Claude With Skills is a progressive course that teaches developers to create reusable, portable Agent Skills for Claude Code, from basic SKILL.md files to advanced automation and plugin packaging. It is intended for developers who want repeatable instructions and workflows instead of repeatedly pasting the same guidance. The catalogue contains the course's skills, agents, and instruction.
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 skills add davila7/claude-with-skills --skill deep-researchgit clone --depth 1 https://github.com/davila7/claude-with-skillsWrote 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-with-skills/deep-research)<a href="https://agentmods.dev/skills/davila7/claude-with-skills/deep-research"><img src="https://agentmods.dev/badge/skills/davila7/claude-with-skills/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-with-skills/deep-research"><img src="https://agentmods.dev/badge/skills/davila7/claude-with-skills/deep-research.svg" alt="Reviewed on agentmods" width="80" 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 5.1 | $0.00048 | $0.00515 |
| Opus 5 | $0.00024 | $0.00258 |
| Sonnet 5 | $0.00010 | $0.00103 |
| Haiku 4.5 | $0.00005 | $0.00052 |
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 11d 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
Research the following topic thoroughly: $ARGUMENTS
Steps
-
Identify the scope of the research. Parse the topic into concrete searchable terms: class names, function names, file name patterns, configuration keys, or framework-specific terms.
-
Search broadly before reading deeply:
- Use Grep to search for the most distinctive identifiers (class names, function names, error strings)
- Use Glob to find files by name pattern (e.g.,
*auth*,*middleware*,*.config.*) - Prioritize files that appear in multiple search results — they are likely central to the topic
-
Read the most relevant files completely. Do not skim. Missing a detail in a critical file leads to incorrect conclusions.
-
For key files, check recent git history to understand why things are the way they are:
git log --oneline --follow -20 <filepath>Read the most informative commit messages. If a commit message references a PR or issue number, note it.
-
Follow import and dependency chains one level deep. If the main file imports from three other modules, read those modules too.
-
Synthesize findings into a structured report with these sections:
Overview What this system or component does, in two to four sentences. Write for someone who has never touched this codebase.
Key files A table: file path | one-line description of its role. Include every file you read that was materially relevant.
How it works The main flow, data structures, and key design decisions. Use concrete terms: function names, type names, config keys. Reference file paths and approximate line numbers for the most important details.
Recent changes What changed in the last 20 commits in the relevant area, and why (based on commit messages and diff context).
Open questions Anything that is unclear from the code alone and would benefit from asking a team member or reading documentation. Be specific.
-
Be precise. If you are uncertain, say so. Do not infer things that are not in the code.
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.
- 11d ago First seen · 49 lines · 48 tokens per session scan A 8b5a4b1308e3
deep-research is a skill published in the GitHub repository davila7/claude-with-skills (12 stars, last pushed 3mo ago), licensed MIT. It adds 48 tokens to every session and 515 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 2026-08-30.
Other skills, from other repositories
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
local-ai-agents
Build local-first AI agents that run entirely on a developer workstation with Microsoft Foundry Local and Qwen function-calling models. Covers Small Language Models (SLMs), the OpenAI-compatible local endpoint, sandboxed local tools, local RAG with Chroma, local MCP servers, hybrid cloud/local routing, and the…
next-cache-components-adoption
Turn on Cache Components in a Next.js app and resolve the blocking routes it surfaces. Use when the user wants to enable, adopt, or migrate to Cache Components, flip the cacheComponents flag, work through a flood of blocking-prerender / instant validation errors, run the cache-components-instant-false codemod, or…
insight-error-page
Write or audit an insight-kind error page for the Next.js dev overlay. Use when creating a new errors/ .mdx page, auditing an existing one, or checking that a page matches the framework fix cards. Covers page structure, title alignment, FixCard cards with Copy prompt button, code snippets, terminology verification…
next-cache-components-optimizer
Drive a Next.js route to instant navigation by setting up an agentic loop, under Cache Components / PPR, on initial load (hard navigation) and client-side navigation (soft navigation). Encode the goal as a failing @next/playwright instant() e2e and work it to green, one verified route at a time; the shipped test then…
next-partial-prefetching-adoption
Turn on Partial Prefetching in a Next.js app and work through the insights it surfaces. Use when the user wants to enable or adopt Partial Prefetching, flip the partialPrefetching flag, opt routes in with export const prefetch = 'partial', audit Link prefetch={true} behavior, preserve existing prefetched UI with…