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.
git clone --depth 1 https://github.com/sigistry/marketplaceWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/commands/sigistry/marketplace/domain-discovery)<a href="https://agentmods.dev/commands/sigistry/marketplace/domain-discovery"><img src="https://agentmods.dev/badge/commands/sigistry/marketplace/domain-discovery/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/commands/sigistry/marketplace/domain-discovery"><img src="https://agentmods.dev/badge/commands/sigistry/marketplace/domain-discovery.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What 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 5.1 | $0.00015 | $0.02983 |
| Opus 5 | $0.00008 | $0.01491 |
| Sonnet 5 | $0.00003 | $0.00597 |
| Haiku 4.5 | $0.00002 | $0.00298 |
Grade A, and why
domain-discovery 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 7d 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 — 459 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Domain Discovery Command
Rapidly understand the business domains and entity structure of an unfamiliar codebase. This command is perfect for initial exploration when you have no context about what the system does.
Objective
Quickly identify what business domains exist, what entities the system manages, and how they relate to each other. Think of this as creating a "business map" of the codebase.
Step 1: Quick Discovery Scan
Identify Entry Points
- Main application files (main., app., index.*)
- Configuration files (package.json, requirements.txt, pom.xml, etc.)
- Routing/endpoint definitions
- Database schema files
- API documentation if present
Technology Detection
- Framework and language
- Database type
- API style (REST, GraphQL, gRPC, etc.)
- Frontend framework (if applicable)
Step 2: Entity Identification
Find all business entities in the codebase:
Where to Look
- Database models/schemas
- API response structures
- TypeScript interfaces / Java classes / Python classes
- GraphQL schemas
- ORM model definitions
- Serializers and DTOs
Entity Extraction
For each entity found, document:
## Entity: [EntityName]
**File Location**: `file_path:line_number`
**Description**: [Infer what this entity represents in business terms]
**Attributes**:
| Field Name | Type | Required | Description |
|------------|------|----------|-------------|
| id | string | yes | Unique identifier |
| name | string | yes | [Inferred purpose] |
| status | enum | yes | [Possible values: draft, active, archived] |
| createdAt | datetime | yes | Creation timestamp |
**Business Purpose**: [What business problem or concept this entity models]
**Lifecycle**: [How this entity is created, updated, and deleted]
Step 3: Relationship Mapping
Identify how entities relate to each other:
Relationship Types
- One-to-Many (User has many Orders)
- Many-to-Many (Students have many Courses, Courses have many Students)
- One-to-One (User has one Profile)
- Hierarchical (Comment has parent Comment)
- Polymorphic relationships
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.
- 7d ago First seen · 459 lines · 15 tokens per session scan A 911a73f01b5a
domain-discovery is a command published in the GitHub repository sigistry/marketplace (3 stars, last pushed today), licensed MIT. It adds 15 tokens to every session and 2,983 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 2026-09-03.
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