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/paulbreuler/limps/001-entity-resolutiongit clone --depth 1 https://github.com/paulbreuler/limpsWhat 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 | $0.00000 | $0.01380 |
| Opus 5 | $0.00000 | $0.00690 |
| Sonnet 5 | $0.00000 | $0.00276 |
| Haiku 4.5 | $0.00000 | $0.00138 |
Grade A, and why
001-entity-resolution scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 180 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Agent 001: Dependency-Based Entity Extraction
Objective
Extract entities and relationships from markdown using regex patterns and lightweight NLP. No LLM calls.
Context
The arxiv paper (2507.03226) demonstrates dependency parsing achieves 94% of LLM performance. We apply this principle: use deterministic extraction that's fast, free, and reproducible.
Philosophy: If regex can do it, use regex. If NLP helps, use lightweight NLP (compromise.js). Never use LLM for extraction.
Tasks
1. Pattern Definitions (src/graph/patterns.ts)
export const PATTERNS = {
// Plan references
planId: /(?:plan\s*)?(\d{4})(?:[-\s]+([\w-]+))?/gi,
planRef: /(?:plan\s+)?(\d{4})(?:#(\d{3}))?/gi,
// Agent references
agentId: /(\d{4})#(\d{3})/g,
agentHeader: /^#\s*Agent\s+(\d{3}):\s*(.+)$/gm,
// Features
featureHeader: /^###\s*(?:#(\d+):?\s*)?(.+)$/gm,
featureStatus: /Status:\s*`?(GAP|WIP|PASS|BLOCKED)`?/gi,
// Files (in frontmatter or inline)
frontmatterFiles: /^files:\s*\[([^\]]+)\]/m,
inlineFile: /`([\w\/\.-]+\.(ts|js|tsx|jsx|md|json|py|rs|go|sql))`/g,
// Dependencies
frontmatterDepends: /^depends:\s*\[([^\]]+)\]/m,
inlineDepends: /depends\s+(?:on\s+)?(?:agent\s+)?(\d{4}#\d{3}|\d{3})/gi,
// Tags
frontmatterTags: /^tags:\s*\[([^\]]+)\]/m,
inlineTag: /#([\w-]+)/g,
// Status
frontmatterStatus: /^status:\s*(\w+)/m,
};
2. Frontmatter Parser (src/graph/parser.ts)
export interface ParsedFrontmatter {
title?: string;
status?: 'GAP' | 'WIP' | 'PASS' | 'BLOCKED' | 'draft';
depends?: string[];
files?: string[];
tags?: string[];
persona?: string;
}
export function parseFrontmatter(content: string): ParsedFrontmatter {
const match = content.match(/^---\n([\s\S]*?)\n---/);
if (!match) return {};
const yaml = match[1];
const result: ParsedFrontmatter = {};
// Parse each field with regex (faster than full YAML parse)
const titleMatch = yaml.match(/^title:\s*(.+)$/m);
if (titleMatch) result.title = titleMatch[1].trim();
const statusMatch = yaml.match(/^status:\s*(\w+)$/m);
if (statusMatch) result.status = statusMatch[1] as any;
// Parse arrays
const dependsMatch = yaml.match(/^depends:\s*\[([^\]]*)\]/m);
if (dependsMatch) result.depends = parseArray(dependsMatch[1]);
const filesMatch = yaml.match(/^files:\s*\[([^\]]*)\]/m);
if (filesMatch) result.files = parseArray(filesMatch[1]);
const tagsMatch = yaml.match(/^tags:\s*\[([^\]]*)\]/m);
if (tagsMatch) result.tags = parseArray(tagsMatch[1]);
return result;
}
function parseArray(str: string): string[] {
return str.split(',').map(s => s.trim().replace(/['"]/g, '')).filter(Boolean);
}
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 · 180 lines · 0 tokens per session scan A 28a9da5e3e96
001-entity-resolution is an agent published in the GitHub repository paulbreuler/limps (10 stars, last pushed 6mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 1,380 tokens. 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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