ocmanjos-lead-qualifier

ocmanjos-lead-qualifier is a skill for Claude Code, Codex from CALLE-AI/awesome-phone-call-agents. It costs 40 tokens per session (763 once invoked), scanned A, original, MIT.

A sales follow-up workflow for product enquiries received through WhatsApp and stored in Supabase, a hosted database service. It applies rules about product category, price, and how recently someone enquired, then calls qualifying leads to confirm interest and delivery or pickup preference.

In plain words
What is it for?
Use it to follow up on recent, high-value WhatsApp enquiries using a product price spreadsheet and configured checks for purchases, competitors, and opt-outs.
Why use it?
Free-text enquiries can include many people who are no longer interested, have opted out, or do not match the business rules. Filtering them before calling reduces unnecessary follow-up and avoids relying on repeated text messages.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit Use it to follow up on recent, high-value WhatsApp enquiries using a product price spreadsheet and configured checks for purchases, competitors, and opt-outs.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/calle-ai/awesome-phone-call-agents/ocmanjos-lead-qualifier
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add CALLE-AI/awesome-phone-call-agents --skill ocmanjos-lead-qualifier
Clone the repo
git clone --depth 1 https://github.com/CALLE-AI/awesome-phone-call-agents

Made for: Claude Code, Codex.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for ocmanjos-lead-qualifier

README.md
[![agentmods](https://agentmods.dev/badge/skills/calle-ai/awesome-phone-call-agents/ocmanjos-lead-qualifier/github.svg)](https://agentmods.dev/skills/calle-ai/awesome-phone-call-agents/ocmanjos-lead-qualifier)
Your own site
<a href="https://agentmods.dev/skills/calle-ai/awesome-phone-call-agents/ocmanjos-lead-qualifier"><img src="https://agentmods.dev/badge/skills/calle-ai/awesome-phone-call-agents/ocmanjos-lead-qualifier/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for ocmanjos-lead-qualifier

Your own site · 80×15
<a href="https://agentmods.dev/skills/calle-ai/awesome-phone-call-agents/ocmanjos-lead-qualifier"><img src="https://agentmods.dev/badge/skills/calle-ai/awesome-phone-call-agents/ocmanjos-lead-qualifier.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 40 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 763 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00040 $0.00763
Opus 5.5 $0.00016 $0.00305
Sonnet 5.5 $0.00008 $0.00153
Haiku 4.5 $0.00004 $0.00076

Measured 1mo ago against content hash e9c1646e2cb0, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

ocmanjos-lead-qualifier scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.

The scan reads SKILL.md. This mod also ships 1 executable file (scripts/fetchEnquiries.js), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

skills/ocmanjos-lead-qualifier/SKILL.md · 54 lines

How it starts

The opening of the file, as written. The whole thing — 54 lines — stays where its author put it; the contents beside it link to each section on GitHub.

O.C. Manjos Lead Qualifier

Scores inbound product enquiries (captured via a WhatsApp sales AI into Supabase) against category, price, and recency rules, then places a CALL-E outbound call to qualifying leads to confirm interest and capture delivery or pickup preference.

Use it for

  • Following up on high-value product enquiries that went unanswered on WhatsApp within 72 hours
  • Confirming customer interest and delivery preference via voice instead of relying on repeat text follow-ups
  • Demonstrating a real small-business use case for CALL-E: filtering noisy, free-text enquiry data down to genuinely qualifying leads before spending a call

Do not use it to

  • Call numbers not sourced from an explicit customer enquiry
  • Call leads marked as already purchased, a known competitor, or opted out
  • Guess phone numbers, prices, or product categories - the skill only prices and calls what matches configured rules and real price-sheet data

Setup

  1. A Supabase table with enquiry records (product_asked, created_at, whatsapp_responded, known_competitor, already_purchased, opted_out)
  2. A product price sheet (.xlsx) with description, unit price, and sell price columns
  3. .env with SUPABASE_URL and SUPABASE_ANON_KEY (never commit this file)
  4. CALL-E CLI authenticated (calle auth login)

How it works

  1. Pull recent enquiries from Supabase
  2. Extract a product keyword and variant (e.g. size/phase) from free-text product_asked
  3. Look up the real sell price from the price sheet, matched by token, not exact phrase
  4. Score against category/price/recency/disqualifier rules
    1. For qualifying leads, the operator manually runs CALL-E's plan_call via the CLI, reviews the confirm_summary, then runs run_call. This script identifies who to call; it does not itself invoke CALL-E.

Qualification rule (example, tune to your own product line)

IF (category = "Distribution Board" AND value >= 35000) OR (category = "Box/Conduit" AND value >= 2500) OR (category = "Solar/Flood Light" AND value >= 34000) AND enquiry_age <= 72h AND no_whatsapp_response = true AND NOT (known_competitor OR already_purchased OR opted_out) THEN CALL

Read the full file on GitHub · 54 lines

Files

What ships with it

5 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 1mo ago First seen · 54 lines · 40 tokens per session scan A e9c1646e2cb0

Subscribe to this mod's changes

ocmanjos-lead-qualifier is a skill published in the GitHub repository CALLE-AI/awesome-phone-call-agents (109 stars, last pushed yesterday), licensed MIT. It adds 40 tokens to every session and 763 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-15.

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