a sha256. Prose memory violates this; structured memory enforces it.
Law of Failure-Driven Learning — failed_attempts is a first-class field. Failed explorations are not wasted; they constrain the next iteration via next_constraints.
Law of the Harness (a 2026 addition, codified post-MopMonk) — the harness is the moat, not the base model. A disciplined runtime beats a larger parameter count. This is the meta-principle that justifies treating runtime engineering as a first-class skill domain.
Although this is a meta-skill (engineering patterns, not a specific technique), the offensive multi-agent runtime it codifies maps to multiple ATT&CK techniques when instantiated against real targets:
ATT&CK Technique
Runtime Engineering Pattern
Detection Hint
T1059.004 — Command and Scripting: Unix Shell
Agents execute via bash + jq + flock
Sigma: high-frequency flock syscalls in short window
T1027 — Obfuscated Files or Information
Atomic JSON writes with version vectors obscure intent
Falco: rapid JSON schema evolution in /tmp shared memory
T1106 — Native API
Direct POSIX (flock, mv, kill) instead of higher-level frameworks
Sysmon EID 1: process tree with jq-pipe chains
T1057 — Process Discovery
Coordinator-agent topology = parallel process enumeration
Suricata: many parallel outbound scans from one host
T1070.004 — File Deletion
Anti-pattern cleanup removes evidence of failed attempts
Auditd: unlink() calls on .lock files in /tmp
T1620 — Reflective Code Loading
Runtime-loaded agent skills (no on-disk binary)
YARA: scan Python imports for importlib + eval combos
Defensive teams using the symmetric patterns (see Defense Perspective) map to ATT&CK for Detection / Threat Hunting accordingly.
Defense Evasion Techniques
Memory-Driven Convergence Stealth
Slow memory updates: Pace memory writes below anomaly threshold.
Match existing schema: Use legitimate schema fields; appears as normal memory update.
Trigger-based activation: Memory poisoning activates only on specific input pattern.
Coordination Attack Stealth
Distributed consensus attack: Multiple compromised agents converge on malicious action.
Version vector manipulation: Forge version vectors to bypass conflict resolution.
POSIX flock abuse: Hold lock longer than expected; force serialization of legitimate agents.
Anti-Pattern Exploitation
Free-form exploration abuse: Exploit agents that explore freely; inject poison in exploratory paths.
Memory drift exploitation: Use drifted memory state as attack vector.
Repeat-without-delta abuse: Force agents to repeat without progress; consume resources.
Premature stop exploitation: Trick agents into premature termination; disrupts coordination.
Shared Memory Stealth
Use existing shared state: Don't create new shared memory; abuse existing KV store.
Cross-process token theft: Steal agent token from shared memory; reuse in another agent.
Gradual trust building: Build up reputation in shared state over time; exploit at scale.
Learning Resources
MopMonk Agent (扫地僧) — CyberGym 73.1% case study, docs/mopmonk-research-and-kali-claw-plan.md — kali-claw's internal MopMonk research (2026-06-30), documenting the three-招 (three-layer) harness pattern and the "Harness > Parameters" thesis. This skill is the direct engineering response to that research.
Berkeley CyberGym paper (ICLR 2026) — arXiv:2506.02548, OpenReview 2YvbLQEdYt. The benchmark that made the harness-vs-base-model debate quantitative. 1,507 real vulnerabilities from 188 open-source projects; agent must produce a PoC that triggers on vulnerable build and is clean on patched build.
Anthropic Multi-Agent Research System blog series (2024-2026) — Anthropic's published patterns for multi-agent coordination, including sub-agent delegation, shared state, and convergence detection. Vendor framing, but the primitives are the same as this skill.
LangGraph / LangChain docs — production graph-based agent orchestration with checkpointed state. The "checkpoint" primitive in LangGraph is exactly this skill's atomic-write pattern, expressed in Python instead of shell.
AutoGen paper (Microsoft, 2023-2024) — multi-agent conversation framework. The "GroupChat" manager is one topology choice from this skill's Layer 5; the underlying state sync is the same.
Magentic-One paper (Microsoft, 2024) — generalist multi-agent system with a fixed four-role topology (Organizer / WebSurfer / FileSurfer / Coder). A concrete reference implementation of the hierarchical coordinator-worker topology from Layer 5.
CrewAI docs — role-based agent framework. The "Crew" primitive is parallel-explorers topology with role-specialized agents; useful contrast to shared-memory atomic-write pattern (CrewAI uses task handoff, this skill uses shared file).
OpenAI Swarm (2024) — lightweight handoff-based multi-agent pattern. Contrast: Swarm's handoff is a peer-to-peer message; this skill's atomic-write is a shared-state mutation. The tradeoffs are documented in guides/real-world-incident-case-studies.md.
Cognition Devin public writeups (2024-2025) — long-horizon agent system; public technical posts describe their memory schema and concurrency model. Useful as a real-world reference for schema design at scale.
Structured payloads in payloads.md, complete test cases in test-cases.md, deep-dive playbooks in guides/multi-agent-runtime-engineering-playbook.md and guides/real-world-incident-case-studies.md.
