cold-email-reply-scoring

name	cold-email-reply-scoring
description	Classify replies to cold email into 4 categories — buy-signal, positive, neutral, not-interested — using deterministic features (intent keywords, time-to-reply, length, question count, calendar/asset asks). Routes each reply per brand-config.operations.reply_routing. Backed by a Python script that uses regex + features (no LLM). Loaded by the main cold-email skill when the operator processes their reply queue.
user-invocable	false
allowed-tools	["Read","Bash","Write","Grep"]

Cold Email Reply Scoring — deterministic reply classifier

Real classifier, not vibes. Backed by scripts/score_reply.py for deterministic categorization. The output is a category + confidence

• recommended routing action.

Activation

Loaded by:

• cold-email-weekly-rhythm — Wednesday + Friday reply triage
• User invocation: "Score this reply", "Classify this response"

Output categories

The classifier returns one of 4 categories with a confidence score:

Category	Signal	Routing (per brand-config)
buy-signal	Explicit ask to talk, calendar request, price question, decision-maker tag	brand-config.operations.reply_routing.buy_signal
positive	Curious, asking follow-up Qs, expressing interest, asking for proof	positive
neutral	"Not now", "circle back later", "send more info" without commitment	neutral (often → nurture stream)
not-interested	Explicit no, unsubscribe ask, hostile, out-of-office on repeat	not-interested

Feature set (what the classifier uses)

Pure feature engineering — no LLM, no vendor APIs:

1. Intent keywords — regex over 50+ patterns per category

   • buy-signal: "send the calendar", "what's the price", "demo", "case study", "intro"
   • positive: "tell me more", "interesting", "curious", "send the deck"
   • neutral: "not the right time", "Q4", "send more info", "in a few months"
   • not-interested: "unsubscribe", "stop emailing", "not a fit", "no thanks"

2. Time-to-reply — minutes since send

   • <15 min after send → likely auto-reply (out-of-office, etc.)
   • <60 min → high-intent (operator was working, saw it, replied immediately)
   • 60 min - 24h → engaged reply

   • 24h → background reply

3. Reply length — chars

   • <20 chars → 1-word answer, usually not-interested or short positive
   • 20-200 → typical engaged reply

   • 200 → often a substantive response (rare; high-signal)

4. Question count — # of "?" in the body

   • 0 → declarative (could be any category)
   • 1-2 → engaged with specific questions
   • 3+ → either highly engaged or asking-for-everything (lower convert)

5. Calendar / asset ask — regex

   • "send ", "let's get a time", "what dates work" → buy-signal
   • "send the case study", "send the deck" → positive (could be buy-signal if combined with other features)

6. Out-of-office indicators — regex

   • "out of office", "OOO", "auto-reply", "I'm currently away" → exclude from scoring
   • These get marked auto-reply (5th category) and not routed

Output format

# Reply scoring

From: <sender>
Subject: Re: <original subject>
Time-to-reply: <minutes since send>
Length: <N chars>

## Classification
Category: **buy-signal** (confidence: 0.87)

## Why
- "send me a calendar link" → buy-signal +0.5
- "what's the price for Series-B teams" → buy-signal +0.4
- Reply within 27 minutes → buy-signal +0.2
- Length 142 chars → engaged-length +0.1
- No not-interested patterns matched

## Recommended routing
Per your brand-config.operations.reply_routing.buy_signal:
→ Slack #pipeline + create HS deal
→ Reply within 4 hours

## Suggested reply opener (not generated; use your own voice)
"Sent — calendar link [here]. <Specific qualifying Q to set the agenda>"

Batch mode

For triaging the Wednesday/Friday batch of replies:

# In the project, with reply data in replies.jsonl
python3 ../../scripts/score_reply.py --batch replies.jsonl --format json | jq

Output:

# Batch reply scoring — <date>

| # | From | Category | Confidence | Route |
|---|---|---|---|---|
| 1 | [e-mail] | buy-signal | 0.87 | Slack + HS deal |
| 2 | [e-mail] | positive | 0.74 | Reply <4h |
| 3 | [e-mail] | auto-reply | 1.00 | Skip |
| 4 | [e-mail] | not-interested | 0.92 | Suppress + log |
| ... |

Why deterministic > LLM here

Reply classification is high-throughput (50-200/week per operator). LLM classification:

• Costs money per reply
• Adds 1-3 seconds latency per reply
• Drifts as LLMs version
• Hard to audit

Feature-based classification:

• Zero cost per reply
• <10ms per reply
• Stable across years
• Fully auditable (every score has a feature contribution list)

For a 100-reply week, the deterministic classifier runs in <1 second and gives the same answer in 6 months as it does today.

False-positive handling

The script flags low-confidence classifications (confidence <0.6) for human review. These are usually:

• Short replies (1-3 words) that could go either way
• Replies in non-English (the lexicon is English-only)
• Sarcasm / context-dependent meaning

Low-confidence replies surface as:

# Manual review needed

<sender>: "<reply body>"
Category candidates: positive (0.42) | neutral (0.38)
→ Read carefully before routing

Calibration

The lexicon is calibrated against JMC's review of 1000+ real cold-email replies (anonymized). Accuracy on the held-out set:

• buy-signal vs not: 94%
• positive vs neutral: 81%
• not-interested precision: 97%

Re-calibration ships when the lexicon updates (every 90 days minimum, or when bulk-sender rules change).

References

• ../../scripts/score_reply.py — the actual classifier
• references/reply-lexicon.md — feature regex source
• ../cold-email-weekly-rhythm/SKILL.md — where batch triage runs
• brand-config.operations.reply_routing — the routing rules