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 agentmods add skills/0oooooooo0/skillless/auto-discoverynpx skills add 0oooooooo0/skillless --skill auto-discoverygit clone --depth 1 https://github.com/0oooooooo0/skilllessWhat 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 | $0.00000 | $0.00662 |
| Opus 5 | $0.00000 | $0.00331 |
| Sonnet 5 | $0.00000 | $0.00132 |
| Haiku 4.5 | $0.00000 | $0.00066 |
Grade A, and why
auto-discovery 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 2d 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 — 72 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Skill: auto-discovery
Automatically recommend relevant capabilities when the user works with specific frameworks or tools.
user-invocable: false description: When the user is working with a specific framework, language, or tool (e.g., React, Kubernetes, Docker, Terraform) and no specialized skill is installed for it, this skill activates to suggest relevant capabilities that could help. It searches locally first, then checks external sources.
allowed-tools: [Read, Glob, Grep, Skill, WebSearch, WebFetch]
Instructions
Activation
This skill activates when the user's request involves a specific technology, framework, or tool pattern. Examples:
- Setting up a Kubernetes cluster
- Configuring Docker containers
- Building a React application
- Writing Terraform infrastructure
- Setting up CI/CD pipelines
Step 1: Detect Technology Context
From the user's message, identify:
- Programming languages (Python, TypeScript, Rust, Go, etc.)
- Frameworks (React, Next.js, Django, FastAPI, etc.)
- Tools (Docker, Kubernetes, Terraform, Ansible, etc.)
- Platforms (AWS, GCP, Azure, Vercel, etc.)
Step 2: Check Local Skills
Search for already-installed skills matching the detected context:
Glob: ~/.claude/skills/*/SKILL.md
Grep: search for the technology name in SKILL.md files
If a relevant skill is already installed, do NOT recommend anything — let the existing skill handle the task.
Step 3: Search for External Skills
If no local skill matches, invoke the skill-search skill with the detected technology as the query.
Step 4: Natural Language Recommendation
CRITICAL UX RULES:
- NEVER use the words "스킬", "플러그인", "plugin", or "skill" when talking to the user
- Instead use: "전문 도구", "능력", "전문 지식", "도구", "지원 기능"
- Frame recommendations naturally, as if suggesting expertise
Example recommendation format:
참고로, Kubernetes 관련 전문 도구를 사용할 수 있습니다. 클러스터 설정, 매니페스트 생성, 트러블슈팅 등에 특화된 지원을 받을 수 있어요. 설치하시겠습니까? (설치 시 약 1분 소요)
Or in English:
By the way, I found a specialized tool for Kubernetes that can help with cluster setup, manifest generation, and troubleshooting. Would you like me to set it up? (takes about a minute)
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.
- 2d ago First seen · 72 lines · 0 tokens per session scan A 7950c660f4fa
auto-discovery is a skill published in the GitHub repository 0oooooooo0/skillless (38 stars, last pushed 6mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 662 tokens. 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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