taskforge

An installable AI Agent Skill that turns one of your real merged GitHub PRs into a bounded, self-contained candidate coding task — problem-first, so candidates must design and justify a solution, not transcribe one — plus a human- and AI-readable grading guide.

You run it inside Claude Code. Working with you, it:

1. scores a real merged PR's task-suitability and pulls it with the gh CLI,
2. carves the relevant code into a standalone, runnable project,
3. proves it builds + tests offline,
4. turns it into a problem — presents the situation and worked scenarios, never the solution, and asks the candidate to write a NOTES.md explaining their approach and where the AI was wrong,
5. writes a candidate-facing BRIEF.md and a grading guide (EVALUATION.md with explicit rubric),
6. packages a task-bundle.zip.

Fail-closed: never ships a detected secret.

Install (≈1 minute)

In Claude Code:

/plugin marketplace add Heedful-ai/taskforge
/plugin install taskforge

Then ask your agent: "build a candidate coding task from PR <url>".

Requirements

git, gh (authenticated), python3 (3.9+), zip, and docker or podman for the offline build/test check. The skill checks these and tells you what's missing.

What you get

task-bundle.zip
├── task/                  the candidate exercise — this is what you send them
│   ├── BRIEF.md           problem statement (no solution hints)
│   └── <source files>     runnable project with lockfile; deps install from the lockfile
├── EVALUATION.md          grading guide — rubric dimensions, what good looks like, NOTES expectations
├── evaluation/reference/  the team's original solution (one acceptable approach, not the target)
└── context.json           metadata: source PR, role/seniority, run commands, machine-readable rubric

Hand the candidate task/ and grade with EVALUATION.md by hand. Or use heedful.ai to run it properly.

Going further with heedful.ai

Upload the task-bundle.zip to heedful.ai and you get:

• Send a link — candidates get a VS Code environment in the browser with your task pre-loaded, a Claude budget to work with, and a time limit. No setup on their end.
• Recording + timeline — every action is captured. You see a structured timeline of what they did, not just the final result.
• AI collaboration grade — evaluates how they used AI: did they understand what it produced, catch its mistakes, drive it with intent? Graded across four dimensions with evidence from the session.
• Solution comparison — candidate's result is automatically compared to the evaluation/reference/ solution from the bundle, so you see exactly where they landed relative to the team's approach.

The task bundle is the portable unit — generate it once, use it however you want.

Development

Scripts are Python 3 (standard library only). Run the tests — including a real --network=none container check when Docker is available:

python3 -m unittest discover -s test

License

MIT — see LICENSE.