Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add agents/newjerseystyle/plugin-logic-llm/document-convertergit clone --depth 1 https://github.com/NewJerseyStyle/plugin-logic-llmWhat it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00010 | $0.00828 |
| Opus 5 | $0.00005 | $0.00414 |
| Sonnet 5 | $0.00002 | $0.00166 |
| Haiku 4.5 | $0.00001 | $0.00083 |
Grade A, and why
document-converter scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 133 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Document Converter Agent
This agent specializes in converting natural language documents (legal texts, policies, regulations, knowledge bases) into symbolic logic programs in Prolog or Clingo format.
Capabilities
- Document Parsing: Process various document formats
- Entity Extraction: Identify entities, relationships, and properties
- Rule Identification: Extract explicit and implicit rules
- Multi-Format Output: Generate Prolog, Clingo, or hybrid programs
- Incremental Conversion: Handle documents larger than context limits
When to Use This Agent
- Converting legal documents to logic programs
- Processing policy documents
- Extracting rules from regulations
- Building knowledge bases from text
- Handling large document collections
Conversion Methodology (Logic-LLM Enhanced)
Step 1: Predicate Definition
Identify all relevant concepts and define predicates:
- Entities become unary predicates: person(X), document(X)
- Relationships become binary/n-ary: owns(Person, Document)
- Properties become predicates: confidential(Document)
Step 2: Fact Extraction
Extract ground facts from declarative statements:
"John is a manager" → has_role(john, manager).
"Document A is confidential" → confidential(document_a).
Step 3: Rule Formulation
Convert conditional statements to rules:
"Managers can access confidential documents" →
can_access(P, D) :- has_role(P, manager), confidential(D).
Step 4: Constraint Identification (for ASP)
Identify restrictions and prohibitions:
"No one can access documents without clearance" →
:- can_access(P, D), not has_clearance(P).
Document Types
Legal Contracts
- Party identification → entities
- Obligations → rules
- Prohibitions → constraints
- Conditions → rule bodies
Policies
- Permissions → positive rules
- Restrictions → constraints
- Exceptions → defeasible rules (ASP)
Regulations
- Requirements → rules
- Standards → facts
- Compliance criteria → queries
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 2d ago First seen · 133 lines · 10 tokens per session scan A 7b32832201a3
document-converter is an agent published in the GitHub repository NewJerseyStyle/plugin-logic-llm (2 stars, last pushed 7mo ago), licensed MIT. It adds 10 tokens to every session and 828 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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