Pharmaclaw Literature Agent

· v2.0.0

Low Risk

Literature mining agent v2.0.0 for novel drug discovery: PubMed/Semantic Scholar + ClinicalTrials Phase II/III + bioRxiv preprints. Novelty scoring, phase/FDA query boosts. Best for latest breakthroughs. Searches PubMed (NCBI E-utilities) and Semantic Scholar for papers related to compounds, targets, diseases, mechanisms, reactions, and catalysts. Returns structured results with titles, authors, abstracts, DOIs, MeSH terms, citation counts, TLDR summaries, and open-access PDFs. Supports paper lookup by DOI/PMID, citation tracking, and related paper discovery. Chains from any PharmaClaw agent (compound name, target, disease) and recommends next agents based on findings. No API keys required. Triggers on literature, papers, publications, PubMed, search papers, citations, references, what's published, research on, studies about, review articles, recent papers, state of the art.

H:0 D:4 A:0 C:1

⚠️ Hazard Flags

NET_EGRESS_ANY PI_WEB

📋 Capabilities

Execution

❌ Shell execution
❌ Code execution
❌ Install dependencies
❌ Persistence
Privilege: user

Filesystem

❌ Read workspace
❌ Write workspace
❌ Read home
❌ Write home
❌ Read system
❌ Delete

Network

Egress: any
❌ Ingress

Credentials

❌ Environment vars
❌ Credential files
❌ Browser data
❌ Keychain

Actions

❌ send messages❌ post public❌ purchase❌ transfer money❌ deploy❌ delete external

🔒 Containment

Level: maximum

Recommended:

LOG_ACTIONS: Audit trail for all actions

⚡ Risks

Social engineering indicators: SOCIAL_ENG_VAGUE_DESCRIPTION low

Mitigation: Provide clear, detailed description of skill functionality

Data exfiltration patterns: DATA_EXFIL_NETWORK_REQUESTS medium

Mitigation: Ensure network access is necessary and documented

Want a deeper analysis?

