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 runkids/feature-radar --skill feature-radar-scangit clone --depth 1 https://github.com/runkids/feature-radarWrote 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/runkids/feature-radar/feature-radar-scan)<a href="https://agentmods.dev/skills/runkids/feature-radar/feature-radar-scan"><img src="https://agentmods.dev/badge/skills/runkids/feature-radar/feature-radar-scan/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/runkids/feature-radar/feature-radar-scan"><img src="https://agentmods.dev/badge/skills/runkids/feature-radar/feature-radar-scan.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 | $0.00136 | $0.02169 |
| Opus 5.5 | $0.00054 | $0.00868 |
| Sonnet 5.5 | $0.00027 | $0.00434 |
| Haiku 4.5 | $0.00014 | $0.00217 |
Grade A, and why
feature-radar-scan 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 27d 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.
How it starts
The opening of the file, as written. The whole thing — 194 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Scan Opportunities
Discover new feature opportunities and add them to .feature-radar/opportunities/.
Deep Read
Behavioral Directives
Additional directive for this skill:
- Filter aggressively — Create an opportunity file only when you can cite concrete demand evidence or make a clear innovation case. Skip weak signals, such as a single issue with no engagement.
Brainstorm Intake
When the user asked for a scan directly ("scan opportunities", "scan ecosystem", "find new features") and brought no idea of their own, jump directly to ## Workflow.
Phase 1: Core Questions
Ask these one at a time. Prefer multiple-choice when possible.
- Problem space — "What problem are you trying to solve, or what experience do you want to improve?"
- Cross-reference: search existing
opportunities/andarchive/for related themes. - If a match is found, surface it: "Is this related to #{nn} {title}, or a completely different direction?"
- Cross-reference: search existing
- Target user — "Who would benefit from this feature?"
- Offer choices derived from
base.mdProject Context if available.
- Offer choices derived from
- Spark — "What triggered this idea?"
- (A) A pain point from my own usage
- (B) Saw a similar feature in another tool/project
- (C) New technical possibilities (new API, new library)
- (D) Community/user feedback
- (E) Pure creative exploration
Phase 2: Adaptive Depth
After Phase 1, assess idea maturity:
Mature (has clear problem + user + demand signal): → Ask 1 closing question to confirm scope, then proceed to Exit.
Emerging (has problem but fuzzy shape): → Ask up to 3 more questions to sharpen:
- "What does the usage look like in your mind?"
- "Have you seen an implementation you particularly liked?" (if yes, consider creating a
references/entry) - "What's the minimum scope that would feel useful?" (MVP scoping)
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.
- 27d ago Changed · -19 lines · -96 tokens per session 071567935eee
- 1mo ago First seen · 213 lines · 232 tokens per session scan A 3163bedd3383
feature-radar-scan is a skill published in the GitHub repository runkids/feature-radar (13 stars, last pushed 27d ago), licensed MIT. It adds 136 tokens to every session and 2,169 once invoked, about $0.0005 per session on Opus 5.5. 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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