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/jfrog/ai-agent-examples/github-configure-package-managersnpx skills add jfrog/ai-agent-examples --skill github-configure-package-managersgit clone --depth 1 https://github.com/jfrog/ai-agent-examplesWhat 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 | $0.00067 | $0.01514 |
| Opus 5 | $0.00034 | $0.00757 |
| Sonnet 5 | $0.00013 | $0.00303 |
| Haiku 4.5 | $0.00007 | $0.00151 |
Grade C, and why
github-configure-package-managers scanned grade C with 2 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Recursive force deletehighDestructive command
rm -rf with a variable or a broad path is one typo away from removing the wrong tree.
cd / && rm -rf "$TMPDIR" Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
curl -u {username}:{password} -T my-chart-0.1.0.tgz "https://{jfrog-hostname}/artifactory/{project-key}-helm-local/my-chart-0.1.0.tgz" How it starts
The opening of the file, as written. The whole thing — 162 lines — stays where its author put it; the contents beside it link to each section on GitHub.
GitHub Configure Package Managers
Updates package manager configuration files in remote GitHub repositories so that developers resolve dependencies from Artifactory virtual repositories.
Inputs
github_repos-- list of owner/repo (e.g.,["myorg/my-app", "myorg/my-lib"])project_key-- JFrog project key (used in repo naming)ecosystems-- list from:npm,maven,pip,go,docker,helmjfrog_url-- JFrog Platform URL (from$JFROG_URL)github_host-- GitHub host URL (e.g.,https://github.comorhttps://github.mycompany.com)
Approach
For each repo in github_repos:
- Shallow-clone the repo into a temp directory
- Create a feature branch:
jfrog-onboarding - Add/update the appropriate config files
- Push the branch
- Instruct the user to open a PR
Cloning and creating a branch
GITHUB_HOST="https://github.com" # or from manifest
REPO="owner/repo" # from github_repos[]
BRANCH_NAME="jfrog-onboarding" # from manifest github.branch_name
TMPDIR=$(mktemp -d)
git clone --depth 1 "${GITHUB_HOST}/${REPO}.git" "$TMPDIR/repo"
cd "$TMPDIR/repo"
git checkout -b "$BRANCH_NAME"
# ... add/update config files ...
git add -A && git commit -m "chore: configure package managers for JFrog Artifactory"
git push -u origin "$BRANCH_NAME"
cd / && rm -rf "$TMPDIR"
After pushing, instruct the user:
Branch
jfrog-onboardinghas been pushed to{repo}. Please open a PR to merge the package manager configuration changes.
Per-Ecosystem Configuration
npm
File: .npmrc (project root)
Template: templates/package-managers/.npmrc
registry=https://{jfrog-url}/artifactory/api/npm/{project-key}-npm/
//{jfrog-url}/artifactory/api/npm/{project-key}-npm/:_authToken=${JFROG_NPM_TOKEN}
always-auth=true
Maven
File: .mvn/settings.xml
Template: templates/package-managers/settings.xml
- Configures
<mirror>to point all repos to the Artifactory virtual - Configures
<server>with credential placeholders
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 · 162 lines · 67 tokens per session scan C 37aed23385d2
github-configure-package-managers is a skill published in the GitHub repository jfrog/ai-agent-examples (5 stars, last pushed 2mo ago), licensed Apache-2.0. It adds 67 tokens to every session and 1,514 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it C with 2 findings (recursive force delete, makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
Other skills, from other repositories
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
brainstorming
You MUST use this before any creative work - creating features, building components, adding functionality, or modifying behavior. Explores user intent, requirements and design before implementation.
auto-perf-optimize
Run agent-driven VS Code performance or memory investigations. Use when asked to launch Code OSS, automate a VS Code scenario, run the Chat memory smoke runner, capture renderer heap snapshots, take workflow screenshots, compare run summaries, or drive a repeatable scenario before heap-snapshot analysis.
chat-perf
Run chat perf benchmarks and memory leak checks against the local dev build or any published VS Code version. Use when investigating chat rendering regressions, validating perf-sensitive changes to chat UI, or checking for memory leaks in the chat response pipeline.
chat-pet-sprite-creation
Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.
cpu-profile-analysis
Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…