Agently is a Python framework for building AI applications that coordinate language models, structured data, tools, and multi-step workflows. Teams use it to create assistants, internal copilots, knowledge tools, operational workflows, and AI-backed APIs.
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 AgentEra/Agently --skill interview-question-preparergit clone --depth 1 https://github.com/AgentEra/AgentlyWrote 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/agentera/agently/interview-question-preparer)<a href="https://agentmods.dev/skills/agentera/agently/interview-question-preparer"><img src="https://agentmods.dev/badge/skills/agentera/agently/interview-question-preparer/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/agentera/agently/interview-question-preparer"><img src="https://agentmods.dev/badge/skills/agentera/agently/interview-question-preparer.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00038 | $0.00684 |
| Opus 5.5 | $0.00015 | $0.00274 |
| Sonnet 5.5 | $0.00008 | $0.00137 |
| Haiku 4.5 | $0.00004 | $0.00068 |
Grade A, and why
interview-question-preparer 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 1mo 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.
Interview Question Preparer
Use this Skill when the task is to prepare a serious blog-style or media-style interview brief for a specified person, author, founder, maintainer, or project owner. This is for a published article or long-form conversation, not a hiring interview, recruiting screen, or candidate evaluation.
Workflow
- Clarify the interview target, audience, and intended article angle from the task input.
- Research public context before drafting. Search broadly first, then browse only the most relevant pages.
- Keep compact notes for source URLs, source titles, and why each source is relevant.
- Reflect on information sufficiency:
- what is well-supported by public evidence;
- what is weak, ambiguous, or missing;
- whether any supplied affiliation, organization, work, role, or alias is contradicted or not supported by the sources;
- whether another search or browse step is needed before finalizing.
- Draft grouped article interview questions that connect the person, project, product philosophy, technical tradeoffs, community adoption, business context, personal narrative, tensions, and future direction.
- Write the final Markdown deliverable to the requested workspace path.
- After writing or revising the requested file, read file back from the workspace when a workspace read capability is available, then include a concise validation checklist in the final response so the verifier can inspect the written content against the task criteria.
Output Requirements
The final Markdown file must include:
- title;
- target and audience;
- story/interview angle;
- source notes with URLs or source labels;
- sufficiency reflection;
- grouped blog/media interview questions;
- at least eight concrete questions;
- a short closing section for optional follow-up probes.
Boundaries
- Do not invent biographical facts when public evidence is weak.
- Mark weak assumptions explicitly.
- Source notes must be specific: include a URL, title, or clear source label, plus one sentence explaining why that source matters to the interview angle.
- If the user's wording implies an affiliation or relationship that sources do not support, mark it as uncertain or contradicted instead of repeating it as fact.
- When public evidence is sparse, state low confidence, list unknowns, and use clarifying or exploratory questions rather than invented biography.
- When names, aliases, or same-name people are ambiguous, preserve the user's original wording and explain the disambiguation choice.
- When multiple targets are supplied, include target-specific questions for each target and comparative article questions that connect them.
- Prefer questions that can elicit original insight from the interviewee, not generic product promotion.
- Do not frame the deliverable as a job interview, hiring guide, candidate assessment, or recruiting screen.
- If the task asks for a file, use the workspace file-writing capability and report the written path.
- If the task is correcting a previously written file, prefer reading the file before deciding whether to patch or fully rewrite it.
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.
- 1mo ago First seen · 72 lines · 38 tokens per session scan A 09e3956cdcef
interview-question-preparer is a skill published in the GitHub repository AgentEra/Agently (1,659 stars, last pushed 3d ago), licensed Apache-2.0. It adds 38 tokens to every session and 684 once invoked, about $0.0002 per session on Opus 5.5. 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 2026-08-30.
Other skills, from other repositories
python-run
Run and debug Python scripts in the project. Use when the user says "run python", "execute this script", "debug this py file", or wants to run/modify a .py file. Handles dependency checks, linting, execution, and error analysis.
python
Python package management.
jupyter-live-kernel
Iterative Python via live Jupyter kernel (hamelnb).
python-feature-lifecycle
Guidance for package and feature lifecycle in the Agent Framework Python codebase, including stage meanings, feature-stage decorators, feature enums, and how to move APIs from one stage to the next.
python-development
Coding standards, conventions, and patterns for developing Python code in the Agent Framework repository. Use this when writing or modifying Python source files in the python/ directory.
hive.chart-creation-foundations
Required reading whenever any chart tool is available. Teaches the one-tool embedding contract (call chartrender → live chart appears in chat AND a downloadable PNG lands in the queen session dir), the ECharts (data viz) vs Mermaid (structural diagrams) decision, the BI/financial-grade aesthetic baseline (no…