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/kirilxd/swe-interview-coach/coding-frameworksnpx skills add kirilxd/swe-interview-coach --skill coding-frameworksgit clone --depth 1 https://github.com/kirilxd/swe-interview-coachWhat 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 | $0.00103 | $0.06863 |
| Opus 5 | $0.00051 | $0.03431 |
| Sonnet 5 | $0.00021 | $0.01373 |
| Haiku 4.5 | $0.00010 | $0.00686 |
Grade A, and why
coding-frameworks 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 3d 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 — 426 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Coding Frameworks
UMPIRE method
The default scaffold for a 35-45 minute algorithmic question — the coding analog of RESHADED. Six phases, run top to bottom: Understand, Match, Plan, Implement, Review, Evaluate. The signal an interviewer scores is the process, not just a passing solution — narrate every phase transition out loud. The single most common failure is jumping straight to Implement; the first three phases are where senior signal lives.
Understand
- Restate the problem in your own words and get the interviewer to confirm before touching the keyboard.
- Ask the constraint questions that change the algorithm: input size (
nup to 10? 10^5? 10^9?), value ranges (negatives? overflow?), duplicates, empty/single-element input, sorted-ness, and the return shape (index vs value, in-place vs new, all answers vs one). Then work one tiny example by hand so you and the interviewer share the same notion of "correct".
Weak: "Two-sum, got it — I'll loop and check pairs." (Never asked sorted-ness, duplicates, or indices-vs-values.)
Strong: "Restating: return the indices of the two numbers summing to target. Exactly one solution? Negatives or duplicates? Is n up to 10^5? Can I reuse an element? Good — indices, one solution, unsorted, no reuse."
Match
- Name the pattern this problem resembles before designing anything: "pair summing to a target in a sorted array → two-pointers"; "longest window under a constraint → sliding-window". Recognition is mostly pattern-matching on input shape and asked-for output — say the candidate pattern aloud; being wrong out loud is cheap and invites a hint.
Weak: Silently starts writing nested loops with no name for what they're doing. Strong: "This smells like sliding-window: contiguous subarray, longest-under-a-constraint. If the window invariant doesn't hold I'll fall back to prefix-sum + hashmap."
Plan
- State the approach AND its time/space complexity BEFORE writing code. This is the phase candidates skip and lose the most points on.
- Start from the brute force, name its cost, then state the optimization and its cost — "brute force is O(n²); with a hashmap I get O(n) time, O(n) space". Sketch the data structures and the core loop in words; get a nod before implementing.
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.
- 3d ago First seen · 426 lines · 103 tokens per session scan A 1b144e3035f7
coding-frameworks is a skill published in the GitHub repository kirilxd/swe-interview-coach (79 stars, last pushed 1mo ago), licensed MIT. It adds 103 tokens to every session and 6,863 once invoked, about $0.0005 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
bq-skill
行为面试 skill。帮求职者挖掘真实经历、用 STAR/CAR 结构化、映射能力标签、构建可复用的中英双语故事库（Story Bank）；并能接入 JD + 简历，针对具体岗位生成 Top 20 BQ 选题 + 基于真实经历的 STAR 准备模板（HTML 报告）。不是背答案，而是建立可复用的职业叙事体系，让任何行为面试题都能自然作答。关键词：behavioral question, BQ, 行为面试, STAR, 故事库, Amazon LP, 职业故事, tell me about a time, 面试准备, JD 面试题预测, top 20 题。.
new-pattern
Guided workflow to create a new pattern following the project template and quality standards. Walks through topic validation, source verification, implementation, exercises, challenge questions, and bilingual docs.
sim
Run a timed coding assessment simulation in the terminal. Use for OA practice, GCA or ICA mocks, mock interviews, pairing rounds, take-homes, and pasted job postings.
concept-coach
Use when the learner wants to build understanding of a general programming, software-engineering, systems, or architecture concept — references, recursion, closures, coupling, dependency injection, caching, concurrency, queues, consistency, and similar — through Socratic coaching from their current mental model, with…
code-review-coach
Use when the learner wants to practise reviewing existing code, a diff, or a pull request through guided questioning — discovering and justifying findings one concern at a time instead of receiving a dumped list or a rewrite. The first non-DSA skill in this suite. Not for chasing an already-observed concrete failure…
dsa-tutor
Use when the user is working through a DSA or LeetCode-style problem and wants to build real problem-solving ability instead of receiving a finished solution. Guides through questions, withholds solutions until genuine struggle has happened, and logs mistakes with root causes instead of surface symptoms.