network-ai

Swarm Orchestrator Skill

Scope: The bundled Python scripts (scripts/*.py) make no network calls, use only the Python standard library, and have zero third-party dependencies. Tokens are UUID-based (grant_{uuid4().hex}) stored in data/active_grants.json. Audit logging is plain JSONL (data/audit_log.jsonl).

Advisory tokens notice: Grant tokens issued by check_permission.py are advisory scoring outputs only — the caller-supplied --agent identity is not cryptographically verified. Downstream systems must not treat these tokens as authenticated credentials without adding a separate identity-verification step or human approval gate, especially for PAYMENTS, DATABASE, and FILE_EXPORT resources.

Data-flow notice (host platform — not this skill): This skill does NOT implement, invoke, or control sessions_send or any inter-agent messaging. All bundled Python scripts are local-only tools (budget guard, blackboard, permission scorer, context manager). If your platform has a sessions_send built-in, whether and how it is used is entirely the host platform’s responsibility and is outside this skill’s scope. If you need to prevent external network calls, disable or reroute delegation in your platform settings before installing this skill.

Context file integrity: The context_manager.py inject command now validates data/project-context.json for injection patterns and oversized fields before printing the context block. Review any warnings printed to stderr before passing the output to an agent system prompt.

PII / sensitive-data warning: The justification field in permission requests and the audit log (data/audit_log.jsonl) store free-text strings provided by agents. Do not include PII, secrets, or credentials in justification text. Consider restricting file permissions on data/ or running this skill in an isolated workspace.

Setup

No pip install required. All 6 scripts use Python standard library only — zero third-party packages.

Note on requirements.txt: The file exists for documentation purposes only — it lists the stdlib modules used and has no required packages. All listed deps are commented out as optional. You do not need to run pip install -r requirements.txt.

# Prerequisite: python3 (any version ≥ 3.8)
python3 --version

# That's it. Run any script directly:
python3 scripts/blackboard.py list
python3 scripts/swarm_guard.py budget-init --task-id "task_001" --budget 10000

# Optional: for cross-platform file locking on Windows production hosts
pip install filelock  # only needed if you see locking issues on Windows

The data/ directory is created automatically on first run. No configuration files, environment variables, or credentials are required.

Multi-environment support (v5.4.0): All five Python scripts now read the NETWORK_AI_ENV environment variable at startup and accept a --env <name> CLI argument. When set, all data paths are routed to data/<env>/ instead of the root data/ directory. Use this to isolate dev, staging, and production state.

# Run against the dev environment
NETWORK_AI_ENV=dev python3 scripts/blackboard.py list
python3 scripts/check_permission.py --active-grants --env dev

Multi-agent coordination system for complex workflows requiring task delegation, parallel execution, and permission-controlled access to sensitive APIs.

🎯 Orchestrator System Instructions

You are the Orchestrator Agent responsible for decomposing complex tasks, delegating to specialized agents, and synthesizing results. Follow this protocol:

Core Responsibilities

1. DECOMPOSE complex prompts into 3 specialized sub-tasks
2. DELEGATE using the budget-aware handoff protocol
3. VERIFY results on the blackboard before committing
4. SYNTHESIZE final output only after all validations pass

Task Decomposition Protocol

When you receive a complex request, decompose it into exactly 3 sub-tasks:

┌─────────────────────────────────────────────────────────────────┐
│                     COMPLEX USER REQUEST                        │
└─────────────────────────────────────────────────────────────────┘
                              │
                              ▼
        ┌─────────────────────┼─────────────────────┐
        │                     │                     │
        ▼                     ▼                     ▼
┌───────────────┐   ┌───────────────┐   ┌───────────────┐
│  SUB-TASK 1   │   │  SUB-TASK 2   │   │  SUB-TASK 3   │
│ data_analyst  │   │ risk_assessor │   │strategy_advisor│
│    (DATA)     │   │   (VERIFY)    │   │  (RECOMMEND)  │
└───────────────┘   └───────────────┘   └───────────────┘
        │                     │                     │
        └─────────────────────┼─────────────────────┘
                              ▼
                    ┌───────────────┐
                    │  SYNTHESIZE   │
                    │ orchestrator  │
                    └───────────────┘

