Agent skill

Engineering Hiring Rubric

by mohitagw15856 in mohitagw15856/pm-claude-skills

Build an engineering hiring rubric and technical interview scorecard for evaluating software engineers at a specific level.

MITAuto-check passedEducation

Install Engineering Hiring Rubric

skills CLI
$ npx skills add mohitagw15856/pm-claude-skills --skill engineering-hiring-rubric -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install mohitagw15856/pm-claude-skills engineering-hiring-rubric --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/mohitagw15856/pm-claude-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/engineering-hiring-rubric .claude/skills/engineering-hiring-rubric && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
engineering-hiring-rubric
GitHub stars
1.4k
Token cost
~5.6k tokens
SKILL.md length
2,469 words
Files
1
Skills in repo
1,348
Repo updated
First seen
Licence
MIT

At a glance

Build an engineering hiring rubric and technical interview scorecard for evaluating software engineers at a specific level.

  • Works in 9 steps: Role Definition and Level Expectations → Interview Loop Structure → Coding Interview Guide → …
  • Asked to create an interview rubric
  • SKILL.md covers Required Inputs, Output Format, 1. Role Definition and Level… and 2. Interview Loop Structure, plus 10 more sections
  • Calls go

What it does

Engineering Hiring Rubric is an agent skill from mohitagw15856/pm-claude-skills. Build an engineering hiring rubric and technical interview scorecard for evaluating software engineers at a specific level. Use when asked to create an interview rubric, design a hiring process, build a technical scorecard, or standardize engineer evaluation. Produces a full interview scorecard, behavioral question bank, technical question set with evaluation criteria, system design rubric, and debrief agenda.

Its SKILL.md is about 5.6k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Education, covering Quizzes and assessments. The repository describes itself as: 1255 professional Agent Skills for Claude, ChatGPT, Gemini, Cursor & Codex — PRDs, postmortems, leases, medical bills, layoffs, go-bags, new countries. Plain markdown, MIT, in… The licence is MIT.

When your agent uses it

  • Asked to create an interview rubric
  • Design a hiring process
  • Build a technical scorecard
  • Standardize engineer evaluation

Example prompts

  • “/engineering-hiring-rubric”

Workflow steps

9 steps, taken from the step headings in SKILL.md.

  1. Role Definition and Level Expectations
  2. Interview Loop Structure
  3. Coding Interview Guide
  4. System Design Interview Guide
  5. Behavioral Interview Question Bank
  6. Full Interview Scorecard
  7. Hiring Recommendation Framework
  8. Debrief Agenda
  9. Calibration and Bias Reduction Notes

What it can do on your machine

Read from SKILL.md and the folder at commit 1cbf1f0. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Shell commands in SKILL.md call:

    • go

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Engineering Hiring Rubric loads about 5.6k tokens when it runs. Until then it costs about 110 tokens; SKILL.md has 2,469 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~110
When it runs · the whole SKILL.md, loaded when a task matches
~5.6k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from mohitagw15856/pm-claude-skills at commit 1cbf1f0, republished under its MIT licence (© mohitagw15856). 2,469 words, ~5,645 tokens.

Download SKILL.mdSave it as .claude/skills/engineering-hiring-rubric/SKILL.md (or your agent's skills folder).
name
engineering-hiring-rubric
description
Build an engineering hiring rubric and technical interview scorecard for evaluating software engineers at a specific level. Use when asked to create an interview rubric, design a hiring process, build a technical scorecard, or standardize engineer evaluation. Produces a full interview scorecard, behavioral question bank, technical question set with evaluation criteria, system design rubric, and debrief agenda.

Engineering Hiring Rubric

Produce a complete hiring rubric and interview scorecard for evaluating software engineers at a specific role and level. The rubric must be specific enough that two interviewers who have never compared notes will score the same candidate within one level of each other. That requires: explicit behavioral anchors (what does "Strong Hire" look like vs. "Hire" for each competency), calibrated technical questions with written evaluation criteria, and a structured debrief format that surfaces signal rather than recency bias. Include calibration notes to help interviewers recognize and counter common evaluation biases.

