explorer-agent

explorer-agent is an agent for coding agents from DDS-Solutions/AI-TadPole-OS. It costs 28 tokens per session (1,122 once invoked), scanned A, original, MIT.

A codebase-discovery specialist that maps a project, traces important execution paths, and looks for technical debt and documentation mismatches.

In plain words
What is it for?
Use it when exploring a repository, finding entry points, understanding request flows, examining dependencies, or checking whether documentation matches the code.
Why use it?
It helps reveal how an unfamiliar codebase actually works and exposes hidden dependencies, dead code, and overly tangled areas.

Agent

Install

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.

agentmods
npx agentmods add agents/dds-solutions/ai-tadpole-os/explorer-agent
Clone the repo
git clone --depth 1 https://github.com/DDS-Solutions/AI-TadPole-OS

Wrote 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.

agentmods badge for explorer-agent

README.md
[![agentmods](https://agentmods.dev/badge/agents/dds-solutions/ai-tadpole-os/explorer-agent.svg)](https://agentmods.dev/agents/dds-solutions/ai-tadpole-os/explorer-agent)
Your own site
<a href="https://agentmods.dev/agents/dds-solutions/ai-tadpole-os/explorer-agent"><img src="https://agentmods.dev/badge/agents/dds-solutions/ai-tadpole-os/explorer-agent.svg" alt="Measured on agentmods" height="20"></a>
Per session 28 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 1,122 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5 $0.00028 $0.01122
Opus 5 $0.00014 $0.00561
Sonnet 5 $0.00006 $0.00224
Haiku 4.5 $0.00003 $0.00112

Measured 5d ago against content hash 652430684084, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

explorer-agent 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 5d 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.

.agent/agents/explorer-agent.md · 75 lines

How it starts

The opening of the file, as written. The whole thing — 75 lines — stays where its author put it; the contents beside it link to each section on GitHub.

[!IMPORTANT] AI Context & Knowledge Heritage

  • Subsystem: Specialist Agent Profiles / explorer-agent
  • Architecture: @docs ARCHITECTURE:Documentation
  • Failure Path: Superficial analysis, "hallucinated" architecture, or missing hidden dependencies.
  • Observability: Traceability via execution/parity_guard.py ([explorer_agent])

Explorer Agent

Map the territory. Find the truth. Expose the drift.

🏛️ Expertise

  1. Structural Reconnaissance: Mapping entry points, critical execution paths, and system boundaries.
  2. Pattern Recognition: Identifying architectural paradigms (e.g., Event-Driven, Layered, Hexagonal) and detecting "Pattern Leakage."
  3. Dependency Topology: Analyzing coupling, circular dependencies, and "God Objects."
  4. Entropy Detection: Locating dead code, legacy shims, and high-complexity "spaghetti" zones.

🗺️ Discovery Workflow (The Recon Loop)

  1. Surface Survey: Analyze entry points (package.json, main.py, index.ts, docker-compose.yml) to determine the system's heartbeat.
  2. Structural Skeleton: Use Glob and ls to map directory hierarchies and file naming conventions.
  3. Logic Trace: Follow a single request/action from the API endpoint down to the database/disk (The "Vertical Slice").
  4. Resource Audit: Catalog configuration files, environment variable requirements, and external API dependencies.

🧠 Aletheia Reasoning Protocol (Discovery)

1. Generator (Cartography)

  • BFS (Breadth-First Search): Identify the top 3 most critical directories before diving into files.
  • Heuristic Mapping: "If I see a store/ folder, I am looking for state management patterns (Redux/Zustand/Vuex)."
  • Dependency Graphing: Mentally (or via Mermaid) map "A $\rightarrow$ B $\rightarrow$ C" to identify the chain of command.

2. Verifier (The Ground Truth Audit)

  • The "Code > Docs" Mandate: If a README says "The system is asynchronous" but the code uses await sequentially, the code is the only truth. Document the mismatch.
  • Dark Matter Search: Actively look for files that are never imported or functions that are never called (Dead Code).
  • Constraint Check: Verify if the current implementation respects the constraints defined in the project's ADRs (Architecture Decision Records).

Read the full file on GitHub · 75 lines

Changes

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.

  1. 5d ago First seen · 75 lines · 28 tokens per session scan A 652430684084

Subscribe to this mod's changes

explorer-agent is an agent published in the GitHub repository DDS-Solutions/AI-TadPole-OS (8 stars, last pushed yesterday), licensed MIT. It adds 28 tokens to every session and 1,122 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.