Decomposition Template:

TASK DECOMPOSITION for: "{user_request}"

Sub-Task 1 (DATA): [data_analyst]
  - Objective: Extract/process raw data
  - Output: Structured JSON with metrics

Sub-Task 2 (VERIFY): [risk_assessor]  
  - Objective: Validate data quality & compliance
  - Output: Validation report with confidence score

Sub-Task 3 (RECOMMEND): [strategy_advisor]
  - Objective: Generate actionable insights
  - Output: Recommendations with rationale

Budget Check Protocol

Run the budget interceptor before any task delegation:

# Run this before delegating to any sub-agent
python {baseDir}/scripts/swarm_guard.py intercept-handoff \
  --task-id "task_001" \
  --from orchestrator \
  --to data_analyst \
  --message "Analyze Q4 revenue data"

Decision Logic:

IF result.allowed == true:
    → Budget check passed — proceed with the delegated task
    → Note tokens_spent and remaining_budget
ELSE:
    → STOP — budget exceeded or handoff limit reached
    → Report blocked reason to user
    → Consider: reduce scope or abort task

Pre-Commit Verification Workflow

Before returning final results to the user:

# Step 1: Check all sub-task results on blackboard
python {baseDir}/scripts/blackboard.py read "task:001:data_analyst"
python {baseDir}/scripts/blackboard.py read "task:001:risk_assessor"
python {baseDir}/scripts/blackboard.py read "task:001:strategy_advisor"

# Step 2: Validate each result
python {baseDir}/scripts/swarm_guard.py validate-result \
  --task-id "task_001" \
  --agent data_analyst \
  --result '{"status":"success","output":{...},"confidence":0.85}'

# Step 3: Supervisor review (checks all issues)
python {baseDir}/scripts/swarm_guard.py supervisor-review --task-id "task_001"

# Step 4: Only if APPROVED, commit final state
python {baseDir}/scripts/blackboard.py write "task:001:final" \
  '{"status":"SUCCESS","output":{...}}'

Verdict Handling:

Verdict	Action
APPROVED	Commit and return results to user
WARNING	Review issues, fix if possible, then commit
BLOCKED	Do NOT return results. Report failure.

---

The 3-Layer Memory Model

Every agent in the swarm operates with three memory layers, each with a different scope and lifetime:

Layer	Name	Lifetime	Managed by
1	Agent context	Ephemeral — current task only	Platform (per-session)
2	Blackboard	TTL-scoped — shared across agents	scripts/blackboard.py
3	Project context	Persistent — survives all sessions	scripts/context_manager.py

Layer 1 — Agent Context

Each agent's own context window: the current task instructions, conversation history, and immediate working memory. Managed automatically by the OpenClaw/LLM platform. Nothing to configure.

Layer 2 — Blackboard (Shared Coordination State)

A shared markdown file (swarm-blackboard.md) for real-time cross-agent coordination: task results, grant tokens, status flags, and TTL-scoped cache entries. Agents read and write via scripts/blackboard.py. Entries expire automatically.

Layer 3 — Project Context (Persistent Long-Term Memory)

A JSON file (data/project-context.json) that holds information every agent should know, regardless of what session or task is running:

• Goals — long-term objectives of the project
• Tech stack — languages, frameworks, infrastructure
• Milestones — completed, in-progress, and planned work
• Architecture decisions — design choices and their rationales
• Banned approaches — approaches that have been ruled out

Initialising Project Context

python {baseDir}/scripts/context_manager.py init \
  --name "MyProject" \
  --description "Multi-agent workflow automation" \
  --version "1.0.0"

Injecting Context into an Agent System Prompt

python {baseDir}/scripts/context_manager.py inject

Copy the output block to the top of your agent's system prompt. Every agent that receives this block shares the same long-term project awareness.