Required Inputs

Ask for these if not already provided:

  • Role — backend, frontend, fullstack, SRE/platform, data, ML, or mobile engineer
  • Level — junior (L3/IC2), mid (L4/IC3), senior (L5/IC4), or staff (L6/IC5); clarify the company's level naming if different
  • Team context — what the team builds, team size, and what problems this hire will work on in the first year
  • Tech stack — primary languages and frameworks for the technical questions; list the stack explicitly
  • Interview format — which rounds are used (phone screen, coding, system design, behavioral, take-home); if not specified, produce a recommended format

Output Format


Engineering Hiring Rubric: [Role] — [Level]

Role: [e.g., Senior Backend Engineer] Level equivalent: [e.g., L5 / IC4 / Senior] Team: [Team name and one-sentence description of what they build] Tech stack: [Languages and frameworks] Interview loop: [List the rounds in order]


1. Role Definition and Level Expectations

What This Role Does

[2–3 sentences describing the scope of work: what systems they'll own, what problems they'll solve, and who they'll work with. Make this specific to the team context provided.]

Level Bar

Define the minimum bar for a Hire recommendation at this level. This is not the ideal candidate description — it is the floor.

Dimension[Level] FloorOne Level Below (No Hire)One Level Above (Stretch)
Technical scope[e.g., "Owns a service or major feature area end-to-end with minimal guidance"][e.g., "Completes well-defined tasks; needs guidance on scope and approach"][e.g., "Leads cross-team technical initiatives; sets technical direction"]
Problem solving[e.g., "Breaks ambiguous problems into concrete sub-problems independently"][e.g., "Solves defined problems well; struggles with ambiguity"][e.g., "Identifies problems others miss; structures organization-level technical challenges"]
Code quality[e.g., "Writes production-ready code; anticipates edge cases; reviewable without significant rework"][e.g., "Writes working code that requires significant review feedback"][e.g., "Sets code quality standards; designs reusable abstractions adopted by others"]
Communication[e.g., "Communicates technical decisions clearly to peers and stakeholders"][e.g., "Communicates well with direct team; struggles with cross-team or stakeholder comms"][e.g., "Drives technical consensus across teams; writes documents others reference"]
Ownership[e.g., "Sees work to production; monitors after deploy; follows up on issues proactively"][e.g., "Delivers assigned work; escalates issues but doesn't drive them to resolution"][e.g., "Owns outcomes across teams; improves team processes and systems beyond their own work"]

2. Interview Loop Structure

RoundFormatDurationInterviewerCompetencies Assessed
Phone screenVideo call, technical questions45 min[Hiring manager or senior engineer]Problem solving, communication, basic technical depth
Coding interview 1Live coding — [platform]60 min[Engineer]Coding, data structures, code quality
Coding interview 2Live coding — [platform]60 min[Engineer]Algorithms, debugging, code quality
System designWhiteboard / shared doc60 min[Senior/Staff engineer]System design, scalability, technical communication
BehavioralStructured interview45 min[Hiring manager]Ownership, collaboration, growth mindset
[Optional] Take-homeAsynchronous project[X hours][Reviewer]Code quality, thoroughness, real-world problem solving

Interview coverage matrix: Each competency dimension must be assessed by at least 2 independent interviewers.

CompetencyPhone ScreenCoding 1Coding 2System DesignBehavioral
Coding○●●○
System design○●
Problem solving●●●●
Code quality●●
Communication●●●●●
Ownership○○●
Debugging●●

● = Primary signal ○ = Secondary signal


3. Coding Interview Guide

Question Selection

Choose 1–2 problems per coding round. Problems should be solvable in 30–40 minutes with the remaining time for discussion and follow-ups. Prefer problems with multiple solution tiers so you can see how far candidates take their thinking.

Problem Template

Problem: [Title]

Prompt (read to candidate):

[Problem statement — be specific. Include constraints (input size, value ranges). Avoid ambiguity that tests problem-reading rather than problem-solving.]

Example:

Given a list of integers representing stock prices at each minute of a trading day, return the maximum profit you could achieve by making exactly one buy and one sell. You may not sell before you buy.

Clarifying questions a strong candidate will ask:

  • [e.g., "Can the list be empty?" / "Are all values positive?" / "Can profit be negative — i.e., should we return 0 if no profit is possible?"]

