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 01-deploy-skillgit 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/01-deploy-skill)<a href="https://agentmods.dev/skills/davila7/claude-with-skills/01-deploy-skill"><img src="https://agentmods.dev/badge/skills/davila7/claude-with-skills/01-deploy-skill/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/01-deploy-skill"><img src="https://agentmods.dev/badge/skills/davila7/claude-with-skills/01-deploy-skill.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 | $0.00034 | $0.00621 |
| Opus 5 | $0.00017 | $0.00311 |
| Sonnet 5 | $0.00007 | $0.00124 |
| Haiku 4.5 | $0.00003 | $0.00062 |
Grade A, and why
python-deploy 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 12d 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
Pre-flight checks
!git status --short
!git log --oneline -5
Deploy to: $ARGUMENTS
If $ARGUMENTS is empty, stop immediately. Report: "No environment specified. Invoke as: /python-deploy (e.g., /python-deploy staging or /python-deploy production)."
Step 1: Abort if there are uncommitted changes
Check the git status output above. If it shows any lines (modified, untracked, staged, or deleted files), stop here.
Report: "Deploy aborted: there are uncommitted changes. Commit or stash them before deploying to $ARGUMENTS."
A clean working tree shows no output from git status --short. Do not proceed unless the output is empty.
Step 2: Run the test suite
Run:
python -m pytest
If any tests fail, report the failing test names and count, then stop. Do not proceed.
If pytest is not installed, try:
python -m unittest discover
If neither is available, report that no test runner was found and stop. Do not deploy without running tests.
Step 3: Build the distribution package
Run:
python -m build
This requires the build package. If it is not installed, run:
pip install build
Then retry python -m build.
If the build fails, report the error output and stop.
On success, confirm the output: the dist/ directory should contain a .whl and a .tar.gz file.
Step 4: Create a deployment tag
Construct the tag name:
deploy-$ARGUMENTS-<YYYYMMDD>-<HHMMSS>
Use current UTC date and time. Example: deploy-staging-20260513-143207.
Run:
git tag deploy-$ARGUMENTS-<YYYYMMDD>-<HHMMSS>
Step 5: Push the tag
Run:
git push origin deploy-$ARGUMENTS-<YYYYMMDD>-<HHMMSS>
If the push fails, report the error and stop.
Step 6: Report success
Print a final summary:
Deploy initiated.
Environment: $ARGUMENTS
Tag: deploy-$ARGUMENTS-<YYYYMMDD>-<HHMMSS>
Time: <YYYY-MM-DD HH:MM:SS UTC>
Build artifacts: dist/
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.
- 12d ago First seen · 98 lines · 34 tokens per session scan A 40c19d61322e
python-deploy is a skill published in the GitHub repository davila7/claude-with-skills (12 stars, last pushed 4mo ago), licensed MIT. It adds 34 tokens to every session and 621 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.
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