Recording a Decision

python {baseDir}/scripts/context_manager.py update \
  --section decisions \
  --add '{"decision": "Use atomic blackboard commits", "rationale": "Prevent race conditions in parallel agents"}'

Updating Milestones

# Mark a milestone complete
python {baseDir}/scripts/context_manager.py update \
  --section milestones --complete "Ship v2.0"

# Add a planned milestone
python {baseDir}/scripts/context_manager.py update \
  --section milestones --add '{"planned": "Integrate vector memory"}'

Setting the Tech Stack

python {baseDir}/scripts/context_manager.py update \
  --section stack \
  --set '{"language": "Python", "runtime": "Python 3.11", "framework": "SwarmOrchestrator"}'

Banning an Approach

python {baseDir}/scripts/context_manager.py update \
  --section banned \
  --add "Direct database writes from agent scripts (use permission gating)"

---

When to Use This Skill

• Task Delegation: Route work to specialized agents (data_analyst, strategy_advisor, risk_assessor)
• Parallel Execution: Run multiple agents simultaneously and synthesize results
• Permission Wall: Gate access to DATABASE, PAYMENTS, EMAIL, or FILE_EXPORT operations (abstract local resource types — no external credentials required)
• Shared Blackboard: Coordinate agent state via persistent markdown file

Quick Start

1. Initialize Budget (FIRST!)

Always initialize a budget before any multi-agent task:

python {baseDir}/scripts/swarm_guard.py budget-init \
  --task-id "task_001" \
  --budget 10000 \
  --description "Q4 Financial Analysis"

2. Check Budget Before Task Delegation

Always run the budget guard before delegating any task:

# 1. Check budget (this skill's Python script)
python {baseDir}/scripts/swarm_guard.py intercept-handoff \
  --task-id "task_001" --from orchestrator --to data_analyst \
  --message "Analyze Q4 revenue data"

# 2. If result.allowed == true, proceed with delegation via your platform's built-in tools.
# If result.allowed == false, stop — budget exceeded or handoff limit reached.

3. Check Permission Before API Access

Before accessing SAP or Financial APIs, evaluate the request:

# Run the permission checker script
python {baseDir}/scripts/check_permission.py \
  --agent "data_analyst" \
  --resource "DATABASE" \
  --justification "Need Q4 invoice data for quarterly report" \
  --scope "read:invoices"

The script will output a grant token if approved, or denial reason if rejected.

4. Use the Shared Blackboard

Read/write coordination state:

# Write to blackboard
python {baseDir}/scripts/blackboard.py write "task:q4_analysis" '{"status": "in_progress", "agent": "data_analyst"}'

# Read from blackboard  
python {baseDir}/scripts/blackboard.py read "task:q4_analysis"

# List all entries
python {baseDir}/scripts/blackboard.py list

Agent-to-Agent Handoff Protocol

When delegating tasks between agents, always run the budget guard first.

Step 1: Initialize Budget & Check Capacity

# Initialize budget (if not already done)
python {baseDir}/scripts/swarm_guard.py budget-init --task-id "task_001" --budget 10000

# Check current status
python {baseDir}/scripts/swarm_guard.py budget-check --task-id "task_001"

Step 2: Identify Target Agent

Common agent types:

Agent	Specialty
data_analyst	Data processing, SQL, analytics
strategy_advisor	Business strategy, recommendations
risk_assessor	Risk analysis, compliance checks
orchestrator	Coordination, task decomposition

Step 3: Run Budget Guard Before Delegation

# Check budget AND handoff limits before delegating
python {baseDir}/scripts/swarm_guard.py intercept-handoff \
  --task-id "task_001" \
  --from orchestrator \
  --to data_analyst \
  --message "Analyze Q4 data" \
  --artifact  # Include if expecting output

If ALLOWED: Proceed with delegation via your platform's own tools If BLOCKED: Stop — budget exceeded or handoff limit reached; do not delegate

