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 skills/nikeyes/stepwise-dev/deep-researchnpx skills add nikeyes/stepwise-dev --skill deep-researchgit clone --depth 1 https://github.com/nikeyes/stepwise-devWhat 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 | $0.00018 | $0.02819 |
| Opus 5 | $0.00009 | $0.01409 |
| Sonnet 5 | $0.00004 | $0.00564 |
| Haiku 4.5 | $0.00002 | $0.00282 |
Grade A, and why
deep-research 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 — 341 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Deep Research Command
You are orchestrating a multi-agent deep research workflow that produces comprehensive, well-cited research reports.
Command Workflow
When the user invokes /stepwise-research:deep-research <topic>, follow these steps:
1. Clarification Phase (Only if Needed)
If the research topic is ambiguous or unclear, ask 1-2 clarifying questions using the AskUserQuestion tool:
- What specific aspect should be prioritized?
- What timeframe or context is relevant?
- Are there specific sources to include/exclude?
Skip this step if:
- Topic is explicit (e.g., "research Docker containerization security")
- User has provided clear context
- Query is self-contained
2. Analyze Query Complexity
Determine the complexity level of the research query to decide how many workers to spawn:
Query Types:
- Simple definition (e.g., "What is Docker?"): 1 worker
- How-to guide (e.g., "How does JWT work?"): 1-2 workers
- Comparison (2 items) (e.g., "React vs Vue"): 2-3 workers
- Comparison (3+ items) (e.g., "Compare top 5 databases"): 3-5 workers
- State-of-the-art (e.g., "Current state of WebAssembly"): 4-6 workers
- Multi-faceted analysis (e.g., "Analyze enterprise AI adoption"): 5-8 workers
- Controversial topic (e.g., "Pros and cons of microservices"): 4-6 workers (ensure balanced perspectives)
3. Generate Sub-Questions
Break the research query into 2-6 focused sub-questions based on complexity:
Example for simple query ("What is Docker?"):
- Sub-question 1: What is Docker and what problem does it solve?
Example for comparison ("PostgreSQL vs MySQL"):
- Sub-question 1: PostgreSQL architecture and performance characteristics
- Sub-question 2: MySQL architecture and performance characteristics
- Sub-question 3: Real-world benchmarks and case studies comparing both
Example for complex research ("State of WebAssembly adoption"):
- Sub-question 1: WebAssembly capabilities and features in 2026
- Sub-question 2: Major frameworks and tools supporting WebAssembly
- Sub-question 3: Enterprise adoption case studies and success stories
- Sub-question 4: Performance benchmarks and limitations
- Sub-question 5: Security considerations and best practices
- Sub-question 6: Future roadmap and emerging use cases
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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 · 341 lines · 18 tokens per session scan A 24f5eea4369f
deep-research is a skill published in the GitHub repository nikeyes/stepwise-dev (24 stars, last pushed 13d ago), licensed Apache-2.0. It adds 18 tokens to every session and 2,819 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 2026-08-30.
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