productivity/email/skills/inbox-setup/SKILL.md
One-time setup skill that builds a personalized inbox triage knowledge base via interactive interview. Interviews the user about their email patterns, business context, reply style, and priorities using grill-me discipline (one question at a time, forcing format where possible, dependency-ordered, each question explains why I'm asking), then generates the knowledge base files that power the companion 'inbox-triage' skill. Run this once before using inbox-triage for the first time. Re-run when business, pricing, or priorities change significantly. Triggers: 'set up my inbox', 'configure inbox triage', 'set up my email system', 'configure email triage', 'build my email knowledge base', 'initialize email management', 'set up inbox triage', 'onboard email triage', or any variation where someone wants to get the email triage system running for the first time.
npx skillsauth add alirezarezvani/claude-skills inbox-setupInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
3 of 9 scanners reported clean
Some scanners were skipped, did not run, or reported a non-clean status. Review each row below.
Paired with
inbox-triage. This skill writes the 7-file knowledge base at${WORKSPACE}/Email/thatinbox-triagereads on every run. The file contracts (names, sections, fields) MUST match between the two skills exactly. Seereferences/kb_file_contract.md.
Run once (or re-run when business/priorities change). Interview the user about their email patterns, business context, reply style, and priorities. Generate the structured knowledge base in ${WORKSPACE}/Email/ that captures everything inbox-triage needs to process the inbox effectively.
Do NOT generate all files at once. Walk through the 8 sections one at a time. Each section commits its file(s) before moving on. Partial completion (e.g., user drops off mid-interview) still produces a usable partial KB.
Grill-me discipline applies throughout:
See references/grill_me_section_walk.md for the 8-section discipline detail.
Exactly these files at ${WORKSPACE}/Email/:
| File | Purpose | Required? |
|---|---|---|
| email-taxonomy.md | Classification system + report preferences | Yes |
| email-patterns.md | Reply voice, tone, templates, hard rules | Yes |
| evaluation-framework.md | Decision tree for opportunity emails | Only if user receives pitches/opportunities |
| rate-card.md | Pricing, terms, negotiation posture | Only if user has pricing |
| blocklist.md | Auto-skip senders + learned decline patterns | Yes (seeded, grows over time) |
| tracker.md | Active follow-ups, overdue items, deadlines | Yes (starts mostly empty) |
| triage-log/ | Directory for per-run logs | Yes (created empty) |
The contract is identical to what inbox-triage expects — see references/kb_file_contract.md for the full spec.
~25–31 questions total across the 8 sections (depending on skip-logic). Hard ceiling: 35 questions including all sub-clarifications. Section 4 (Evaluation Framework) is skipped entirely when Section 1 surfaced no opportunity-email category, dropping the total by 6 questions and the rate-card file. After Section 8's confirmation + handoff message, intake is closed — never re-open it. To change preferences later, the user re-runs the skill (which detects existing files and asks per-file: replace / merge / skip). The grill-me one-at-a-time rule applies across section boundaries: do NOT batch questions even when moving from S{n} to S{n+1}.
Six grill-me questions, one at a time:
Action: Build mental model. Do NOT write files yet. Note whether opportunity emails are a category (drives S4 skip-logic).
Propose 5–7 categories based on Section 1 — pre-recommend a subset, not the whole template menu:
Then three forcing questions, one at a time:
Action: Generate email-taxonomy.md with categories, signals (for each: trigger phrases / sender patterns / subject markers), and default actions per category.
Six grill-me questions plus the critical sample request:
Paste 3–5 real sent emails from your inbox.
Why I'm asking: Self-description of voice is unreliable. Real samples are the best signal — I'll analyze them for voice patterns that supplement everything above. Use
scripts/voice_sample_analyzer.pyto extract patterns deterministically.
If user runs a business: also ask about media kits, rate sheets, standard pitches, repeated replies.
Action: Generate email-patterns.md with tone description (with do/don't examples), persona rules, templates, signatures, hard rules. See references/voice_calibration.md for the sample-extraction discipline.
Skip-logic: only run this section if Section 1 surfaced opportunity emails as a meaningful inbox category. Otherwise jump straight to Section 5.
Six grill-me questions, one at a time:
Action: Generate evaluation-framework.md (decision tree + recommendation categories + VIP list) AND rate-card.md if pricing exists.
Three grill-me questions, one at a time:
Action: Generate blocklist.md (auto-maintained by triage thereafter).
