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 spiralcrew-ou/profilespider-agent-skills --skill job-title-normalizationgit clone --depth 1 https://github.com/spiralcrew-ou/profilespider-agent-skillsWrote 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/spiralcrew-ou/profilespider-agent-skills/job-title-normalization)<a href="https://agentmods.dev/skills/spiralcrew-ou/profilespider-agent-skills/job-title-normalization"><img src="https://agentmods.dev/badge/skills/spiralcrew-ou/profilespider-agent-skills/job-title-normalization/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/spiralcrew-ou/profilespider-agent-skills/job-title-normalization"><img src="https://agentmods.dev/badge/skills/spiralcrew-ou/profilespider-agent-skills/job-title-normalization.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00044 | $0.00411 |
| Opus 5 | $0.00022 | $0.00205 |
| Sonnet 5 | $0.00009 | $0.00082 |
| Haiku 4.5 | $0.00004 | $0.00041 |
Grade A, and why
job-title-normalization 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 11d 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.
What it actually says
Job Title Normalization
Purpose
Convert inconsistent job titles into standardized role, department, and seniority labels.
When to use this skill
- Standardizing titles for segmentation or routing
- Filtering a list by seniority reliably
- Mapping titles to departments for targeting
- Cleaning multi-source contact data
When not to use this skill
- The records have no title field
- Titles are in languages you have not configured
- You need verified org-chart data
Required inputs
- Records containing job titles
Optional inputs
- A role-family taxonomy
- A seniority scale
- Department definitions
Rules
- Map to the supplied taxonomy and scale when provided.
- Preserve the original title.
- Report confidence and ambiguity.
- Do not invent seniority not implied by the title.
- Be consistent across identical titles.
Process
- Parse each title.
- Map to role family and department.
- Assign seniority.
- Record confidence and ambiguity.
- Output original and normalized values.
Output format
Return one record per title with the following fields:
- original_title
- normalized_title
- role_family
- department
- seniority
- confidence
- ambiguity_notes
Validation
- Confirm identical titles map identically.
- Confirm low-confidence rows are flagged.
- Confirm seniority is implied by the title.
Limitations
- Titles vary by company; mapping is heuristic.
- Inflated or vague titles may misstate seniority.
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.
- 11d ago First seen · 83 lines · 44 tokens per session scan A 8e58eb201ac2
job-title-normalization is a skill published in the GitHub repository spiralcrew-ou/profilespider-agent-skills (10 stars, last pushed 2mo ago), licensed MIT. It adds 44 tokens to every session and 411 once invoked, about $0.0002 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 2026-08-31.
Other skills, from other repositories
inbound-lead-enrichment
Fills in missing data for inbound leads — researches the company, identifies the person's role and seniority, finds other stakeholders at the company, checks for existing CRM relationships, and updates the lead record. Produces enriched lead data ready for qualification or outreach. Tool-agnostic.
inbound-lead-qualification
Qualifies inbound leads against full ICP criteria — company size, industry, use case fit, role/seniority of the person. Checks CRM and existing customer base for duplicates and existing relationships. Outputs a scored CSV with qualification status, reasoning, and pipeline overlap flags. Tool-agnostic — works with any…
inbound-lead-triage
Triages all inbound leads from a given period — demo requests, free trial signups, content downloads, webinar registrations, chatbot conversations. Classifies by urgency, qualifies against ICP, enriches with context, and produces a prioritized action queue with recommended response for each lead. Tool-agnostic — works…
company-contact-finder
Find decision-makers at a specific company using Apollo, Crustdata, Fiber, and PDL people search via Gooseworks MCP. Given a company name and target titles, returns a list of contacts with name, title, LinkedIn URL, and location.
funding-signal-monitor
Monitor web sources for Series A-C funding announcements. Aggregates signals from TechCrunch, Crunchbase (via web search), Twitter, Hacker News, and LinkedIn. Filters by stage, amount, and industry. Returns qualified recently-funded companies ready for outreach.
kol-engager-icp
Find ICP-fit leads from KOL audiences on LinkedIn. Given a list of KOLs, scrapes their most relevant high-engagement post from the last 30 days, extracts engagers (reactors + commenters), pre-filters by position, enriches top profiles, and ICP-classifies. Cost-controlled: 1 post per KOL. Use when someone wants to…