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⚠ Low Risk — Risk Score 20/100
Last scan:21 hr ago Rescan
20 /100
openclaw-continuous-work
Continuous execution and optimization pipeline skill for OpenClaw. Runs naming audits, content/link audits, module validation, reference mapping, and encoding normalization on a target directory.
Legitimate OpenClaw continuous-work/optimization skill with minor documentation gap around subprocess invocation; no malicious patterns found.
Skill Nameopenclaw-continuous-work
Duration47.4s
Enginepi
✓
Safe to install
Add explicit declaration of subprocess usage in SKILL.md allowed-tools section. Otherwise this skill is safe to deploy.

Findings 3 items

Severity Finding Location
Low
Subprocess shell execution not declared in allowed-tools Doc Mismatch
RunOptimizationPipeline.py uses subprocess.run() to invoke local scripts, but SKILL.md does not declare shell:WRITE in its allowed-tools section. Execution is constrained to hardcoded skill-internal paths, so the practical risk is low.
import subprocess
→ Add shell:WRITE to allowed tools declaration, or move the pipeline orchestration to inline function calls to avoid subprocess entirely.
 Scripts/RunOptimizationPipeline.py:19
Low
Broad filesystem scope not declared Doc Mismatch
NamingAudit.py and ContentLinkAudit.py accept a target path argument and recursively scan the entire directory tree. SKILL.md does not declare filesystem:READ scope for these operations.
for p in root.rglob('*')
→ Document that scripts accept a target directory parameter and perform recursive scans. Declare filesystem:READ scope.
 Scripts/NamingAudit.py:36
Low
File-writing scripts not declared as requiring filesystem:WRITE Doc Mismatch
NormalizeEncoding.py writes normalized UTF-8 bytes back to files; ValidateModuleOrder.py and BuildReferenceMap.py write reports. These require filesystem:WRITE but no allowed-tools declaration exists.
path.write_bytes(new_raw)
→ Add filesystem:WRITE to allowed-tools or scope writes to a designated output directory.
 Scripts/NormalizeEncoding.py:25
ResourceDeclaredInferredStatusEvidence
Shell NONE WRITE ✓ Aligned Scripts/RunOptimizationPipeline.py:19 (import subprocess) and line 25 (subproces…
Filesystem NONE READ ✓ Aligned NamingAudit.py:36 (root.rglob), ContentLinkAudit.py:52 (root.rglob) — reads file…
Filesystem NONE WRITE ✓ Aligned NormalizeEncoding.py:25-29 (writes normalized bytes), ValidateModuleOrder.py:59 …

File Tree

27 files · 36.0 KB · 1142 lines
Python 8f · 704L Markdown 15f · 381L JSON 4f · 57L
├─ ▾ 📁 References
│ ├─ 📝 AcceptanceTemplate.md Markdown 25L · 609 B
│ ├─ 📋 ConflictReport.json JSON 4L · 63 B
│ ├─ 📝 ConflictReport.md Markdown 13L · 396 B
│ ├─ 📝 ContinuousExecutionDirective.md Markdown 18L · 732 B
│ ├─ 📝 GeneralRules.md Markdown 34L · 1.6 KB
│ ├─ 📋 ModuleGraph.json JSON 29L · 758 B
│ ├─ 📝 ModuleGraph.md Markdown 35L · 938 B
│ ├─ 📋 ModuleOrder.json JSON 18L · 370 B
│ ├─ 📋 ModuleOrderReport.json JSON 6L · 95 B
│ ├─ 📝 ModuleOrderReport.md Markdown 14L · 181 B
│ ├─ 📝 ModuleSystem.md Markdown 33L · 1.3 KB
│ ├─ 📝 ModuleTemplate.md Markdown 16L · 202 B
│ ├─ 📝 OptimizationChecklist.md Markdown 8L · 172 B
│ ├─ 📝 OptimizationDirective.md Markdown 18L · 701 B
│ ├─ 📝 OptimizationRules.md Markdown 19L · 825 B
│ ├─ 📝 QualityRubric.md Markdown 38L · 882 B
│ ├─ 📝 ReferenceMap.md Markdown 31L · 1.1 KB
│ └─ 📝 ReportingTemplate.md Markdown 23L · 536 B
├─ ▾ 📁 Scripts
│ ├─ 🐍 BuildModuleGraph.py Python 80L · 2.3 KB
│ ├─ 🐍 BuildReferenceMap.py Python 90L · 3.1 KB
│ ├─ 🐍 ContentLinkAudit.py Python 100L · 2.6 KB
│ ├─ 🐍 DetectRuleConflicts.py Python 106L · 3.3 KB
│ ├─ 🐍 NamingAudit.py Python 88L · 2.5 KB
│ ├─ 🐍 NormalizeEncoding.py Python 54L · 1.2 KB
│ ├─ 🐍 RunOptimizationPipeline.py Python 90L · 3.7 KB
│ └─ 🐍 ValidateModuleOrder.py Python 96L · 2.9 KB
└─ 📝 SKILL.md Markdown 56L · 3.1 KB

Dependencies 1 items

PackageVersionSourceKnown VulnsNotes
Python standard library only N/A stdlib No No third-party packages in requirements.txt or package.json — uses only json, subprocess, pathlib, re, collections, itertools, argparse, argparse

Security Positives

✓ All scripts use only Python standard library — no third-party dependencies that could introduce supply-chain risk
✓ No network requests (no urllib, requests, httpx, socket to remote IPs)
✓ No credential or environment variable harvesting
✓ No base64/encoded payloads or obfuscation techniques
✓ No access to sensitive paths (~/.ssh, ~/.aws, .env, key stores)
✓ subprocess targets are hardcoded local script paths — no arbitrary command injection
✓ File writes are confined to text/UTF-8 normalization or auto-generated markdown/JSON reports
✓ Grep scan found zero matches for credential keywords, reverse shell patterns, or suspicious network patterns