Solution tiers:

TierApproachTime ComplexitySpace ComplexitySignals
Baseline[Brute force — O(n²) nested loop]O(n²)O(1)Can solve the problem; understands correctness
Expected[Single pass, tracking min price seen so far]O(n)O(1)Strong problem solver; explains tradeoff
Strong[Generalizes to k transactions, or extends to cooldown variant without prompting]O(n)O(1)Staff-level generalization thinking

Follow-up questions:

  • [e.g., "What if you could make at most k trades?"]
  • [e.g., "How would you test this function? Write me 3 test cases."]
  • [e.g., "Walk me through your code as if you're explaining it in a code review."]

Evaluation rubric for this problem:

SignalStrong HireHireNo Hire
Problem comprehensionAsks 1–2 clarifying questions immediately; identifies edge cases before codingUnderstands the problem after 1 prompt; misses 1–2 edge casesMisunderstands the problem or requires repeated clarification
Solution qualityO(n) solution; clean code; handles all edge casesO(n) with hints; code is readable but has minor issuesO(n²) with hints, or correct solution with significant issues
Code qualityWell-named variables; logical structure; would pass code reviewFunctional but verbose or inconsistently namedHard to follow; would require significant review feedback
CommunicationNarrates thinking throughout; explains complexity; self-correctsExplains solution when asked; answers follow-ups wellSilent during coding; unable to explain their approach
Follow-upsExtends solution confidently; identifies further improvementsHandles follow-ups with moderate promptingUnable to extend or explain tradeoffs

4. System Design Interview Guide

[Level]-Appropriate Design Scope

At [Level], expect the candidate to:

  • [e.g., Senior: "Design a complete system with capacity estimates, component breakdown, and discussion of failure modes"]
  • [e.g., Mid: "Design the core components of a system; may need prompting on scalability and failure handling"]
  • [e.g., Junior: "Design a simple client-server system; focus on clarity of thinking over complete distributed systems knowledge"]
Sample Design Question

Question: "Design [a URL shortener / a rate limiter / a notification service / a ride-matching system — choose one relevant to the team's domain]."

Evaluation dimensions:

DimensionWhat to assessStrong HireHireNo Hire
Requirements clarificationDoes the candidate ask before designing?Asks scope, scale, SLA, and key use cases before drawing anythingAsks some questions; may miss scale or SLAStarts designing immediately without clarifying
High-level designCan they describe the major components?Clear component breakdown with justified choices; covers data flowReasonable breakdown; may overcomplicate or undercomplicateMissing key components or cannot explain data flow
Data modelCan they design a schema or data structure for the system?Models the core entities with normalization/denormalization tradeoffs discussedReasonable schema; may miss indexing or partitioning needsCannot model the data or produces clearly wrong schema
ScalabilityCan they identify and address bottlenecks?Identifies bottlenecks proactively; proposes horizontal scaling, caching, or sharding as appropriateDiscusses scaling when prompted; reasonable solutionsCannot identify bottlenecks or proposes solutions that don't match the scale
Failure handlingDo they think about what happens when things break?Proactively discusses failure modes: single points of failure, retry logic, idempotencyDiscusses failure when prompted; identifies some failure modesDoes not think about failure; assumes happy path
CommunicationIs the design explained clearly?Could run this meeting with a team of engineers at a real companyClear enough to follow; some gaps in explanationDifficult to follow; interviewer cannot understand the design
Design Probing Questions

Use these to probe depth after the candidate presents their design:

  • "Walk me through what happens when a write request comes in at peak load — 10,000 requests per second."
  • "Your primary database just failed. What happens to the system?"
  • "You estimated X QPS. How would your design change if it needed to handle 100× that?"
  • "Where is the first place this system would fall over under load?"
  • "How would you monitor this in production? What would your on-call runbook look like?"

5. Behavioral Interview Question Bank

Map every question to a competency. Ask 4–6 questions per behavioral round using STAR format (Situation, Task, Action, Result). Do not ask leading questions.

Competency: Ownership and Delivery
  1. "Tell me about a time you owned something end-to-end — from design through production monitoring. What did you do when something went wrong after launch?"

