Skill radar

Agent Skill Radar

Use this skill to find project opportunities from GitHub evidence instead of brainstorming from scratch.

Workflow

1. Run the radar CLI.
2. Read the top-scoring repos and issue samples.
3. Pick one opportunity with a narrow companion-tool shape.
4. Generate a build brief, fake-door probe, README angle, and launch post.
5. Save the report under reports/ and keep the data under data/.

Commands

Run the default daily scan:

agent-skill-radar run

Collect a custom scan:

agent-skill-radar collect \
  --query "agent memory coding assistant stars:>100 created:>=2025-01-01" \
  --limit-per-query 12 \
  --issue-limit 6 \
  --out data/custom.json

Render a report:

agent-skill-radar report --input data/custom.json --out reports/custom.md

Interpretation Rules

• Treat build-now as a proof-of-concept signal, not permission to build a large product.
• Prefer a companion tool, plugin, skill pack, template, benchmark, or CLI wrapper over a fork.
• Avoid topics where the only signal is stars. Look for issue comments, reactions, recurring workarounds, and ecosystem gaps.
• For giant repositories, build around the ecosystem edge: docs, config, migration, compatibility, monitoring, security, or publishing workflows.
• If the same gap appears across 3 or more repos, promote it to a standalone project idea.

Output Template

For each shortlisted idea, write:

Project:
Target user:
Observed demand:
Repo evidence:
Why upstream will not solve it:
Smallest useful artifact:
Distribution wedge:
Two-week scope:
Fake-door question:

Probe Template

Use a neutral validation comment. Do not pitch too early.

I have seen this come up in a few agent-tooling repos. My current workaround is <specific workaround>.

I am considering a small standalone <CLI / skill / MCP server / template> that does <specific job> without requiring upstream changes.

Before building it: would this solve your case, or is the real blocker different?