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/parkerm2/create-claude-workflow/deep-researchgit clone --depth 1 https://github.com/ParkerM2/create-claude-workflowWrote 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/parkerm2/create-claude-workflow/deep-research)<a href="https://agentmods.dev/commands/parkerm2/create-claude-workflow/deep-research"><img src="https://agentmods.dev/badge/commands/parkerm2/create-claude-workflow/deep-research.svg" alt="Measured on agentmods" 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 | $0.00036 | $0.04982 |
| Opus 5 | $0.00018 | $0.02491 |
| Sonnet 5 | $0.00007 | $0.00996 |
| Haiku 4.5 | $0.00004 | $0.00498 |
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 3d 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 — 578 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/deep-research — Conversational Deep Research
Deeply research a topic before making decisions. Works through phases with user checkpoints between each — you control the direction. Each layer of research is cross-referenced against the goal, prior findings, and codebase reality before presenting. Produces a research report, not a design doc. Use
/new-planafter to turn findings into an implementation plan.
When to Use
- Exploring a new technology, pattern, or approach before committing to it
- Investigating how other tools/frameworks solve a problem you're facing
- Understanding the current codebase deeply before planning changes
- Comparing multiple approaches with trade-offs before choosing one
- Gathering context that will inform a future
/new-planor/agent-team - Any question where "just try it" is too risky and you need data first
When NOT to Use
- You already know what to build — use
/new-planor/agent-teamdirectly - Simple factual question — just ask directly without invoking a skill
Core Principle: Research Before Presenting
Never present raw findings directly. Every piece of information goes through a validation layer before the user sees it:
Raw finding → Cross-reference against goal → Validate against prior research → Check against codebase → Assess confidence → THEN present
If you can't validate a finding, say so explicitly with the reason. Don't present unvalidated information as fact.
How It Works
Phase 1: Scope → Define question, dimensions, success criteria
↓ checkpoint
Phase 2: Explore → Broad research across all dimensions
↓ internal validation (DO NOT present raw findings)
Phase 2.5: First Pass → Cross-reference, filter, assess confidence
↓ checkpoint (present validated findings only)
Phase 3: Analyze → Deep dive on user-selected threads (ITERATIVE)
↓ validate each round against goal + prior context
↓ checkpoint per round (user can loop or advance)
Phase 4: Synthesize → Final validation pass, compile report
↓ checkpoint (user can refine)
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.
- 3d ago First seen · 578 lines · 36 tokens per session scan A b7c51f6269ca
deep-research is a command published in the GitHub repository ParkerM2/create-claude-workflow (4 stars, last pushed 5mo ago), licensed MIT. It adds 36 tokens to every session and 4,982 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 commands, from other repositories
git
Git operations with intelligent commit messages and workflow optimization.
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.