    • Strong signal: Describes proactive monitoring setup, a specific incident they caught themselves, and what they changed
    • Weak signal: Describes writing the code and handing off; no discussion of production behavior
  2. "Describe a project that was significantly delayed or failed. What was your role, and what did you take responsibility for?"

    • Strong signal: Direct ownership of their contribution to the failure; specific changes to how they work
    • Weak signal: Attributes all delay to external factors; no reflection on their own actions
Show full SKILL.md (978 more words)Show less
Competency: Technical Judgment
  1. "Tell me about a significant technical decision you made. What options did you consider, and how did you decide?"

    • Strong signal: Named alternatives with clear tradeoffs; explains who they consulted; reflects on whether they'd decide the same way today
    • Weak signal: "I knew X was the right answer" without describing the decision process
  2. "Describe a time you had to push back on a technical direction — either from management or from peers. What happened?"

    • Strong signal: Evidence-based disagreement; constructive communication; willing to commit once decision was made even if they lost the argument
    • Weak signal: Either never pushed back or pushed back emotionally without evidence
Competency: Collaboration and Communication
  1. "Tell me about a time you had to explain a complex technical concept to a non-technical stakeholder. How did you approach it?"

    • Strong signal: Used analogy or simplified model; confirmed understanding; adapted to the audience
    • Weak signal: "I explained it technically and told them to trust me"
  2. "Describe a situation where you and a peer strongly disagreed on an approach. How did it resolve?"

    • Strong signal: Sought a third opinion or data; focused on the right outcome, not being right; maintained relationship
    • Weak signal: Escalated immediately or capitulated without engaging
Competency: Growth and Learning
  1. "What is a significant technical mistake you made in the last two years? What did you learn from it?"

    • Strong signal: Specific mistake, clear causal analysis, concrete behavioral change afterward
    • Weak signal: Cannot name a specific mistake; describes a minor issue to avoid vulnerability
  2. "How do you stay current in [relevant technical area]? Give me a specific example of something you learned recently and applied."

    • Strong signal: Named sources, applied learning in a specific project with a concrete outcome
    • Weak signal: "I read blogs" with no specifics; no applied example

6. Full Interview Scorecard

Complete one scorecard per interview round. Collect all scorecards before the debrief.

INTERVIEW SCORECARD
===================
Candidate:         ______________________
Interviewer:       ______________________
Round:             ______________________
Date:              ______________________
Interview format:  ______________________

COMPETENCY RATINGS
Rate each dimension independently. Do not average.
Scale: 1 = Strong No Hire | 2 = No Hire | 3 = Hire | 4 = Strong Hire

                          1    2    3    4    Notes
Coding / Technical skill  [ ]  [ ]  [ ]  [ ]  ___________________________
Problem solving           [ ]  [ ]  [ ]  [ ]  ___________________________
System design             [ ]  [ ]  [ ]  [ ]  ___________________________  
Code quality              [ ]  [ ]  [ ]  [ ]  ___________________________
Debugging                 [ ]  [ ]  [ ]  [ ]  ___________________________
Communication             [ ]  [ ]  [ ]  [ ]  ___________________________
Ownership                 [ ]  [ ]  [ ]  [ ]  ___________________________
Collaboration             [ ]  [ ]  [ ]  [ ]  ___________________________

SPECIFIC EVIDENCE
What did the candidate do or say that drove your rating?
(Required — write observable behaviors, not impressions)

Strongest signal (positive):
___________________________________________________________________________

Strongest concern or gap:
___________________________________________________________________________

OVERALL RECOMMENDATION
[ ] Strong Hire    [ ] Hire    [ ] No Hire    [ ] Strong No Hire

OVERALL RECOMMENDATION RATIONALE
(Required — 3–5 sentences minimum. State your recommendation, the evidence
that supports it, and the specific gap or risk if not a Strong Hire)
___________________________________________________________________________
___________________________________________________________________________
___________________________________________________________________________

Level signal: This candidate demonstrated [ L_ / L_ ] level behaviors.

