qual-doctor

qual-doctor is a skill for Claude Code from octavehq/lfgtm. It costs 80 tokens per session (6,130 once invoked), scanned A, original, MIT.

A diagnostic tool for agents that qualify potential customers or leads by assigning scores based on answers.

In plain words
What is it for?
Use it to test known-fit prospects, inspect scoring by question, and recommend changes to questions, weights, descriptions, and explanations.
Why use it?
It helps identify why prospects receive particular scores and where the qualification setup may be producing poor results.

Skill for Claude Code

Written for Claude Code: argument-hint in frontmatter. Also seen: names the AskUserQuestion tool.

Part of the octave plugin — 29 skills, 7 agents shipped together

Good fit Use it to test known-fit prospects, inspect scoring by question, and recommend changes to questions, weights, descriptions, and explanations.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/octavehq/lfgtm/qual-doctor
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 octavehq/lfgtm --skill qual-doctor
Clone the repo
git clone --depth 1 https://github.com/octavehq/lfgtm

Made for: Claude Code.

Or install octave, the plugin that ships this one along with the rest of its 29 skills, 7 agents.

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 qual-doctor

README.md
[![agentmods](https://agentmods.dev/badge/skills/octavehq/lfgtm/qual-doctor/github.svg)](https://agentmods.dev/skills/octavehq/lfgtm/qual-doctor)
Your own site
<a href="https://agentmods.dev/skills/octavehq/lfgtm/qual-doctor"><img src="https://agentmods.dev/badge/skills/octavehq/lfgtm/qual-doctor/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 qual-doctor

Your own site · 80×15
<a href="https://agentmods.dev/skills/octavehq/lfgtm/qual-doctor"><img src="https://agentmods.dev/badge/skills/octavehq/lfgtm/qual-doctor.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 80 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 6,130 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. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
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 $0.00080 $0.06130
Opus 5 $0.00040 $0.03065
Sonnet 5 $0.00016 $0.01226
Haiku 4.5 $0.00008 $0.00613

Measured 12d ago against content hash ab65d76897cc, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

qual-doctor 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 12d ago.

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/qual-doctor/SKILL.md · 615 lines

How it starts

The opening of the file, as written. The whole thing — 615 lines — stays where its author put it; the contents beside it link to each section on GitHub.

/qual-doctor - Qualification Agent Tuner

Diagnose why your qualification agent scores prospects the way it does, then tune it with targeted changes to questions, weights, entity descriptions, and rationales. Think of it as a doctor's visit for your qualification setup: examine, diagnose, prescribe, verify.

Principles

Follow these standards during generation. Read each before producing output.

  • Editorial rules — no AI-isms, banned vocabulary, honest analyst tone
  • Presentation principles — use for any visual output (HTML, dashboards, tables); text follows the editorial rules above
  • Octave value — prioritize grounded workspace data over generic AI content

Instructions

When the user runs /qual-doctor:

Phase 1: Setup

1a: Resolve MCP Server

The Octave MCP server provides tools like verify_connection, get_entity, qualify_person, qualify_company, run_qualify_person_agent, run_qualify_company_agent. From your tool list, identify the active Octave MCP server name (e.g. octave-acme, octave-octave-clean).

1b: Determine Execution Mode

Ask: How do you want to run qualification?

AskUserQuestion({
  questions: [{
    question: "How should I run qualification?",
    header: "Run mode",
    options: [
      { label: "Saved agent (Recommended)", description: "Use a specific qualification agent — tests exact production config including which sections are active" },
      { label: "Raw qualify tool", description: "Use qualify_person/qualify_company directly — tests against your full library" }
    ],
    multiSelect: false
  }]
})

If "Saved agent":

  1. List qualification agents for BOTH types:
    list_agents({ type: "QUALIFY_COMPANY" })
    list_agents({ type: "QUALIFY_PERSON" })
    
  2. Present the combined list — the agent type determines person vs company mode.
  3. User picks one. Then fetch full config:
    get_agent({ oId: "<selected_agent_id>" })
    

Read the full file on GitHub · 615 lines

Files

What ships with it

2 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. 12d ago First seen · 615 lines · 80 tokens per session scan A ab65d76897cc

Subscribe to this mod's changes

qual-doctor is a skill published in the GitHub repository octavehq/lfgtm (11 stars, last pushed 22d ago), licensed MIT. It adds 80 tokens to every session and 6,130 once invoked, about $0.0004 per session on Opus 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 2026-08-30.

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