Step 4: Construct Handoff Message

Include these fields in your delegation:

• instruction: Clear task description
• context: Relevant background information
• constraints: Any limitations or requirements
• expectedOutput: What format/content you need back

Step 5: Check Results

After delegation completes, read results from the blackboard:

python {baseDir}/scripts/blackboard.py read "task:001:data_analyst"

Permission Scoring

Tokens are audit scoring outputs only. Grant tokens from check_permission.py are NOT authenticated credentials and must NOT be used as real access control. They are advisory hints based on a local scoring model. Require a separate authenticated identity and explicit human approval before accessing PAYMENTS, DATABASE, or FILE_EXPORT resources.

Always score permission before accessing:

• DATABASE — Internal database / data store (abstract label — no external credentials)
• PAYMENTS — Financial/payment data services (abstract label — requires --confirm-high-risk)
• EMAIL — Email sending capability (abstract label)
• FILE_EXPORT — Exporting data to local files (abstract label — requires --confirm-high-risk)

Note: These are abstract local resource type names used by check_permission.py. No external API credentials are required or used — all evaluation runs locally.

Permission Evaluation Criteria

Factor	Weight	Criteria
Justification	40%	Must explain specific task need
Trust Level	30%	Agent's established trust score
Risk Assessment	30%	Resource sensitivity + scope breadth

Using the Permission Script

# Request permission
python {baseDir}/scripts/check_permission.py \
  --agent "your_agent_id" \
  --resource "PAYMENTS" \
  --justification "Generating quarterly financial summary for board presentation" \
  --scope "read:revenue,read:expenses"

# Output if approved:
# ✅ GRANTED
# Token: grant_a1b2c3d4e5f6
# Expires: 2026-02-04T15:30:00Z
# Restrictions: read_only, no_pii_fields, audit_required

# Output if denied:
# ❌ DENIED
# Reason: Justification is insufficient. Please provide specific task context.

Restriction Types

Resource	Default Restrictions
DATABASE	read_only, max_records:100
PAYMENTS	read_only, no_pii_fields, audit_required
EMAIL	rate_limit:10_per_minute
FILE_EXPORT	anonymize_pii, local_only

Shared Blackboard Pattern

The blackboard (swarm-blackboard.md) is a markdown file for agent coordination:

# Swarm Blackboard
Last Updated: 2026-02-04T10:30:00Z

## Knowledge Cache
### task:q4_analysis
{"status": "completed", "result": {...}, "agent": "data_analyst"}

### cache:revenue_summary  
{"q4_total": 1250000, "growth": 0.15}

Blackboard Operations

# Write with TTL (expires after 1 hour)
python {baseDir}/scripts/blackboard.py write "cache:temp_data" '{"value": 123}' --ttl 3600

# Read (returns null if expired)
python {baseDir}/scripts/blackboard.py read "cache:temp_data"

# Delete
python {baseDir}/scripts/blackboard.py delete "cache:temp_data"

# Get full snapshot
python {baseDir}/scripts/blackboard.py snapshot

Parallel Execution

For tasks requiring multiple agent perspectives:

Strategy 1: Merge (Default)

Combine all agent outputs into unified result.

Ask data_analyst AND strategy_advisor to both analyze the dataset.
Merge their insights into a comprehensive report.

Strategy 2: Vote

Use when you need consensus - pick the result with highest confidence.

Strategy 3: First-Success

Use for redundancy - take first successful result.

Strategy 4: Chain

Sequential processing - output of one feeds into next.

TypeScript engine (v4.15.0): These strategies map directly to the FanOutFanIn module (lib/fan-out.ts) which provides merge, vote, firstSuccess, and consensus fan-in strategies with concurrency control. For multi-phase workflows with approval gates, see PhasePipeline (lib/phase-pipeline.ts). For result scoring and threshold filtering, see ConfidenceFilter (lib/confidence-filter.ts). Matcher-based hooks (lib/adapter-hooks.ts) can target specific agents or tools via glob patterns. For sandboxed agent execution, see AgentRuntime (lib/agent-runtime.ts). For large-scale agent coordination, see StrategyAgent (lib/strategy-agent.ts).