SHOULD INTERVIEWERS DISCUSS BEFORE DEBRIEF? 
[ ] No — I have a clear independent signal
[ ] Yes — I need context on [specific area] to complete my assessment

7. Hiring Recommendation Framework

RecommendationMeaningWhen to use
Strong HireConfident the candidate will exceed the level bar and be a high performer on the teamEvidence across 3+ competencies at above-bar level; no significant concerns
HireConfident the candidate meets the level bar; will perform wellMeets bar on all must-have competencies; may have 1 area to develop
No HireDoes not meet the level barBelow bar on 1+ must-have competency, or gap too large to close quickly
Strong No HireClear mismatch — well below the bar, or a specific disqualifying signalSignificant gaps across multiple competencies, or a values/behavior concern

Must-hire competencies for [Role] at [Level]: [List 3–4 competencies where a No Hire score on any one of them means the overall recommendation must be No Hire, regardless of performance elsewhere. Example: "Coding and System Design are must-hire competencies for a Senior Backend Engineer. Strong performance on Behavioral dimensions cannot compensate for a No Hire on Coding."]

Debrief rule: A Strong Hire can override one No Hire only if: (a) the No Hire is not on a must-hire competency, and (b) the Strong Hire interviewer can articulate why the concern is not disqualifying. A Strong No Hire cannot be overridden — escalate to hiring manager.


8. Debrief Agenda

Run the debrief before scorecards are shared verbally. Everyone submits a written scorecard first.

DEBRIEF AGENDA — [Candidate Name]
Duration: 45 minutes
Facilitator: [Hiring Manager]

0:00 – 0:05  SCORECARD REVIEW
  Each interviewer states their overall recommendation only (no rationale yet).
  Facilitator notes alignment and disagreements on whiteboard/doc.

0:05 – 0:15  EVIDENCE ROUND
  Go around the table. Each interviewer shares:
    - Their strongest positive signal (observable behavior, not impression)
    - Their biggest concern (observable behavior, not impression)
  No discussion yet — just evidence gathering.

0:15 – 0:30  DISCUSS DISAGREEMENTS
  Address only the competency dimensions where interviewers disagree.
  Anchor discussion on: "What did you observe?" not "What do you think?"
  If interviewers assessed different competencies, disagreement may reflect
  insufficient signal — note this.

0:30 – 0:40  DECISION
  Reach a decision on overall recommendation.
  If consensus: state the recommendation and rationale.
  If not consensus: hiring manager makes the call and states why.

0:40 – 0:45  PROCESS NOTES
  - Were any questions unclear or hard to compare across candidates?
  - Any bias signals observed during the debrief? (see Section 9)
  - Feedback to improve the process for next time.

9. Calibration and Bias Reduction Notes

Brief every interviewer on these before they conduct their first interview for this role.

BiasHow it manifestsCounter-measure
Halo effectStrong performance in round 1 colors ratings in round 2Submit scorecard before reading others; rate each competency independently
Similarity bias"I liked them" correlates with "they think like me"Require observable evidence for every rating; check: "Is this a signal about their ability or their similarity to me?"
Recency biasFinal impression dominates overall ratingTake notes during the interview; write evidence immediately after; debrief uses written evidence, not memory
Expectation anchoringFirst interviewer's opinion anchors all othersNo verbal discussion between interviewers before debrief; written scorecards submitted before debrief starts
Culture fit as cover"Not a culture fit" without specific behavioral evidence"Culture fit" is not a valid dimension on this scorecard; use Collaboration and Communication with evidence
Credential biasDegree or previous employer overweights ratingDo not list educational background in pre-interview briefing documents; focus on demonstrated behaviors
Confidence ≠ CompetenceArticulate candidates rated higher regardless of correctnessGrade the answer quality, not the delivery style; use written rubrics per question

Quality Checks

  • Level bar table defines a concrete floor for the level — not aspirational traits — with a comparison to one level below and above
  • Every behavioral question includes explicit Strong Hire and Weak/No Hire signal descriptions — not just the question text
  • Coding problem(s) include solution tiers with time and space complexity, plus a per-question rubric with behavioral anchors
  • System design rubric evaluates at minimum: requirements clarification, component design, data model, scalability, and failure handling
  • Scorecard uses observable behavior fields ("What did the candidate do or say") — not impression fields
  • Must-hire competencies are explicitly named for the role and level
  • Debrief agenda enforces written scorecard submission before verbal discussion to prevent anchoring

