paperzilla
by K-Dense-AIpaperzilla is a chat-and-CLI skill for working with Paperzilla projects, recommendations, canonical papers, markdown summaries, feedback, and feed export. Use it when you need direct access to Paperzilla data for Academic Research, not just a generic summary. It helps with paperzilla usage, paperzilla guide tasks, and structured output.
This skill scores 78/100, which means it is a solid listing candidate for directory users who want direct access to Paperzilla data through an agent. It has a clear trigger, multiple concrete use cases, and a real CLI-backed install path, though it still leaves some operational details and edge-case behavior to the user or adjacent profile instructions.
- Clear triggerability: the description explicitly says to use it for recent project recommendations, canonical paper details, markdown summaries, feedback, feed export, and Atom feed URLs.
- Operationally useful workflow guidance: the body includes concrete example asks and notes that most profiles use the `pz` CLI.
- Install value is credible: it includes platform-specific install steps for macOS, Windows, Linux, and source builds.
- Workflow boundaries are somewhat thin: it says the skill does not impose a workflow or external delivery integration, so agents may still need adjacent instructions.
- No support files or scripts are present, which reduces confidence in deeper automation or validation beyond the documented CLI usage.
Overview of paperzilla skill
What paperzilla does
paperzilla is a chat-and-CLI skill for working with Paperzilla data: projects, recommendations, canonical papers, markdown summaries, feedback actions, and feed export. It is most useful when you want your agent to retrieve or transform Paperzilla content directly instead of paraphrasing from a pasted snippet.
Best-fit use cases
Use the paperzilla skill when the job is to inspect recent recommendations, open a canonical paper, explain why a paper matters for Academic Research, pull a project feed, or export data as JSON/markdown for downstream use. It is a good fit for researchers, reviewers, and team members who need fast access to structured Paperzilla content.
Why install paperzilla
The main value of paperzilla is direct data access with less prompt guesswork. Instead of asking a generic model to infer project context, the skill gives the agent a clearer pathway for feed URLs, recommendation review, paper summaries, and feedback workflows. That makes the paperzilla guide more reliable when you need actionable output, not just a summary.
How to Use paperzilla skill
paperzilla install and setup
Install paperzilla with the CLI your environment supports, then confirm the pz tool is available before relying on the skill in production. On macOS, the repo documents brew install paperzilla-ai/tap/pz; on Windows, it uses Scoop; on Linux, follow the official CLI getting-started guide. If your profile adds extra agent instructions, treat those as higher-priority usage rules.
What to read first
Start with SKILL.md, then inspect any profile-specific instructions that mention pz, access patterns, or output format. If you are integrating paperzilla into a broader workflow, read the sections that describe what you can ask, access method, and install details before you customize prompts or automation.
How to frame a strong request
Good paperzilla usage starts with a concrete target, not a vague research ask. Strong inputs name the project, paper, or feed, the output shape, and the intended use. For example: “Open the latest recommendation for project X, summarize the rationale in markdown, and export the result as JSON.” That is better than “tell me about project X,” because it tells the skill what to fetch and how to format it.
Practical workflow tips
Use paperzilla for retrieval first, then ask for interpretation. If you need Academic Research support, request the canonical paper plus relevance framing separately so the agent does not mix source lookup with analysis. If you want a feed or export, say that explicitly up front; the skill supports feed URLs and JSON output, but only if the request makes that target clear.
paperzilla skill FAQ
Is paperzilla only for Paperzilla users?
Yes. The paperzilla skill is designed for Paperzilla content and workflows, so it is most useful when your source material already lives in that ecosystem. If you just need a generic paper summary, a normal prompt may be enough.
Is paperzilla useful for Academic Research?
Yes, especially when you want canonical paper details, markdown-based summaries, or a quick explanation of why a paper is relevant to your research. It is strongest when the question depends on Paperzilla records rather than broad literature search.
Do I need the pz CLI?
Usually yes. The repository says most current profiles use the pz CLI, so the cleanest paperzilla install path is to ensure that tool is available and then follow any profile-specific instructions. If your environment blocks CLI use, the skill will be less useful.
When should I not use this skill?
Do not use paperzilla if you need full literature discovery, external database crawling, or a custom review workflow that is not already represented in Paperzilla. In those cases, paperzilla may help with downstream handling, but it is not the primary research engine.
How to Improve paperzilla skill
Give the agent better source boundaries
The biggest quality gain comes from naming the exact project, recommendation, or paper and stating whether you want the latest item, a specific item, or a feed. Ambiguous requests like “summarize the recommendations” often produce weaker retrieval because the agent has to guess scope.
Specify the output shape
If you care about how the result is used, say so. For paperzilla usage, request one of these shapes explicitly: markdown summary, JSON export, feed URL, or a short explanation for Academic Research. This reduces cleanup and makes the output easier to reuse.
Add evaluation criteria
Tell the agent what matters most: recency, canonical status, relevance to a topic, or readiness to share with a teammate. That helps paperzilla avoid overemphasizing surface summary when you actually need decision support.
Iterate on missed details
If the first pass is too generic, correct it with a narrower prompt: name the missing field, the document type, or the context you want preserved. For example, ask for “the rationale only,” “the feed URL only,” or “a markdown summary with no speculation.” That kind of correction improves paperzilla more than asking for a longer answer.