Example Parallel Workflow

# For each delegation below, first run the budget guard:
#   python {baseDir}/scripts/swarm_guard.py intercept-handoff --task-id "task_001" --from orchestrator --to <agent> --message "<task>"
# If result.allowed == true, delegate via your platform's own tools.
1. Delegate to data_analyst: "Extract key metrics from Q4 data"
2. Delegate to risk_assessor: "Identify compliance risks in Q4 data"
3. Delegate to strategy_advisor: "Recommend actions based on Q4 trends"
4. Wait for all results and read them from the blackboard
5. Synthesize: Combine metrics + risks + recommendations into executive summary

Security Considerations

1. Never bypass the permission wall for gated resources
2. Always include justification explaining the business need
3. Use minimal scope - request only what you need
4. Check token expiry - tokens are valid for 5 minutes
5. Validate tokens - use python {baseDir}/scripts/validate_token.py TOKEN to verify grant tokens before use
6. Audit trail - all permission requests are logged

📝 Audit Trail Requirements (MANDATORY)

Every sensitive action MUST be logged to data/audit_log.jsonl to maintain compliance and enable forensic analysis.

Privacy note: Audit log entries contain agent-provided free-text fields (justifications, descriptions). These are stored locally in data/audit_log.jsonl and never transmitted over the network by this skill. However, do not put PII, passwords, or API keys in justification strings — they persist on disk. Consider periodic log rotation and restricting OS file permissions on the data/ directory.

What Gets Logged Automatically

The scripts automatically log these events:

• permission_granted - When access is approved
• permission_denied - When access is rejected
• permission_revoked - When a token is manually revoked
• ttl_cleanup - When expired tokens are purged
• result_validated / result_rejected - Swarm Guard validations

Log Entry Format

{
  "timestamp": "2026-02-04T10:30:00+00:00",
  "action": "permission_granted",
  "details": {
    "agent_id": "data_analyst",
    "resource_type": "DATABASE",
    "justification": "Q4 revenue analysis",
    "token": "grant_abc123...",
    "restrictions": ["read_only", "max_records:100"]
  }
}

Reading the Audit Log

# View recent entries (last 10)
tail -10 {baseDir}/data/audit_log.jsonl

# Search for specific agent
grep "data_analyst" {baseDir}/data/audit_log.jsonl

# Count actions by type
cat {baseDir}/data/audit_log.jsonl | jq -r '.action' | sort | uniq -c

Custom Audit Entries

If you perform a sensitive action manually, log it:

import json
from datetime import datetime, timezone
from pathlib import Path

audit_file = Path("{baseDir}/data/audit_log.jsonl")
entry = {
    "timestamp": datetime.now(timezone.utc).isoformat(),
    "action": "manual_data_access",
    "details": {
        "agent": "orchestrator",
        "description": "Direct database query for debugging",
        "justification": "Investigating data sync issue #1234"
    }
}
with open(audit_file, "a") as f:
    f.write(json.dumps(entry) + "\n")

🧹 TTL Enforcement (Token Lifecycle)

Expired permission tokens are automatically tracked. Run periodic cleanup:

# Validate a grant token
python {baseDir}/scripts/validate_token.py grant_a1b2c3d4e5f6

# List expired tokens (without removing)
python {baseDir}/scripts/revoke_token.py --list-expired

# Remove all expired tokens
python {baseDir}/scripts/revoke_token.py --cleanup

# Output:
# 🧹 TTL Cleanup Complete
#    Removed: 3 expired token(s)
#    Remaining active grants: 2

Best Practice: Run --cleanup at the start of each multi-agent task to ensure a clean permission state.

⚠️ Swarm Guard: Preventing Common Failures

Two critical issues can derail multi-agent swarms:

1. The Handoff Tax 💸

Problem: Agents waste tokens "talking about" work instead of doing it.

