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/agentera/agently/interview-question-preparernpx skills add AgentEra/Agently --skill interview-question-preparergit clone --depth 1 https://github.com/AgentEra/AgentlyWhat 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 | $0.00038 | $0.00684 |
| Opus 5 | $0.00019 | $0.00342 |
| Sonnet 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 yesterday.
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.
- yesterday First seen · 72 lines · 38 tokens per session scan A 09e3956cdcef
interview-question-preparer is a skill published in the GitHub repository AgentEra/Agently (1,644 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. 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
implementation-final-review
Perform the repository's risk-tiered independent final review before implementation completion. Use only when explicitly invoked or when repository instructions require it after behavior-impacting implementation work; audit the complete task diff, supported contracts, lifecycle and security boundaries, complexity, and…
complete-partial-pr
Evaluate and complete an issue or PR where the submitted patch fixes only a narrow symptom of the reported pain point. Use when a contribution may miss adjacent integration surfaces, provider/spec semantics, roundtrip behavior, tests, docs, or historical maintainer decisions.
testing-skill
Record, rewrite, and debug VCR cassettes for HTTP recordings. Use when running tests with --record-mode, verifying cassette playback, or inspecting request/response bodies in YAML cassettes.
gh-pr-description
Drafts and reviews GitHub pull request descriptions for the eve repository. Use when opening, updating, or reviewing a PR, or when summarizing a branch for reviewers.
adding-a-provider-api-feature
Add a new provider API capability (prompt caching, strict/structured tool calling, thinking/reasoning effort, service tier, safety settings, logprobs, etc.) to Pydantic AI. Use when wiring a provider feature through the library — it enforces reasoning from the existing cross-provider abstraction before designing…
hive.slack-notifications-setup
Set up a Slack notification channel (Sentinel) for a colony by driving the browser — reuse or create the "Hive Sentinel" Slack app from a JSON manifest, install it, capture the bot + app tokens, create/select the channel via the Slack API, and turn Sentinel on so the colony can ping the user on Slack and accept…