Anti-Patterns

  • Do not use a single behavioral anchor description per competency — you must define what Strong Hire AND No Hire look like separately, or interviewers cannot calibrate
  • Do not allow "culture fit" as a standalone assessment dimension — it masks similarity bias; all judgments must use observable behavioral evidence
  • Do not let interviewers share scorecard feedback before the debrief — verbal pre-debrief discussion anchors everyone to the first opinion expressed
  • Do not set the same must-hire competency list for all engineering roles — a senior backend engineer and a frontend engineer have different non-negotiable competencies
  • Do not skip the calibration bias notes section — interviewers who have never been briefed on halo effect, recency bias, and credential bias will reproduce them in every loop

Example Trigger Phrases

  • "Create an interview rubric."
  • "Design a hiring process."
  • "Build a technical scorecard."

© mohitagw15856, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in skills/engineering-hiring-rubric of mohitagw15856/pm-claude-skills.

Open the folder on GitHubat commit 1cbf1f0

Compare with similar skills

Engineering Hiring Rubric next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Engineering Hiring Rubric compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Engineering Hiring Rubric this skillmohitagw15856/pm-claude-skills1.4k—~5.6kAutomated safety check: PassMIT
DeepTutor CLIHKUDS/DeepTutor41k—~2.8kAutomated safety check: PassApache-2.0
AI Engineering Placement Quizrohitg00/ai-engineering-from-scratch67k—~2kAutomated safety check: PassMIT
Codebase to Coursezarazhangrui/codebase-to-course5.7k—~4.4kAutomated safety check: PassNone
AI Engineering Phase Quizrohitg00/ai-engineering-from-scratch67k—~2.1kAutomated safety check: PassMIT
Scholar EvaluationK-Dense-AI/claude-scientific-writer2.4k2 repos~2.9kAutomated safety check: NotesMIT

Similar skills

  • DeepTutor CLI

    HKUDS/DeepTutor

    Teaches the agent to set up and run DeepTutor from the command line: chat and capabilities, knowledge bases, partners, memory, sessions, notebooks and the server or Web app.

    41k GitHub stars~2.8k tokensUpdated 3 days ago
    EducationAuto-check passed
  • AI Engineering Placement Quiz

    rohitg00/ai-engineering-from-scratch

    Runs a 10-question quiz across five areas to place a learner in the AI Engineering from Scratch curriculum, so they skip what they already know.

    67k GitHub stars~2k tokensUpdated today
    EducationAuto-check passed
  • Codebase to Course

    zarazhangrui/codebase-to-course

    Turns a codebase into an interactive single-page HTML course for non-technical learners, with scroll modules, animated diagrams, quizzes and plain-English code translations.

    5.7k GitHub stars~4.4k tokensUpdated 6 mo ago
    EducationAuto-check passed
  • AI Engineering Phase Quiz

    rohitg00/ai-engineering-from-scratch

    Quizzes you on a completed phase of the AI Engineering from Scratch course, taking a phase number or name and mapping it to that phase's directory.

    67k GitHub stars~2.1k tokensUpdated today
    EducationAuto-check passed
  • Scholar Evaluation

    K-Dense-AI/claude-scientific-writer

    Provide qualitative-first, evidence-traceable developmental review of scholarly works and audit low-stakes research-assessment rubrics with optional local quality controls.

    2.4k GitHub starsUsed in 2 repos~2.9k tokens
    EducationAuto-check: notes
  • Evaluation

    guanyang/open-agent-hub

    This skill should be used when building agent evaluation systems: deterministic checks, regression suites, multi-dimensional rubrics, quality gates, production monitoring, baseline comparison, and…

    977 GitHub starsUsed in 2 repos~4.2k tokens
    EducationAuto-check passed

More from mohitagw15856/pm-claude-skills

All 1,348 skills in this repo
  • Car Tco

    mohitagw15856/pm-claude-skills

    Compare the total cost of car ownership across buy-new, buy-used, lease, and keep-your-current-car — depreciation, insurance, maintenance ramp, and fuel over a real horizon, not just the monthly…

    1.4k GitHub stars~1.1k tokensUpdated 2 days ago
    Auto-check passed
  • Cs Health Scorecard

    mohitagw15856/pm-claude-skills

    Build a customer health scorecard for a specific account. An agent skill from mohitagw15856/pm-claude-skills.

