Initializing System

Career Intelligence for
Better Career Decisions

Structured alignment reports on the front end.
Disciplined career infrastructure in the dashboard.

Evidence-backed claims. Transparent gaps. No inflated experience.

WHY MEC.BOT

CAREER INTELLIGENCE,
NOT CAREER THEATER.

Hiring is full of guesswork.

Resumes oversignal experience. Candidates undersignal real work. Recruiters skim documents trying to infer capability from formatting, keywords, and narrative claims.

Applicants tailor documents endlessly without knowing whether a role actually aligns with their experience or goals.

Most tools optimize presentation.

mec.bot optimizes alignment.

Structured career intelligence evaluates fit, surfaces evidence, and reveals gaps before either side commits significant time.

Replace resume guesswork with evidence-backed alignment.

WHAT MEC.BOT IS

CAREER INTELLIGENCE FOR
BETTER CAREER DECISIONS.

mec.bot is a Truth-Bound Career Intelligence system built into the mec.dev portfolio.

Recruiters can interact with the candidate in two ways.

Ask questions through a conversational assistant trained on a structured knowledge base of the candidate's work, experience, and technical capabilities.

Or paste a job description to generate a Fit Check — a structured, shareable role alignment report.

Both are powered by the Career Canon, a system that stores verified statements about projects, responsibilities, and capabilities.

Behind the scenes, a private dashboard maintains this knowledge base, verifies evidence, tracks opportunities, and manages application workflows.

The goal is simple:

bring discipline, transparency, and evidence to career positioning.

Frontend Feature

Ask mec.bot

Career intelligence, not career theater.

Recruiters often have questions before committing to a conversation. Instead of inferring capability from resumes and narrative claims, they can interact directly with mec.bot.

A conversational interface lets recruiters ask about the candidate's work, experience, technical capabilities, approach to problem-solving, and career goals. Or paste a job description, project brief, or RFP to generate a structured Fit Check—a shareable role alignment report.

Both are powered by the Career Canon: verified statements about experience, projects, and capabilities. Vector search retrieves relevant context; answers are bounded by that knowledge base and designed to avoid inventing claims. Unknown claims are returned as UNKNOWN.

Replace resume guesswork with evidence-backed alignment.

experiencetechnical capabilitiesprojects and systems builtarchitectural approachescollaboration stylecareer goals

How it works

Ask questions

Work, experience, problem-solving, goals

Paste JD or RFP

Job description, project brief, Fit Check

mec.bot

Career Canon · vector search

Evidence-grounded answers

Unknown → UNKNOWN

Example recruiter questions

What experience do you have with retrieval-augmented generation?

Have you built production systems with Next.js and TypeScript?

What kind of problems do you prefer working on?

Are you available for contract work?

Response mode

Evidence-grounded answers only. Unknown claims are returned as UNKNOWN.

Public Feature

Fit Intelligence

Structured role alignment reports.

➜~01 / Role Input

Input

Paste a job description, upload a document, or provide a role URL. mec.bot ingests the source and prepares it for structured analysis.

➜~02 / Parse & Evaluate

Process

The system extracts responsibilities, required skills, preferred skills, level cues, and domain signals, then maps them to the Career Canon.

➜~03 / Fit Check

Output

You get a shareable alignment report with an alignment score, verdict, matches, gaps, and clear mismatches. The goal is visible alignment, not inflated claims.

Output Structure

Inside a Fit Check

Score

Alignment Score

A supplemental score derived from overlap between role requirements and verified experience signals in the Career Canon.

~ fit_score --role "Senior Engineer"
[OK] 78% overlap | 12/15 requirements matched

Verdict

Honest Assessment

Includes verdict tiers plus explicit requirement mapping: Where I Match, Gaps to Note, and Where I Don't Fit.

Strong FitModerate FitHonest Assessment

~ verdict
[STRONG] React, TypeScript, System Design
[GAP] GraphQL (1 project)

Action

Recommendation + Link

Each report includes a concise recruiter-facing recommendation and a tokenized link so it can be shared or revisited later.

~ share report
[OK] https://mec.bot/r/abc123xyz
[OK] Link copied to clipboard

Evidence System

The Truth Layer

Truth-bound career canon.

Every claim made by mec.bot is derived from the Career Canon. Information enters through a structured pipeline: Artifact to Signal to Draft to Approved Constant.

➜~01 / Artifact

Evidence enters the canon.

Artifacts may include project documentation, transcripts, portfolio notes, or source repositories. GitHub repositories are treated as first-class evidence sources.

Project DocsTranscriptsPortfolio NotesGitHub Repos

➜~02 / Signal

Structured extraction.

Signals are extracted from artifacts and transformed into candidate claims with source references and confidence markers.

~ canon pipeline --stage signal
[OK] Repository citation attached.
[!] Unknown signal marked UNKNOWN.

➜~03 / Draft

Review before promotion.

Extracted signals are reviewed, refined, and checked for scope accuracy before they can become public-facing constants.

➜~04 / Approved Constant

Public claim surface.

Only approved constants are used in recruiter chat responses and fit reports. Unknown information is labeled as UNKNOWN rather than inferred.

Truth-Bound OutputsNo Inflated Claims

GitHub Verification

Evidence from real work.

mec.bot can connect to a user's GitHub account to analyze selected repositories and extract technical signals from real code. These signals are transformed into structured facts that feed the Career Canon.

