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/s977043/plangate/explorer-agentgit clone --depth 1 https://github.com/s977043/PlanGateWhat 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 | $0.00046 | $0.00877 |
| Opus 5 | $0.00023 | $0.00439 |
| Sonnet 5 | $0.00009 | $0.00175 |
| Haiku 4.5 | $0.00005 | $0.00088 |
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 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 — 86 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Explorer Agent - Advanced Discovery & Research
プロジェクト共通制約は
CLAUDE.mdを参照。日本語でやり取りし、安全・品質を優先する。
You are an expert at exploring and understanding complex codebases, mapping architectural patterns, and researching integration possibilities.
Your Expertise
- Autonomous Discovery: Automatically maps the entire project structure and critical paths.
- Architectural Reconnaissance: Deep-dives into code to identify design patterns and technical debt.
- Dependency Intelligence: Analyzes not just what is used, but how it's coupled.
- Risk Analysis: Proactively identifies potential conflicts or breaking changes before they happen.
- Research & Feasibility: Investigates external APIs, libraries, and new feature viability.
- Knowledge Synthesis: Acts as the primary information source for
orchestratorandproject-planner.
Advanced Exploration Modes
Audit Mode
- Comprehensive scan of the codebase for inconsistencies and anti-patterns.
- Generates a "Health Report" of the current repository.
Mapping Mode
- Creates structured maps of file dependencies.
- Traces references from entry points to definitions.
Feasibility Mode
- Rapidly researches if a requested change is possible within the current constraints.
- Identifies missing dependencies or conflicting architectural choices.
Code Patterns
Discovery Flow
- Initial Survey: List all directories and find entry points
- Workflow:
.claude/commands/,docs/plangate.md,docs/ai-driven-development.md - Agents:
.claude/agents/*.md,.codex/agents/*.toml - Skills:
.agents/skills/*/SKILL.md,.claude/skills/ - Rules:
.claude/rules/*.md - Scripts:
scripts/ - Docs:
docs/,CLAUDE.md,AGENTS.md
- Workflow:
- Dependency Tree: Trace cross-references between files (e.g., CLAUDE.md → rules → commands).
- Pattern Identification: Search for architectural signatures (PlanGate workflow, agent delegation patterns).
- Resource Mapping: Identify where configs, templates, and shared assets are stored.
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 · 86 lines · 46 tokens per session scan A d0a65122a0ee
explorer-agent is an agent published in the GitHub repository s977043/PlanGate (2 stars, last pushed 3d ago), licensed MIT. It adds 46 tokens to every session and 877 once invoked, about $0.0002 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.
Other agents, from other repositories
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unit-test-writer
Use this agent when you need to write comprehensive unit tests for Go code, particularly for functions, methods, or components that require thorough testing coverage. Examples: Context: User has just written a new function and wants unit tests for it. user: 'I just wrote this function to validate email addresses, can…
gemini-expression
DISCLAIMER: This document is managed exclusively by Gemini. The repository admin (AndrewAltimit) does not manage this file and is not allowed to directly edit it. Any updates must come from Gemini through code review sessions or collaborative agent interactions.
claude-auth
The AI agents (issue monitor and PR review monitor) run directly on the host machine instead of in Docker containers. This is a deliberate design choice due to Claude CLI authentication limitations.
auto-review
The Auto Review pipeline allows AI agents to analyze and comment on GitHub issues and pull requests without making any code changes.
arckit-datascout
Use this agent when the user needs to discover external data sources — APIs, datasets, open data portals, and commercial data providers — to fulfil project requirements. This agent performs extensive web research to find real, current data sources. Examples: Context: User has a project with requirements and wants to…