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.
npx skills add octavehq/lfgtm --skill qual-doctorgit clone --depth 1 https://github.com/octavehq/lfgtmWrote 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.
[](https://agentmods.dev/skills/octavehq/lfgtm/qual-doctor)<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.
<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>- NVIDIA SkillSpector pass
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.
| Model | Per session | Once 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 |
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.
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":
- List qualification agents for BOTH types:
list_agents({ type: "QUALIFY_COMPANY" }) list_agents({ type: "QUALIFY_PERSON" }) - Present the combined list — the agent type determines person vs company mode.
- User picks one. Then fetch full config:
get_agent({ oId: "<selected_agent_id>" })
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.
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.
- 12d ago First seen · 615 lines · 80 tokens per session scan A ab65d76897cc
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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