capture-spam-sample

capture-spam-sample

Main entry point. Turns one annoying email into a structured corpus entry plus durable defenses.

Inputs (resolve interactively if missing)

• Email reference — one of:
  • a Gmail message id (preferred, when an email MCP like gws-personal / gws-business is available)
  • an absolute path to a .eml file the user already saved
  • the raw text the user pasted into the conversation
• User rationale — one to two sentences from the user: what kind of spam is this, why it bothered them, what pattern it represents. This is load-bearing — it becomes the why_added field and seeds the filter pattern.

If the user invoked the skill without a rationale, ask once: "What kind of spam does this represent? (one sentence is fine — e.g. 'AI-faked personalised outreach asking for a backlink', 'unsolicited drip-cadence sales pitch', 'fake-friendly intro request from a scraped list')."

Resolve corpus path

Read ${CLAUDE_USER_DATA:-$HOME/.local/share/claude}/spamhole/config.json for corpus_path. If missing, run setup-corpus first.

Steps

1. Materialise the raw email: if the input is a Gmail id, fetch via the available email MCP (mcp__*__read_email with format: raw or equivalent). If it's pasted text, write it as-is. Save to <corpus>/inputs/emails/<YYYY-MM-DD>-<slug>.eml. Slug = lowercased sender domain + short subject hash.

2. Parse: extract From, Reply-To, Return-Path, Subject, Date, List-Unsubscribe, plain-text body, HTML body.

3. Run the email-analyst agent (agents/email-analyst.md) on the saved file. It returns structured JSON: indicators, tracking pixels, confidence, and a filter_instruction snippet.

4. Append to inventories (all under <corpus>/data/):

   • processed-emails.json — full entry: filename, dates, sender, subject, body_plain, spam_confidence, spam_reasoning, why_added (the user's rationale), flags object (scraped_personalisation, asymmetric_ask, tracking_links, tracking_pixels, artificial_urgency, templated_structure, commercial_disguised_as_personal), trackers_found counts.
   • sender-blocklist.json — { email, domain, display_name, reason, date }.
   • tracking-domains.json — for each pixel/click-tracker the analyst found.

5. Suggest a filter pattern: based on indicators, propose ONE concrete rule the user can paste into Gmail / Sieve / Fastmail filters. Examples:

   • from:(*@<domain>) has:the-words "<scraped phrase>" → label + skip inbox
   • list:(<list-id>) → trash when a List-Id header is the strongest signal
   • sender-domain block when the campaign is single-source Keep it minimal — over-broad filters cause false positives.

6. Suggest system-prompt tuning: emit a single bullet the user can paste into the system prompt of an AI email-triage agent. Format: "If an email , treat as scraped-list outreach unless the sender has prior thread history." Phrase as an additive heuristic, not a replacement for existing prompt.

7. Offer optional actions (ask before doing — these have side-effects):

   • Server-side block via Gmail filter: if a Google Workspace email MCP is available (look for mcp__*__create_filter or similar), offer to create a filter that auto-trashes/labels future mail from this sender. If the older scripts/gmail-block-sender.py is being used by the user instead of an MCP, surface it as a fallback path but recommend the MCP route.
   • Draft a manual unsubscribe-request reply: compose a brief, professional reply asking to be removed from the list and any other lists at the sender's organisation, threaded on the original message. Default tone: polite-firm. If the user signals frustration, allow a passive-aggressive variant (acceptable for genuine spam — never for marginal cases). Send via the email MCP only after the user confirms.

8. Report to the user, in this shape:

   • corpus entry filename
   • spam confidence + one-line reasoning
   • filter pattern suggestion (copy-pasteable)
   • system-prompt tuning bullet
   • what side-effects (block / reply) were performed vs offered

Privacy

Raw .eml may contain personal data (recipient address, internal references, embedded recipient IDs in tracking links). Default to keeping the corpus local. If the user wants to share samples publicly (e.g. to seed a research dataset), redact via the existing inputs/emails/sanitised/ convention before sharing — never copy raw originals into a public location.