Research, qualify, draft, and track email outreach for WordPilot.pro with a non-salesy boutique growth strategist approach. Includes 5-phase workflow (research, qualify, outreach, track, nurture), ICP scoring rubric, pipeline CRM, daily action log, research playbook, and personalization-first email templates.
Research-first, non-salesy lead generation for WordPilot.pro. Daily-cadence system with 6-stage conversion pipeline, persona-specific research methods, and professional outreach templates. Finds qualified leads through deep research, tracks every interaction, and converts through genuine connection — not mass blasting. Workspace-first tracking in /leads/.
Lints a DNS zone file or a provider's CSV record export before it breaks web or mail - CNAME at the apex or next to other records, MX/NS pointing at a CNAME or IP, missing trailing dots, missing or duplicate SPF, +all/?all and the 10-lookup limit, DMARC and DKIM presence, syntax and key size, TTL extremes, private IPs and SOA mistakes. Returns corrected records. Includes an offline stdlib Python linter with --self-test.
Reviews GitHub Actions workflow YAML for supply-chain and injection risks: pull_request_target with PR checkout, untrusted github.event text in run steps, actions pinned to tags or branches instead of commit SHAs, missing or write-all permissions, secrets in shell commands, curl | bash, self-hosted runners on PRs, and missing timeouts. Use before merging CI changes or auditing a repo. Includes a stdlib Python linter with --self-test.
Finds what you cannot see in pasted code, configs, prompts and data files - Trojan Source bidi controls, hidden Unicode tag text (prompt injection), zero-width and invisible characters, odd spaces, control characters, Cyrillic/Greek homoglyphs, mojibake, invalid UTF-8, BOMs, mixed line endings and non-NFC text. Decodes hidden text, ranks findings and writes a cleaned copy. Includes an offline stdlib Python checker with --self-test.
Answers enterprise security questionnaires for small teams: drafts each answer from your real policies and past replies, marks what needs proof or a fix, and keeps a reusable answer library.
Reviews suspicious inbox messages with a color-coded 0–100 scam score based on urgency tricks, lookalike domains, payment asks and known alert patterns—read-only unless you approve an action.
Screens email for hidden instructions aimed at AI assistants—white text, zero-width characters, and "ignore previous instructions" lines—and warns you before those messages are fed to any bot.
Proposes concrete actions from guarded facts and waits for explicit human approval before any side effect.