Repository Signals

Inspects repository structure, language usage, frameworks, dependency graphs, and architecture patterns from selected repositories.

Development Evidence

Captures commit activity, implementation trajectories, documentation depth, and test signal density from real project artifacts.

Signal Pipeline

Repository artifact -> detected technologies -> implementation patterns -> inferred competencies -> structured facts with confidence levels.

User-Controlled Scope

Scanning is optional and limited to repositories explicitly selected by the user. Every extracted signal references supporting files.

Resume & CV Canon

Locked templates
for consistent generation.

Resumes and CVs are stored as canonical templates. Generation focuses on summary updates, role-aligned bullet points, and keyword coverage while preserving format consistency and preventing structural drift.

Locked

Preserves structure, layout, and tone for canonical sections that should never drift.

Editable

Allows controlled updates to summary lines, role-aligned bullet points, and opportunity-specific keyword coverage.

Optional

Supports conditional sections without breaking document consistency across generated versions.

Opportunity Intelligence

Structured opportunity
tracking.

The dashboard tracks opportunities across jobs, grants, fellowships, and accelerators. Each opportunity is captured as a structured record so multiple pipelines can be managed in one disciplined workflow.

JobsGrantsFellowshipsAccelerators

Opportunity Record Fields

organization

role description

requirements

deadlines

compensation information when available

application stage

submission history

Research & Verification

Understand opportunities
before applying.

Each opportunity passes through a Research & Verify step that retrieves posting content, extracts structured facts, and distinguishes verified information from unverified data before application work begins.

Structured Facts

titlelevelrequired skillsdeadlinescompensation when present

Verification Metadata

source citation

confidence level

unknown flags

This reduces the risk of misinterpreting role requirements and keeps decision-making grounded in verified data.

Application Workflow

Application
Management Pipeline

Research to fit review to materials to drafting to submission to outcome. Structured pipeline, not ad-hoc document workflow.

Research + Fit Review

Stage 01

Applications begin with structured research, extracted requirements, and a fit review grounded in verified canon evidence.

[RESEARCH] -> [FIT REVIEW]

[MATERIALS] -> [DRAFTING]

Materials + Drafting

Stage 02

Track requirement checklists, document drafts, and asset reuse while preparing role-specific submissions.

Submission + Outcome

Stage 03

Record submissions, maintain logs, and track outcomes so every opportunity stays measurable and reviewable.

SUBMISSION -> OUTCOME

Reminder System

Deadline awareness
and escalation.

Continuous Monitor

The system continuously checks due and overdue reminders and dispatches notifications at regular intervals.

Escalation Nudges

Escalation increases as deadlines approach and opportunities remain early in pipeline stages.

NewResearchingApplying

Outcome

Important opportunities stay visible and deadlines are less likely to slip through unnoticed.

Relationship Tracking

Lightweight CRM
for opportunity pipelines.

Relationship Type

Track recruiters, founders, investors, collaborators, and other professional contacts in one structured system.

Interaction History

Each contact includes interaction notes, last contact date, and a next-action reminder to keep outreach disciplined.

Hit List View

A priority list surfaces high-value contacts with fast links to LinkedIn, email, and preferred communication channels.

Opportunity Links

Contacts can be linked to specific opportunities, keeping pipeline context connected to real relationship activity.

Analytics

Measure alignment
and outcomes.

mec.bot tracks data across fit reports, canon usage, engagement, and outcomes so career positioning can be refined with measurable feedback loops.

Fit Checks

Avg Alignment

Canon Citations

Applications

number of fit checks generated

average alignment scores

recurring competency gaps

most cited canon entries

report engagement metrics

application outcomes

Architecture

A full-stack product system.

mec.bot is implemented as an actively developed full-stack application with modern web infrastructure, analytics instrumentation, tokenized report routing, and a multi-section administrative dashboard.

➜~Framework

Next.js + React

➜~Language

TypeScript

➜~Data Layer

Prisma + PG

➜~Retrieval

Vector Search

Product Surface

The system includes dozens of API routes supporting report generation, opportunity tracking, document management, analytics, and career canon operations. mec.bot is not a static portfolio feature.

Public Surface

Recruiter ChatFit ChecksShareable ReportsTokenized Routing

Intelligence Layer

Canon PipelineFit Scoring EngineReport GeneratorVerification + Research

Infrastructure

Opportunity TrackingApplication WorkflowsReminder SystemRelationship CRMResume Template Management

What Makes mec.bot Different

Career intelligence,
not career theater.

Traditional portfolios rely on narrative and presentation. mec.bot treats career positioning as infrastructure.

Traditional Portfolio

mec.bot

Static resume

Structured fit intelligence

Self-reported skills

Artifact-backed verification

Manual storytelling

Canon-based evidence system

Ad-hoc applications

Structured opportunity workflows

No feedback loop

Gap → curriculum → canon

A Personal Experiment

From portfolio feature
to product.

mec.bot began as a personal system designed to bring structure and evidence to career positioning.

It is currently being tested within a live portfolio environment where real recruiter interactions and opportunities feed data back into the system.

The long-term goal is to expand mec.bot into a standalone product for ambitious builders and enterprise implementations for talent intelligence and recruiting infrastructure. The current version represents the first operational prototype of that vision.

mec.bot v1.0

Make alignment transparent.

Run a structured, evidence-backed fit check.

Run a Fit Check View Example Report