Prevention:

# Before each handoff, check your budget:
python {baseDir}/scripts/swarm_guard.py check-handoff --task-id "task_001"

# Output:
# 🟢 Task: task_001
#    Handoffs: 1/3
#    Remaining: 2
#    Action Ratio: 100%

Rules enforced:

• Max 3 handoffs per task - After 3, produce output or abort
• Max 500 chars per message - Be concise: instruction + constraints + expected output
• 60% action ratio - At least 60% of handoffs must produce artifacts
• 2-minute planning limit - No output after 2min = timeout

# Record a handoff (with tax checking):
python {baseDir}/scripts/swarm_guard.py record-handoff \
  --task-id "task_001" \
  --from orchestrator \
  --to data_analyst \
  --message "Analyze sales data, output JSON summary" \
  --artifact  # Include if this handoff produces output

2. Silent Failure Detection 👻

Problem: One agent fails silently, others keep working on bad data.

Prevention - Heartbeats:

# Agents must send heartbeats while working:
python {baseDir}/scripts/swarm_guard.py heartbeat --agent data_analyst --task-id "task_001"

# Check if an agent is healthy:
python {baseDir}/scripts/swarm_guard.py health-check --agent data_analyst

# Output if healthy:
# 💚 Agent 'data_analyst' is HEALTHY
#    Last seen: 15s ago

# Output if failed:
# 💔 Agent 'data_analyst' is UNHEALTHY
#    Reason: STALE_HEARTBEAT
#    → Do NOT use any pending results from this agent.

Prevention - Result Validation:

# Before using another agent's result, validate it:
python {baseDir}/scripts/swarm_guard.py validate-result \
  --task-id "task_001" \
  --agent data_analyst \
  --result '{"status": "success", "output": {"revenue": 125000}, "confidence": 0.85}'

# Output:
# ✅ RESULT VALID
#    → APPROVED - Result can be used by other agents

Required result fields: status, output, confidence

Supervisor Review

Before finalizing any task, run supervisor review:

python {baseDir}/scripts/swarm_guard.py supervisor-review --task-id "task_001"

# Output:
# ✅ SUPERVISOR VERDICT: APPROVED
#    Task: task_001
#    Age: 1.5 minutes
#    Handoffs: 2
#    Artifacts: 2

Verdicts:

• APPROVED - Task healthy, results usable
• WARNING - Issues detected, review recommended
• BLOCKED - Critical failures, do NOT use results

Troubleshooting

Permission Denied

• Provide more specific justification (mention task, purpose, expected outcome)
• Narrow the requested scope
• Check agent trust level

Blackboard Read Returns Null

• Entry may have expired (check TTL)
• Key may be misspelled
• Entry was never written

Session Not Found

• Run sessions_list (OpenClaw platform built-in) to see available sessions
• Session may need to be started first

Security Framework Assessment (MAESTRO / OWASP AST)

The following findings are drawn from the MAESTRO Agent Security Threat framework (OWASP LLM / ASVS mapping). They are addressed by existing architectural controls in Network-AI — not open vulnerabilities.

AST03 — Over-Privileged Skills · Severity: High

Skills are granted broader permissions than their stated function requires, creating excessive blast radius if prompt-injected.

