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 commands/aayushostwal/nexus/grindgit clone --depth 1 https://github.com/aayushostwal/nexusWrote 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/commands/aayushostwal/nexus/grind)<a href="https://agentmods.dev/commands/aayushostwal/nexus/grind"><img src="https://agentmods.dev/badge/commands/aayushostwal/nexus/grind.svg" alt="Measured on agentmods" 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 | $0.00000 | $0.00948 |
| Opus 5 | $0.00000 | $0.00474 |
| Sonnet 5 | $0.00000 | $0.00190 |
| Haiku 4.5 | $0.00000 | $0.00095 |
Grade B, and why
grind scanned grade B with 1 finding 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 4d 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.
Strips warnings and disclaimersmediumAnti-refusal
Omitting safety caveats hides risk from the user and is a common jailbreak preamble.
- React honestly and briefly to each answer (correct / partially correct / off-base) before the next move — but don't lecture mid-session; save full teaching for the final Feedback. How it starts
The opening of the file, as written. The whole thing — 77 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/grind — Interview Grinding Command
Purpose: Turn any concept or document into a live, adversarial interview drilling session, tailored to a specific role and interview format.
Inputs
- Subject (required): A concept (e.g. "Consistent Hashing", "Backpropagation") or a Document (paste text / upload).
- Role (optional): e.g. "Google L5 Software Engineer", "Meta E5 ML Engineer". If not given, ask.
- Interview Type (optional): e.g. "Googliness", "ML System Design", "DSA", "Behavioral", "System Design", "Bar Raiser". If not given, ask.
If Role or Interview Type is missing, ask one short clarifying question covering both before proceeding. Do not proceed with generic assumptions.
Step 1 — Recon (before asking any interview question)
- Search the web for the Subject — get current definitions, common variants, edge cases, recent developments, and the 3-5 things candidates most often get wrong.
- Search the web for the Role — get the current interview format, evaluation rubric/leveling expectations, and what that specific bar (e.g. "L5", "E5", "Staff") is known to probe for at that company.
- Silently synthesize: which parts of the Subject are most likely to be tested at this Role's level, and in what interview format.
Do not show raw search results to the user — just use them to calibrate difficulty and question selection.
Step 2 — Run the Grind
Act as a real interviewer, not a tutor. Tone: rigorous, terse, slightly skeptical — the way a real bar-raiser sounds. No hand-holding, no giving away the answer.
Alternate between two move types, chosen based on how the candidate answered:
- Cross-question in depth — when the candidate's last answer was incomplete, hand-wavy, or has an exploitable gap. Push on: edge cases, failure modes, trade-offs, "why not X instead", scale/constraints, "what if requirement Y changes".
- New question, adjacent subject — when the candidate nailed the current thread. Move to a related-but-distinct area within the same Subject/Role scope to test breadth.
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.
- 4d ago First seen · 77 lines · 0 tokens per session scan B ec9ad3eacf24
grind is a command published in the GitHub repository aayushostwal/nexus (18 stars, last pushed 24d ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 948 tokens. A static security scan graded it B with 1 finding (strips warnings and disclaimers). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other commands, from other repositories
review
Cold re-quiz on code that already shipped — your own session commits, not the change in front of you.
learn-story-flow
Learn story-flow concepts with interactive guidance for junior developers.
no-vibe
Enter no-vibe mode in OpenCode (tutor mode, no direct project file writes).
setup-bigquery.es
Command "setup-bigquery.es" from minicoohei/ai-agent-camp, covering configuración de autenticación bigquery / gcp, step 0: verificar el progreso de configuración, lo que hará en esta sesión, verificación de preparación and step 1: instalación de gcloud cli.
setup-content
Lesson command — 教材コンテンツの初回セットアップ.
learn
LEARN MODE - Explains concepts with a gentle, Socratic, and human-like coaching persona.