    1.4k GitHub stars~2.4k tokensUpdated 2 days ago
    Auto-check passed
  • Exit Waterfall

    mohitagw15856/pm-claude-skills

    Compute who gets what at each exit price from a cap table — liquidation preferences, conversion points, and where the founders' share collapses.

    1.4k GitHub stars~1.1k tokensUpdated 2 days ago
    Auto-check passed
  • Feature Prioritisation

    mohitagw15856/pm-claude-skills

    Apply prioritisation frameworks (RICE, MoSCoW, Kano, ICE, Opportunity Scoring) to rank features and backlog items.

    1.4k GitHub stars~2k tokensUpdated 2 days ago
    Auto-check passed
  • Fire Number

    mohitagw15856/pm-claude-skills

    Compute a financial-independence (FIRE) target and years-to-reach with every assumption labeled as an assumption — plus a sensitivity table instead of a single false-precision answer.

    1.4k GitHub stars~1.1k tokensUpdated 2 days ago
    Auto-check passed
  • Freelance Rate

    mohitagw15856/pm-claude-skills

    Derive a freelance day/hourly rate backwards from target income, honest billable utilization, overhead, and the self-employment tax premium — the arithmetic that proves a rate is not salary÷2000.

    1.4k GitHub stars~1.2k tokensUpdated 2 days ago
    Auto-check passed

Categories

Questions about Engineering Hiring Rubric

What does Engineering Hiring Rubric do?

Build an engineering hiring rubric and technical interview scorecard for evaluating software engineers at a specific level. Engineering Hiring Rubric is an agent skill from mohitagw15856/pm-claude-skills. Build an engineering hiring rubric and technical interview scorecard for evaluating software engineers at a specific level.

When should I use Engineering Hiring Rubric?

Engineering Hiring Rubric fits situations like: asked to create an interview rubric; design a hiring process; build a technical scorecard; standardize engineer evaluation.

How do I install Engineering Hiring Rubric in Claude Code?

Run `npx skills add mohitagw15856/pm-claude-skills --skill engineering-hiring-rubric -a claude-code`. Or copy the skill folder (skills/engineering-hiring-rubric in mohitagw15856/pm-claude-skills) into .claude/skills/engineering-hiring-rubric in your project. Claude Code loads it when a task matches its description.

How do I install Engineering Hiring Rubric in Codex?

Run `npx skills add mohitagw15856/pm-claude-skills --skill engineering-hiring-rubric -a codex`. Or copy the skill folder (skills/engineering-hiring-rubric in mohitagw15856/pm-claude-skills) into .agents/skills/engineering-hiring-rubric in your project. Codex loads it when a task matches its description.

Can I use Engineering Hiring Rubric in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add mohitagw15856/pm-claude-skills --skill engineering-hiring-rubric -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/engineering-hiring-rubric, .gemini/skills/engineering-hiring-rubric, .github/skills/engineering-hiring-rubric and .opencode/skills/engineering-hiring-rubric in your project.

What does Engineering Hiring Rubric need to run?

Going by SKILL.md and its folder, Engineering Hiring Rubric needs the command-line tools its instructions call (go).

Does Engineering Hiring Rubric access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Engineering Hiring Rubric safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Engineering Hiring Rubric use?

Engineering Hiring Rubric is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Engineering Hiring Rubric use?

About 5.6k tokens (SKILL.md is roughly 23k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Engineering Hiring Rubric?

Skills that share tags, products or a category with Engineering Hiring Rubric: DeepTutor CLI (HKUDS/DeepTutor, 41k stars), AI Engineering Placement Quiz (rohitg00/ai-engineering-from-scratch, 67k stars), Codebase to Course (zarazhangrui/codebase-to-course, 5.7k stars) and AI Engineering Phase Quiz (rohitg00/ai-engineering-from-scratch, 67k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Engineering Hiring Rubric?

mohitagw15856 (a GitHub user) maintains it in mohitagw15856/pm-claude-skills, which has 1,434 GitHub stars. The repository holds 1,348 skills in this directory. The repository was last updated on October 9, 2026.

Source: mohitagw15856/pm-claude-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.