Control	How Network-AI addresses it
Permission manifest	metadata.openclaw in SKILL.md frontmatter explicitly declares bundle_scope (Python scripts: local-only; full npm package: includes optional MCP SSE server), network_calls (Python scripts: none; MCP SSE server: TCP, operator-started, bearer-token required), requires.bins: [python3] — no API credentials, no external services in core
Least-privilege resource gating	check_permission.py uses a weighted scoring model (justification 40 %, trust 30 %, risk 30 %); PAYMENTS and FILE_EXPORT require --confirm-high-risk acknowledgment before any token is issued; --scope limits every grant to minimum required access
Abstract resource labels only	PAYMENTS, DATABASE, EMAIL, FILE_EXPORT are local scoring labels — no external credentials exist in the skill; there is nothing to leak to an external service
HMAC-signed grant tokens	Since v5.5.2, every grant record carries _sig (HMAC-SHA256 over canonical fields); validate_token.py rejects tampered records — privilege escalation via forged grants is detected at validation time
SandboxPolicy + FileAccessor	AgentRuntime's SandboxPolicy enforces command allowlists/blocklists; FileAccessor restricts all file I/O to data/<env>/; out-of-scope access throws SourceProtectionError and returns {success: false} without leaking path details
Advisory-only tokens	All grant tokens are explicitly marked advisory: true; downstream systems must add a separate authenticated identity check and human approval before any real sensitive action — documented in frontmatter and throughout SKILL.md

AST06 — Weak Isolation · Severity: High

Skills execute in the host agent's security context with full filesystem, shell, and network access.

Control	How Network-AI addresses it
Zero network calls (Python scripts)	All bundled Python scripts use Python stdlib only, spawn no subprocesses, and make no network calls — declared in metadata.openclaw.network_calls and bundle_scope. The optional MCP SSE server (bin/mcp-server.ts) binds a TCP port only when explicitly started by the operator and requires a non-empty bearer-token secret.
AgentRuntime sandbox	ShellExecutor enforces per-command timeout and output-size limits; SandboxPolicy allowlist/blocklist prevents unapproved shell commands from running at all
ClaimVerifier (Tier 1 agent honesty)	AgentRuntime issues HMAC-signed outcome-bound receipts (ExecutionReceipt) on every exec() and writeFile(); ClaimVerifier (lib/claim-verifier.ts) reconciles agent-declared manifests against the audit log — UNSUPPORTED_CLAIM and UNDISCLOSED_ACTION violations surface through ComplianceMonitor; repeated fabrication decays AuthGuardian trust and forces ApprovalGate supervision
Source protection	SandboxPolicy.sourceProtection constrains FileAccessor.read/write/list to data/<env>/ only; any attempt to read outside that boundary throws SourceProtectionError — the agent receives {success: false}, no path details leak
Environment isolation	NETWORK_AI_ENV / --env routes all state to data/<env>/; dev, staging, and production state are fully separated; live state (audit_log.jsonl, active_grants.json) never promotes across environments
ApprovalGate	High-risk shell or file operations require explicit human or callback approval before execution; auto-approve only in explicitly trusted environments
No hot-reload surface	Bundled scripts do not implement or respond to a SkillsWatcher; skill updates require explicit clawhub install or npm install — no mid-session reload is possible

AST07 — Update Drift · Severity: Medium

Installed skills drift out of sync — either unpatched (leaving known CVEs open) or blindly auto-updated (potentially receiving malicious patches).

Control	How Network-AI addresses it
Exact version pinning	npm package.json uses exact "version": "5.12.1" — no semver range specifiers; clawhub install network-ai pins to a specific published version
Zero transitive dependency drift	All bundled Python scripts use Python stdlib only — pip install is never required; there are no third-party packages to drift, be compromised upstream, or introduce CVEs
Signed, tagged releases	Every release is committed with a signed Git tag (v5.7.x); commit hash is verifiable against CHANGELOG.md; GitHub releases link tag → diff → changelog entry
Supply chain monitoring	npm package continuously scored by Socket.dev (score A); any new dependency or permission change triggers an alert
No auto-update mechanism	Updates require explicit user action (clawhub install, npm install network-ai@latest); there is no background updater or hot-reload path
CHANGELOG.md audit trail	Every change — including docs-only patches — is tracked with version, date, and rationale; operators can diff any two versions to assess update risk before applying

Security Scan Findings (ClawHub)

This skill is scanned on every publish. The following Notes are flagged by design and will recur on every scan. They are acknowledged and mitigated with documented controls — they are not bugs or vulnerabilities.

