ANOLISA is a server-side operating layer for AI agent workloads that provides terminal access, token-saving tool-output compression, runtime controls, security, observability, skills, memory, and sandbox management. It is for running and supervising agents from a Linux terminal while retaining an existing shell, agent framework, and sandbox. The catalogue add-ons are components of its agent operating environment and workflows.
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 alibaba/anolisa --skill synonymsgit clone --depth 1 https://github.com/alibaba/anolisaWrote 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/alibaba/anolisa/synonyms)<a href="https://agentmods.dev/skills/alibaba/anolisa/synonyms"><img src="https://agentmods.dev/badge/skills/alibaba/anolisa/synonyms/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/alibaba/anolisa/synonyms"><img src="https://agentmods.dev/badge/skills/alibaba/anolisa/synonyms.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 | $0.00035 | $0.00391 |
| Opus 5.5 | $0.00014 | $0.00156 |
| Sonnet 5.5 | $0.00007 | $0.00078 |
| Haiku 4.5 | $0.00003 | $0.00039 |
Grade A, and why
synonyms 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 2mo 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.
This is a copy
100% identical to synonyms — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
What it actually says
This skill helps generate synonyms and alternative expressions for given words or phrases. It provides contextually appropriate alternatives to enhance vocabulary and improve writing variety.
The user provides a word, phrase, or sentence where they need synonym suggestions. They may specify the context, tone, or formality level desired.
Synonym Generation Guidelines
When generating synonyms, consider:
- Context: The specific domain or situation where the word will be used
- Tone: Formal, informal, neutral, academic, conversational, etc.
- Nuance: Subtle differences in meaning between similar words
- Register: Appropriate level of formality for the intended audience
Output Format
For each input word or phrase, provide:
- Direct Synonyms: Words with nearly identical meanings
- Related Alternatives: Words with similar but slightly different connotations
- Context Examples: Brief usage examples when helpful
Best Practices
- Prioritize commonly used synonyms over obscure alternatives
- Note any subtle differences in meaning or usage
- Consider regional variations when relevant
- Indicate formality levels (formal/informal/neutral)
- Provide multiple options to give users choices
Example
Input: "happy"
Synonyms:
- Direct: joyful, cheerful, delighted, pleased, content
- Informal: thrilled, stoked, over the moon
- Formal: elated, gratified, blissful
- Subtle variations:
- content - peaceful satisfaction
- ecstatic - intense, overwhelming happiness
- cheerful - outwardly expressing happiness
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.
- 2mo ago First seen · 49 lines · 35 tokens per session scan A b46efbcc0db9
synonyms is a skill published in the GitHub repository alibaba/anolisa (603 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 35 tokens to every session and 391 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 100% identical to synonyms, differing in 0 lines, and is treated as a copy.
synonyms compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| synonymsalibaba/anolisa | Skill | 603 | 1mo ago | Apache-2.0 | A | 35 | Claude Code, Codex |
| appsdesplega-ai/agent-swarm | Skill | 1.0k | today | MIT | A | 43 | Claude Code |
| kapso-whatsappdesplega-ai/agent-swarm | Skill | 1.0k | today | MIT | A | 156 | Claude Code |
| swarm-scriptsdesplega-ai/agent-swarm | Skill | 1.0k | today | MIT | A | 100 | Claude Code |
| composiodesplega-ai/agent-swarm | Skill | 1.0k | today | MIT | A | 128 | Claude Code |
| motion-design-video-analysisdesplega-ai/agent-swarm | Skill | 1.0k | today | MIT | A | 107 | Claude Code |
| open-prompt-showreeldesplega-ai/agent-swarm | Skill | 1.0k | today | MIT | A | 181 | Claude Code |