by alexgreensh
Make the most out of your subscriptions. Delegate work to other harnesses and models, while keeping your main session the orchestrator. Nothing new to learn. Keep working like you do, but better.
# Add to your Claude Code skills
git clone https://github.com/alexgreensh/outsourcererLast scanned: 7/15/2026
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}outsourcerer is an open-source ai agents skill for AI coding assistants such as Claude Code, Codex CLI, and ChatGPT, built by alexgreensh. Make the most out of your subscriptions. Delegate work to other harnesses and models, while keeping your main session the orchestrator. Nothing new to learn. Keep working like you do, but better. It has 102 GitHub stars.
Yes. outsourcerer passed SkillsLLM's automated security scan — a dependency vulnerability audit plus prompt-injection heuristics — with no high-severity issues. You can read the full report in the Security Report section on this page.
Clone the repository with "git clone https://github.com/alexgreensh/outsourcerer" and add it to your Claude Code skills directory (see the Installation section above).
outsourcerer is primarily written in Shell. It is open-source under alexgreensh on GitHub, so you can review or fork the full source.
Yes. SkillsLLM lists many other AI Agents skills you can browse and compare side by side. Open the AI Agents category from the badge at the top of this page, or use the Related Skills and comparison links further down to weigh outsourcerer against similar tools.
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Outsourcerer hands the grunt work to the cheapest engine you already pay for, brings in a stronger one (or a whole panel of them) when it matters, and shows you the receipt.
You already pay for a fleet of AIs: Claude, Codex, Gemini, maybe Devin, a stack of OpenRouter credits. Each is brilliant at something the others aren't, and they sit in separate rooms. Outsourcerer makes them work as one team. It sends the boring work to the cheapest engine that can nail it, pulls a top-tier model in when the job actually needs a big brain, keeps a running receipt of what that saved you, and, uniquely, clones your setup onto whichever engine runs the job.
When Outsourcerer hands a job to a different engine or harness, that engine doesn't show up empty-handed. It carries your setup with it: the same skills, the same plugins, the same MCP servers your main agent uses. A cheap model running on Devin can use your custom skill and reach your MCP tools exactly the way your Claude session would. It's the difference between borrowing a stranger's bare laptop and having your own, fully set up, wherever the work happens to run.
You're already smart about this. Even inside Claude Code you hand the small stuff to Sonnet instead of Opus. Good instinct, Outsourcerer just takes it further than any single harness can:
The models and the subscriptions are already on your machine. You could stand up a full multi-agent system to connect them, and sometimes that's exactly the right call. Outsourcerer is the lighter path for when you just want the work moved to the right engine and the savings counted.
No flags to memorize, no routing tables. You say what you want; it checks what you actually have installed, picks the right engine, and offers you the cheap path before spending a cent it didn't have to.
If something isn't installed, it tells you the one command to fix it. You just say yes.
The first time it wakes up in a session, Outsourcerer shows you what's ready, how much of your token budget is left, and asks one question: how do you want to drive?
Pick once and it remembers. Change your mind in plain language any time ("take the wheel", "ask me first"). And no mode is a trap: safety, cloud consent, and your sign-off on anything risky hold in all three. Beginners aren't dropped at a command line, they're handed a steering wheel and told what each pedal does.
You know the feeling: 2pm, deep in a build, and your 5-hour limit taps out. Work stops.
Outsourcerer reads your live usage and, the moment you cross into the danger zone, quietly shifts the heavy lifting off your main model onto a cheaper lane that's still full, and tells you why it did. Your best model stays sharp for the judgment calls instead of getting spent on grep. The wall stops being a surprise you slam into and becomes a corner the sorcerer already saw coming. Different setup, same care: no Devin? It leans on Codex. Only local models? It keeps everything on your machine. It conserves with whatever you actually have.
You shouldn't have to memorize which model is best at what, or keep up with a leaderboard that shuffles every week. Ask Outsourcerer to pick, and it does the homework for you: it reads what the task really is (code? reasoning? a quick errand?), pulls live benchmark scores for every model you can reach, weighs quality against what each one actually costs you, and recommends the best value for this job, not just the most famous name.
That means a frontier-grade answer on a budget lane when the cheap model genuinely clears the bar, and a premium model only when the task truly earns it. It shows you the score and the reason it chose what it chose, so the pick is never a black box, and you can always overrule it. When it can't reach the live boards, it falls back to a built-in capability map, so the recommendation is never worse than a good default.
This is the same brain your copilot uses to choose. In auto-pilot it scores the models, takes the best value, and just proceeds (telling you which and why). In you-drive or hybrid it scores them the same way, then shows you the pick and waits for your nod. The benchmarks drive the choice in every mode; the mode only decides whether it asks first.
Delegation runs both directions. Push work down to a cheaper model to save money, or pull the strongest models up as advisors to make the work better, and not just one:
A second, differently-wired head at the right moment is where the real leverage is. Three of them, agreeing, is even better.
The models don't think alike, so Outsourcerer doesn't prompt them alike. The right prompting techniques are baked in per capability tier: it frames a task for GPT-5.6 one way, for Fable another, for a genuinely small model a third, each to its strengths. And cheap does not mean dumb: GLM-5.2, Hy3 and DeepSeek are capable tier, frontier capability at a budget price (~Opus-4.8 class), so they get the same high-autonomy prompting as the flagships, not a hand-holding work order. You ask in plain language; the sorcerer translates it into the dialect each engine actually responds to. You get better output from the cheap lane than you'd get by sending it the prompt you'd send Claude. Dial thinking depth per task with --effort.
fanout, watch them live, and collect every finding into one file. No 16-session bootstrap tax.