Three grill-me questions, one at a time:
Action: Generate tracker.md with active follow-ups table, overdue section, resolved section (empty), update log (empty). Also create empty triage-log/ directory.
Three grill-me questions, one at a time:
Action: Save these preferences into email-taxonomy.md under a "Report Preferences" section.
List every file created with one-sentence summary. Then:
Your triage system is ready. Run the inbox-triage skill to process your inbox. First runs need oversight — system learns from your edits and overrides.
Remind: re-run this setup anytime business/pricing/priorities change.
Run scripts/kb_validator.py --workspace ${WORKSPACE} to confirm the 7-file contract is satisfied before final handoff.
Never persist passwords, full account numbers, SSNs, or other sensitive credentials in knowledge base files. If the user volunteers such info during the interview, acknowledge it but don't store it; the relevant KB file gets [stored separately by user] in its place.
Re-running on an existing setup:
${WORKSPACE}/Email/| Situation | Behavior |
|---|---|
| Workspace inaccessible | Stop. Tell user where files would go and ask for permission/path |
| User refuses to share samples | Use self-description; flag in patterns file that calibration may need iteration |
| User says "skip this" mid-interview | Honor it; flag the gap in the file as [needs follow-up] |
| Sensitive info volunteered | Acknowledge but don't persist; note in file as [stored separately by user] |
| Re-run on existing setup | Detect existing files; ask user per-file: replace, merge, skip |
| User has no pricing / opportunities | Skip Section 4 entirely; don't create empty files |
| Script | Role |
|---|---|
| scripts/kb_validator.py | Validates the 7-file KB output (required files present, conditional files only if their sections ran, headers + structure correct). |
| scripts/section_progress_tracker.py | JSON-backed walk state at ~/.inbox_setup_sessions/<session>.json. Tracks active section, answered questions, committed files. |
| scripts/voice_sample_analyzer.py | Extracts voice patterns from pasted sent-email samples — opening phrases, sign-offs, length distribution, register markers. |
references/kb_file_contract.md — the canonical 7-file contract (write perspective; mirror lives in inbox-triage/references/)references/grill_me_section_walk.md — 8-section discipline, skip-logic, commit-per-sectionreferences/voice_calibration.md — sample-based voice extraction theory + anti-patternsrate-card.md or evaluation-framework.md when they don't applyVersion: 1.0.0
Source spec: megaprompts/06-inbox-setup-megaprompt.md
Build pattern: Path B (direct conversion). Paired with inbox-triage.
tools
Code review automation for TypeScript, JavaScript, Python, Go, Swift, Kotlin, C#, .NET, Java, C, C++, Rust, Ruby, PHP, and Dart/Flutter. Analyzes PRs for complexity and risk, checks code quality for SOLID violations and code smells, generates review reports. Use when reviewing pull requests, analyzing code quality, identifying issues, generating review checklists.
tools
Use when planning, funding, scoping, or synthesizing enterprise research across workstreams — clinical study design, R&D program finance, market sizing/surveys, or product/user research. Triggers on "design this clinical study", "what sample size", "R&D budget", "burn rate", "capitalize or expense", "TAM SAM SOM", "market sizing", "survey design", "segment the market", "plan user interviews", "usability test", "synthesize research insights". Forks context to route to one of four Research-Operations sub-skills (clinical-research, research-finance, market-research, product-research) and returns a digest. Distinct from ra-qm-team (regulatory submission), finance (corporate close/valuation), research/grants (funding discovery), product-team (persona/journey/live experiments), and marketing-skill (campaign analytics).
development
Use when managing the money for an internal R&D program or portfolio — building a multi-period program budget with the F&A (indirect) split, tracking burn rate and runway against value-inflection milestones, or routing R&D cost items to a capitalize-vs-expense determination. Every budget output surfaces its assumptions block; capitalize-vs-expense is decision-support only and routes to a named finance owner — it never books an entry or decides accounting treatment. Distinct from finance/financial-analysis (corporate DCF, close, valuation) and research/grants (funding discovery — this manages money already won).
development
Use when planning and synthesizing product/user research as a method-and-repository discipline — selecting the right method for the goal (generative interviews vs usability test vs concept test vs validation), computing method-based saturation/sample size with an explicit confidence level, or synthesizing coded observations into insights while flagging single-source anecdotes. Never fabricates user insight; an insight requires recurrence across independent participants. Distinct from product-team/ux-researcher-designer (persona/journey artifacts), product-discovery (discovery-sprint planning), and experiment-designer (live A/B) — this is the research-ops method + insight-repository layer.