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🚨 Incident Response

Kill switch: Stop the agent process

Containment: Review logs for unexpected actions

Recovery: Depends on skill capabilities

📄 Raw SSDS JSON click to expand

{
  "meta": {
    "document_id": "ssds:auto:pharmaclaw-literature-agent:2.0.0",
    "ssds_version": "0.2.0",
    "scanner_version": "0.4.0+fe6fd9123d50",
    "created_at": "2026-03-05T15:54:41.298Z",
    "created_by": {
      "agent": "safeagentskills-cli/generate-ssds"
    },
    "language": "en",
    "notes": "Auto-generated SSDS. Manual review recommended."
  },
  "skill": {
    "name": "Pharmaclaw Literature Agent",
    "version": "2.0.0",
    "format": "agent_skill",
    "description": "Literature mining agent v2.0.0 for novel drug discovery: PubMed/Semantic Scholar + ClinicalTrials Phase II/III + bioRxiv preprints. Novelty scoring, phase/FDA query boosts. Best for latest breakthroughs. Searches PubMed (NCBI E-utilities) and Semantic Scholar for papers related to compounds, targets, diseases, mechanisms, reactions, and catalysts. Returns structured results with titles, authors, abstracts, DOIs, MeSH terms, citation counts, TLDR summaries, and open-access PDFs. Supports paper lookup by DOI/PMID, citation tracking, and related paper discovery. Chains from any PharmaClaw agent (compound name, target, disease) and recommends next agents based on findings. No API keys required. Triggers on literature, papers, publications, PubMed, search papers, citations, references, what's published, research on, studies about, review articles, recent papers, state of the art.",
    "publisher": "unknown",
    "source": {
      "channel": "local"
    },
    "artifact": {
      "sha256": "f82f93ee86c910494b3bb819187f38487ef02b152ff40b8b9e3a2a262e9a18ad",
      "hash_method": "files_sorted"
    }
  },
  "capabilities": {
    "execution": {
      "can_exec_shell": false,
      "can_exec_code": false,
      "privilege_level": "user",
      "can_install_deps": false,
      "can_persist": false
    },
    "filesystem": {
      "reads_workspace": false,
      "reads_user_home": false,
      "reads_system": false,
      "writes_workspace": false,
      "writes_user_home": false,
      "writes_system": false,
      "can_delete": false
    },
    "network": {
      "egress": "any",
      "ingress": false
    },
    "credentials": {
      "reads_env_vars": false,
      "reads_credential_files": false,
      "reads_browser_data": false,
      "reads_keychain": false
    },
    "services": [],
    "actions": {
      "can_send_messages": false,
      "can_post_public": false,
      "can_purchase": false,
      "can_transfer_money": false,
      "can_deploy": false,
      "can_delete_external": false
    },
    "prompt_injection_surfaces": [
      "web"
    ],
    "content_types": [
      "general"
    ]
  },
  "hazards": {
    "hdac": {
      "H": 0,
      "D": 4,
      "A": 0,
      "C": 1
    },
    "flags": [
      "NET_EGRESS_ANY",
      "PI_WEB"
    ],
    "custom_flags": [
      {
        "code": "SOCIAL_ENGINEERING",
        "name": "Social Engineering Risk",
        "description": "SOCIAL_ENG_VAGUE_DESCRIPTION: Skill description is too vague or missing"
      },
      {
        "code": "DATA_EXFILTRATION",
        "name": "Data Exfiltration Risk",
        "description": "DATA_EXFIL_NETWORK_REQUESTS: HTTP client library imports that enable external communication"
      }
    ],
    "confidence": {
      "level": "medium",
      "basis": [
        "static_analysis"
      ],
      "notes": "Detected 2 security patterns (5 vendored rule hits). Review recommended."
    },
    "rationale": {
      "H": "H0: No host access",
      "D": "D4: Critical: Credential theft or data exfiltration",
      "A": "A0: No side effects detected",
      "C": "C1: General content"
    }
  },
  "containment": {
    "level": "maximum",
    "required": [],
    "recommended": [
      {
        "control": "LOG_ACTIONS",
        "reason": "Audit trail for all actions"
      }
    ],
    "uncontained_risk": "Risk level depends on manual review of actual capabilities."
  },
  "risks": {
    "risks": [
      {
        "risk": "Social engineering indicators: SOCIAL_ENG_VAGUE_DESCRIPTION",
        "severity": "low",
        "mitigation": "Provide clear, detailed description of skill functionality"
      },
      {
        "risk": "Data exfiltration patterns: DATA_EXFIL_NETWORK_REQUESTS",
        "severity": "medium",
        "mitigation": "Ensure network access is necessary and documented"
      }
    ],
    "limitations": [
      "Static analysis only - runtime behavior not verified"
    ]
  },
  "incident_response": {
    "kill_switch": [
      "Stop the agent process"
    ],
    "containment": [
      "Review logs for unexpected actions"
    ],
    "recovery": [
      "Depends on skill capabilities"
    ]
  },
  "evidence": [
    {
      "evidence_id": "EV:file-1",
      "type": "file_excerpt",
      "title": "scripts/biorxiv_search.py",
      "file_path": "scripts/biorxiv_search.py"
    },
    {
      "evidence_id": "EV:file-2",
      "type": "file_excerpt",
      "title": "scripts/chain_entry_v2.py",
      "file_path": "scripts/chain_entry_v2.py"
    },
    {
      "evidence_id": "EV:file-3",
      "type": "file_excerpt",
      "title": "scripts/chain_entry.py",
      "file_path": "scripts/chain_entry.py"
    },
    {
      "evidence_id": "EV:file-4",
      "type": "file_excerpt",
      "title": "scripts/clinicaltrials_search.py",
      "file_path": "scripts/clinicaltrials_search.py"
    },
    {
      "evidence_id": "EV:file-5",
      "type": "file_excerpt",
      "title": "scripts/pubmed_search.py",
      "file_path": "scripts/pubmed_search.py"
    },
    {
      "evidence_id": "EV:file-6",
      "type": "file_excerpt",
      "title": "scripts/semantic_scholar.py",
      "file_path": "scripts/semantic_scholar.py"
    },
    {
      "evidence_id": "EV:file-7",
      "type": "file_excerpt",
      "title": "SKILL.md",
      "file_path": "SKILL.md"
    },
    {
      "evidence_id": "EV:file-8",
      "type": "file_excerpt",
      "title": "_meta.json",
      "file_path": "_meta.json"
    },
    {
      "evidence_id": "EV:cisco-1",
      "type": "file_excerpt",
      "title": "DATA_EXFIL_NETWORK_REQUESTS [MEDIUM] scripts/biorxiv_search.py:19: import requests",
      "file_path": "scripts/biorxiv_search.py"
    },
    {
      "evidence_id": "EV:cisco-2",
      "type": "file_excerpt",
      "title": "DATA_EXFIL_NETWORK_REQUESTS [MEDIUM] scripts/clinicaltrials_search.py:19: import requests",
      "file_path": "scripts/clinicaltrials_search.py"
    },
    {
      "evidence_id": "EV:cisco-3",
      "type": "file_excerpt",
      "title": "DATA_EXFIL_NETWORK_REQUESTS [MEDIUM] scripts/pubmed_search.py:21: import requests",
      "file_path": "scripts/pubmed_search.py"
    },
    {
      "evidence_id": "EV:cisco-4",
      "type": "file_excerpt",
      "title": "DATA_EXFIL_NETWORK_REQUESTS [MEDIUM] scripts/semantic_scholar.py:20: import requests",
      "file_path": "scripts/semantic_scholar.py"
    },
    {
      "evidence_id": "EV:cisco-5",
      "type": "file_excerpt",
      "title": "SOCIAL_ENG_VAGUE_DESCRIPTION [LOW] SKILL.md:1: ---",
      "file_path": "SKILL.md"
    }
  ]
}