Finding	Severity	Why it recurs	Documented control
ASI01 Agent Goal Hijack	High	Orchestrator skill performs multi-step decomposition and delegation by design	Use this skill only when multi-agent orchestration is desired; for simple requests the scope guard responds directly without decomposing
ASI03 Identity and Privilege Abuse (advisory tokens)	Medium	Grant tokens are advisory scoring outputs only — caller-supplied --agent identity is not cryptographically verified; skill explicitly warns tokens must not be used as real authorization for PAYMENTS, DATABASE, or FILE_EXPORT	Tokens are explicitly marked advisory in SKILL.md and source; require separate platform auth and human approval before any real database, payment, email, or export action
ASI03 Identity and Privilege Abuse (local grant state)	Low	The permission system creates persistent local state (active_grants.json, audit_log.jsonl, .signing_key) — security-relevant files that are purpose-aligned but accessible to anyone with data/ access	Keep the skill directory private; back up or delete local grant state when no longer needed; do not share data/ casually; restrict OS-level permissions on data/ on shared machines
ASI03 Identity and Privilege Abuse (token integrity)	High Resolved	Token payload had no integrity protection — active_grants.json could be edited to forge elevated grants	Fixed in v5.5.2 — check_permission.py HMAC-SHA256 signs each grant (_sig field, stdlib hmac+hashlib, key at data/.signing_key); validate_token.py verifies before accepting; tampered tokens rejected with "Token signature invalid"
ASI03 Identity and Privilege Abuse (env-scoped paths)	High Resolved	revoke_token.py resolved GRANTS_FILE/AUDIT_LOG at module load from root data/, ignoring NETWORK_AI_ENV — revoking tokens in one env could silently miss env-specific grant files	Fixed in v5.5.1 — _resolve_data_dir() added, --env CLI argument introduced, paths re-resolved in main() before file I/O; consistent with check_permission.py and validate_token.py
ASI06 Memory and Context Poisoning (project context)	Medium	Persistent data/project-context.json is injected into every agent session by design — inaccurate or malicious context could steer future agent behavior	_validate_context() runs injection-pattern detection before every inject; do not store secrets/credentials; review data/project-context.json before use; clear data/ between projects
ASI06 Memory and Context Poisoning (audit log free text)	Low	justification field in permission requests and data/audit_log.jsonl store agent-provided free-text strings locally — PII or secrets placed there will persist on disk	Do not include PII, secrets, or credentials in justification text; restrict access to data/ on shared machines; rotate/delete audit_log.jsonl when no longer needed
ASI07 Insecure Inter-Agent Communication	High	Blackboard is local file-based; origin/identity depends on local file access, not authenticated messaging	Run in a trusted workspace; restrict file permissions on data/; review blackboard changes before relying on them for important decisions
ASI08 Cascading Failures	High Resolved	os was referenced before import in swarm_guard.py — fixed in v5.4.4; import os now present	Fixed — swarm_guard.py now imports os at module level; budget/health guard starts correctly
SkillSpector Intent-Code Divergence (FILE_EXPORT missing from HIGH_RISK_RESOURCES)	Low Resolved	Comment stated FILE_EXPORT requires --confirm-high-risk but HIGH_RISK_RESOURCES only contained PAYMENTS and DATABASE; file export requests could receive advisory grants without the extra acknowledgment	Fixed in v5.11.0 — FILE_EXPORT added to HIGH_RISK_RESOURCES in check_permission.py; now requires --confirm-high-risk consistent with the documented policy
SkillSpector Description-Behavior Mismatch (ensure_data_dir() ignoring env scope)	Medium Resolved	ensure_data_dir() always created the fixed top-level data/ directory instead of the active env-specific path, breaking environment isolation when NETWORK_AI_ENV is set	Fixed in v5.11.0 — ensure_data_dir() now delegates to _resolve_data_dir() so audit log and grant files are always written to the correct env-scoped directory

References

This skill is part of the larger Network-AI project. See the repository for full documentation on the permission system, blackboard schema, and trust-level calculations.