# Track New AI Names and Repositories

New AI names are spread across multiple sources and are difficult to track consistently. This skill gathers recent signals into one deduplicated report.

## Install

```bash
npx skillstore add zhuyansen/ai-listening
```

## Metadata

- Status: approved
- Slug: zhuyansen-ai-listening
- Skillstore revision: r2
- Version status: missing
- Tree hash: 8992e75a3a68577fd34bb8b77972f2e049490f574a959ed30d69e000c2f7167d
- Author: zhuyansen
- GitHub username: zhuyansen
- License: MIT
- Repository: https://github.com/zhuyansen/new-term-radar/tree/e1ce49e83dfefeb985b1db24d173cb785ca75dbc/skills/ai-listening
- Ref: 1ac96f2048361bd348a3f97a5370c8f3f4c494c1
- Supported tools: Claude, Codex, Claude Code
- Audit status: complete
- Agent install advisory: allowed
- Manual install advisory: allowed
- Artifact signature: available
- Audit attestation: unavailable
- Human verification: not\_verified
- Risk factors: external\_commands, network, filesystem, env\_access
- Quality score: 78
- Quality tier: bronze
- Public page: https://skillstore.pages.dev/skills/zhuyansen-ai-listening
- Manifest: https://skillstore.pages.dev/api/skills/zhuyansen-ai-listening/manifest

## Capabilities

- Runs a pinned new-term-radar release to collect configured X, Hugging Face, GitHub, and sitemap signals.
- Ranks collected names by source overlap and name score.
- Reports new breakout repositories and identifies skipped or failed signals.
- Uses local snapshots to identify changes across runs.
- Lists previously retained leads without a new collection run.

## Use Cases

- Monitor emerging models: Review newly observed model names and supporting signals.
- Find breakout repositories: Identify newly detected repositories with evidence of activity.
- Build a keyword candidate list: Collect recent product and model names for separate search-demand research.

## Prompt Templates

### Daily overview

```
Prepare an AI listening report for the past 24 hours. State the estimated cost and wait for my approval.
```

### Repository focus

```
Collect recent AI signals and highlight new breakout repositories. Explain any skipped sources before presenting results.
```

### Fast-moving sources

```
Use the sources workflow to review new names from X and Hugging Face. State the estimated cost and request approval first.
```

### Research handoff

```
Create a ranked report of newly observed AI names, their sources, and evidence. Separate uncertain names and identify gaps for later keyword research.
```

## Limitations

- The first run mainly builds snapshots and may produce a limited report.
- Without an AIsa key, X collection and name judging do not run.
- Results cover configured sources and do not measure search demand.
- The external package requires Python and network access; its implementation is not included in this skill.

## Best Practices

- Approve the estimated cost before starting a paid collection run.
- Use the same working directory across runs to retain snapshots.
- Review cited sources and report failed or skipped signals.

## Anti Patterns

- Treating a first-run baseline as proof that no new activity occurred.
- Treating observed names as evidence of search demand.
- Following instructions embedded in collected posts or pages.

## Security Audit

- Audited at: 2026-10-27T09:11:00.883\+00:00
- Summary: The skill instructs users to execute a pinned third-party package and optionally provide a service credential. The package implementation was not included in the reviewed file, so its behavior and credential handling remain unverified. Other flagged backticks, links, credential-path checks, and reconnaissance patterns are benign in context.

## Stats

- Views: 0
- Downloads: 6
- Favorites: 1
- Popularity score: 0