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 commands/stefan-jansen/claude-code-toolkit/exploregit clone --depth 1 https://github.com/stefan-jansen/claude-code-toolkitWhat 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 | $0.00007 | $0.00484 |
| Opus 5 | $0.00003 | $0.00242 |
| Sonnet 5 | $0.00001 | $0.00097 |
| Haiku 4.5 | $0.00001 | $0.00048 |
Grade A, and why
explore 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.
What it actually says
Requirements Exploration
Analyze requirements and codebase context. First step in "Explore → Plan → Code → Commit" workflow.
Input: $ARGUMENTS
Sources
@file.md- Read and analyze document#123- Fetch GitHub issue"description"- Natural language requirement- (empty) - General codebase exploration
Process
-
Create Work Unit
- Generate ID:
YYYY-MM-DD_NN_topic - Create
.claude/work/{id}/with: metadata.json, requirements.md, exploration.md - Set as ACTIVE_WORK
- Generate ID:
-
Analyze Source
- Documents: Extract requirements, identify gaps, assess complexity
- Issues: Fetch details, understand context, map technical needs
- Description: Clarify scope, define success criteria, identify constraints
-
Explore Codebase
- Understand architecture and patterns
- Identify integration points
- Map affected components
- Use Serena for semantic analysis when available
-
Generate Output
requirements.md: Functional/non-functional requirements, acceptance criteria, risksexploration.md: Architecture analysis, implementation approach, key files
-
Smart Planning
- Simple requirements → Generate complete plan + state.json → Ready for /next
- Complex requirements → Generate outline → Recommend /plan
Work Unit Structure
.claude/work/YYYY-MM-DD_NN_topic/
├── metadata.json # Status, created_at, requirement_type
├── requirements.md # Captured requirements
├── exploration.md # Analysis findings
└── state.json # Tasks (if plan auto-generated)
Next Steps
- Clear plan generated: "Run /next to implement"
- Needs refinement: "Run /plan for detailed breakdown"
- Has ambiguities: List clarifying questions first
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 · 61 lines · 7 tokens per session scan A 187109ff71af
explore is a command published in the GitHub repository stefan-jansen/claude-code-toolkit (85 stars, last pushed 1mo ago), licensed MIT. It adds 7 tokens to every session and 484 once invoked, about $0.0000 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 2026-08-30.
Other commands, from other repositories
remember
Save something to agent memory - picks project or user scope automatically from context.
disambiguate-plan
Thorough collaborative planning with iterative disambiguation - resolves all ambiguity before planning.
pr-review
Review a GitHub pull request: analyzes changed files to select relevant reviewers, loads project and user memory, runs reviewers in parallel, lets the user pick which findings to post, then submits the review on behalf of the user via gh CLI.
dataviz
Define chart types, color encoding, and data display conventions.
ship
Pre-launch gate — six-domain checklist before any production deployment.
build
Implement the next ready task from .forge/tasks.yaml using TDD discipline.