practice-coding

A guided coding-practice session where you solve a problem in your editor while an interviewer gives hints and runs your code.

In plain words
What is it for?
Use it to prepare for coding interviews, learn from guided hints, and review your solution with test cases.
Why use it?
It lets you practise solving coding problems without time pressure or the stress of being graded.

Command

Install

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.

agentmods
npx agentmods add commands/kirilxd/swe-interview-coach/practice-coding
Clone the repo
git clone --depth 1 https://github.com/kirilxd/swe-interview-coach
Per session 42 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 1,565 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5 $0.00042 $0.01565
Opus 5 $0.00021 $0.00783
Sonnet 5 $0.00008 $0.00313
Haiku 4.5 $0.00004 $0.00156

Measured 2d ago against content hash 410d13f58c7b, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

practice-coding 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 2d 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.

commands/practice-coding.md · 119 lines

How it starts

The opening of the file, as written. The whole thing — 119 lines — stays where its author put it; the contents beside it link to each section on GitHub.

You are running /practice-coding.

Step 1 — Resolve the topic

Take $ARGUMENTS and resolve it to exactly one of topic_source / topic_prompt:

  • If ${CLAUDE_PLUGIN_ROOT}/library/coding/<arg>.md exists: Read it and hold its full content as topic_source (a library entry).
  • Else if $CLAUDE_PROJECT_DIR/coding/imported/<arg>.md exists (also strip a leading imported/ from the arg and retry, so both <id> and imported/<id> resolve): Read it and hold its full content as topic_source (an imported entry).
  • Else if $ARGUMENTS is non-empty (a quoted or spaced free prompt): treat it as a free-form problem statement — hold it verbatim as topic_prompt.
  • If empty: Read the frontmatter id and difficulty of each file in ${CLAUDE_PLUGIN_ROOT}/library/coding/*.md, present a numbered list grouped by difficulty, and wait for the user to pick → Read that entry as topic_source.

When topic_source is set, hold its ## Test cases JSON block (if present) and its frontmatter signature for later steps. A library/imported entry grounds the interview but is never read aloud verbatim.

Step 2 — Compute the session folder + seed the scratch file

Compute the session folder up front: $CLAUDE_PROJECT_DIR/coding/sessions/<YYYY-MM-DD-HHMM>-practice-coding/ where <YYYY-MM-DD-HHMM> comes from date +%Y-%m-%d-%H%M. Hold it as session_folder. Hold solution_file = <session_folder>/solution.py.

Write solution.py into the session folder, seeded as follows (Writing with the full absolute path creates intermediate dirs automatically — no mkdir needed):

  • topic_source with a ## Starter stub python block → write that block's body verbatim.
  • topic_source without a starter stub → synthesize from the frontmatter signature:
    from typing import List   # include only if a param/return type references List/Dict/Optional/etc.
    
    def <signature.name>(<param-name>: <param-type>, …) -> <signature.returns>:
        # your code here
        pass
    
  • topic_prompt (free-form, no signature) → a generic stub:
    def solve(*args):
        # your code here
        pass
    

Read the full file on GitHub · 119 lines

Changes

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.

  1. 2d ago First seen · 119 lines · 0 tokens per session scan A 410d13f58c7b

Subscribe to this mod's changes

practice-coding is a command published in the GitHub repository kirilxd/swe-interview-coach (79 stars, last pushed 1mo ago), licensed MIT. It adds 42 tokens to every session and 1,565 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.