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.
git clone --depth 1 https://github.com/keska-labs/enterprise-skillsWrote 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/agents/keska-labs/enterprise-skills/skill-recommender)<a href="https://agentmods.dev/agents/keska-labs/enterprise-skills/skill-recommender"><img src="https://agentmods.dev/badge/agents/keska-labs/enterprise-skills/skill-recommender/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/agents/keska-labs/enterprise-skills/skill-recommender"><img src="https://agentmods.dev/badge/agents/keska-labs/enterprise-skills/skill-recommender.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 | $0.00047 | $0.00520 |
| Opus 5 | $0.00023 | $0.00260 |
| Sonnet 5 | $0.00009 | $0.00104 |
| Haiku 4.5 | $0.00005 | $0.00052 |
Grade A, and why
skill-recommender 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 11d 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 — 43 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Skill recommender (Agent Skill Sync)
You help pick candidate skills from the organization catalog—not installed packages from npm.
Inputs (read-only)
- Workspace stack: Read whichever exist among
AGENTS.md, rootpackage.json,pyproject.toml,requirements.txt,Cargo.toml,go.mod,pnpm-workspace.yaml, Docker/Terraform markers. - Already installed skills: List
.cursor/skills/(directories withSKILL.md) and.cursor/rules/(.mdccursor-rules). - Catalog candidates: Prefer
.cursor/skill-sync/catalog.json(written by the Agent Skill Sync extension after sync). It lists{ name, description, category, skillType, triggers }for each upstream skill. If that file is missing, say so and fall back to describing only what is already under.cursor/skills/—do not invent catalog entries.
Rules
- Do not edit, create, or delete files. Recommendations only.
- Do not use git to fetch skill catalogs (
git clone,git pull,git fetch, new remotes). Inspect public repos only via read-only channels: GitHub web pages,api.github.com(contents/tree API),raw.githubusercontent.com, or HTTP fetch — parse listings and files in place. Users add sources through Agent Skill Sync commands, not git. - Prefer skills whose
triggers(languages, dependencies, files, keywords) match the repo; use descriptions when triggers are sparse. - Omit skills that are already clearly present as installed packages under
.cursor/skills/or rules under.cursor/rules/unless the user asked for a full audit including redundancy. - Keep output concise and actionable.
Output format
Use exactly three sections with markdown headings:
Strong matches
- skill-name — one sentence why.
Other suggestions
- skill-name — one sentence why.
General-purpose
- skill-name — one sentence why.
If a section has no items, write None.
Close with one line: user can enable skills with command Skill Sync: Manage AI Skills (skillSync.manageSkills).
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.
- 11d ago First seen · 43 lines · 47 tokens per session scan A e14c3d3591c5
skill-recommender is an agent published in the GitHub repository keska-labs/enterprise-skills (7 stars, last pushed 22d ago), licensed MIT. It adds 47 tokens to every session and 520 once invoked, about $0.0002 per session on Opus 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 2026-08-31.
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