Claude Code Templates is a command-line tool and catalogue for configuring Anthropic’s Claude Code with agents, commands, settings, hooks, integrations, skills, and project templates. Developers use it to browse and install reusable components for their coding workflows. The catalogue includes many of these Claude Code components.
Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/davila7/claude-code-templatesnpx agentmods add skills/davila7/claude-code-templates/deep-researchWrote 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/skills/davila7/claude-code-templates/deep-research)<a href="https://agentmods.dev/skills/davila7/claude-code-templates/deep-research"><img src="https://agentmods.dev/badge/skills/davila7/claude-code-templates/deep-research/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/davila7/claude-code-templates/deep-research"><img src="https://agentmods.dev/badge/skills/davila7/claude-code-templates/deep-research.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Snyk fail
- NVIDIA SkillSpector pass
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 5.1 | $0.00023 | $0.00712 |
| Opus 5.5 | $0.00009 | $0.00285 |
| Sonnet 5.5 | $0.00005 | $0.00142 |
| Haiku 4.5 | $0.00002 | $0.00071 |
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.
Copies of this mod
5 near-identical copies found in the catalogue:
- deep-research — 91% identical, 8 lines differ
- deep-research — 89% identical, 13 lines differ
- deep-research — 89% identical, 17 lines differ
- deep-research — 83% identical, 18 lines differ
- deep-research — 83% identical, 18 lines differ
How it starts
The opening of the file, as written. The whole thing — 117 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Gemini Deep Research Skill
Run autonomous research tasks that plan, search, read, and synthesize information into comprehensive reports.
When to Use This Skill
Use this skill when:
- Performing market analysis
- Conducting competitive landscaping
- Creating literature reviews
- Doing technical research
- Performing due diligence
- Need detailed, cited research reports
Requirements
- Python 3.8+
- httpx:
pip install -r requirements.txt - GEMINI_API_KEY environment variable
Setup
- Get a Gemini API key from Google AI Studio
- Set the environment variable:
Or create aexport GEMINI_API_KEY=your-api-key-here.envfile in the skill directory.
Usage
Start a research task
python3 scripts/research.py --query "Research the history of Kubernetes"
With structured output format
python3 scripts/research.py --query "Compare Python web frameworks" \
--format "1. Executive Summary\n2. Comparison Table\n3. Recommendations"
Stream progress in real-time
python3 scripts/research.py --query "Analyze EV battery market" --stream
Start without waiting
python3 scripts/research.py --query "Research topic" --no-wait
Check status of running research
python3 scripts/research.py --status <interaction_id>
Wait for completion
python3 scripts/research.py --wait <interaction_id>
Continue from previous research
python3 scripts/research.py --query "Elaborate on point 2" --continue <interaction_id>
List recent research
python3 scripts/research.py --list
Output Formats
- Default: Human-readable markdown report
- JSON (
--json): Structured data for programmatic use - Raw (
--raw): Unprocessed API response
Cost & Time
| Metric | Value |
|---|---|
| Time | 2-10 minutes per task |
| Cost | $2-5 per task (varies by complexity) |
| Token usage | ~250k-900k input, ~60k-80k output |
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 Changed · +1 lines 790f36751192
- 1mo ago First seen · 116 lines · 23 tokens per session scan A 837803bb1090
deep-research is a skill published in the GitHub repository davila7/claude-code-templates (32,942 stars, last pushed yesterday), licensed MIT. It adds 23 tokens to every session and 712 once invoked, about $0.0001 per session on Opus 5.5. 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 skills, from other repositories
claude-md-review
Audit a CLAUDE.md file for the patterns that actually degrade Claude Code's output — vagueness, unnamed files, stale facts, and bloat. Use when asked to review, audit, improve, shrink, or fix a CLAUDE.md, and when a project's results feel inconsistent or Claude keeps rediscovering the same context.
five-whys
Finds the root cause of a bug, failure or recurring problem by asking "why" until the answer is a cause you can fix. Use when the user asks why something keeps happening, or wants the root cause rather than a quick fix.
tdd
Build this test-first, one behavior at a time: $ARGUMENTS.
fix
Cross-cutting: when something breaks. Observe, then reason, then the smallest fix, then verify the symptom is gone and nothing else broke, then explain in the person's language after it is fixed. Never show a raw error to a beginner; never blame them.
see
S1 of see-it-ship-it: understand what the person wants from the way they said it. Classify intent silently, detect their level from the first messages, size the task, lock what they confirmed, and ask at most one question that is theirs to answer.
storybrand-messaging-framework
Use StoryBrand for "clarify my message", "rewrite my homepage", "create a BrandScript", "what is my one-liner", or "fix my CTA".