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 company-data-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/company-data-normalization)<a href="https://agentmods.dev/skills/spiralcrew-ou/profilespider-agent-skills/company-data-normalization"><img src="https://agentmods.dev/badge/skills/spiralcrew-ou/profilespider-agent-skills/company-data-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/company-data-normalization"><img src="https://agentmods.dev/badge/skills/spiralcrew-ou/profilespider-agent-skills/company-data-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 | $0.00038 | $0.00409 |
| Opus 5.5 | $0.00015 | $0.00164 |
| Sonnet 5.5 | $0.00008 | $0.00082 |
| Haiku 4.5 | $0.00004 | $0.00041 |
Grade A, and why
company-data-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 2mo 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
Company Data Normalization
Purpose
Normalize company names, domains, industries, and locations into consistent values.
When to use this skill
- Standardizing company fields before a join or merge
- Cleaning a multi-source company list
- Preparing data for segmentation or analysis
- Resolving inconsistent industry labels
When not to use this skill
- You need to detect duplicates (use Duplicate Record Review)
- The records contain no company identifiers
- You need verified, authoritative registry data
Required inputs
- Company records
Optional inputs
- A canonical industry taxonomy
- A location format standard
- Known aliases to map
Rules
- Produce canonical values; do not invent unknown fields.
- Map industries only to the supplied taxonomy when given.
- Report a confidence level per record.
- List unresolved fields explicitly.
- Preserve originals alongside normalized values.
Process
- Parse each company record.
- Normalize name and domain.
- Map industry and standardize location.
- Assign confidence.
- List unresolved fields.
Output format
Return one record per company with the following fields:
- normalized_company_name
- normalized_domain
- standardized_industry
- standardized_location
- normalization_confidence
- unresolved_fields
Validation
- Confirm domains are root domains without protocol.
- Confirm industries match the taxonomy when provided.
- Confirm low-confidence rows are flagged.
Limitations
- Normalization is heuristic without an authoritative registry.
- Ambiguous names may need manual disambiguation.
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.
- 2mo ago First seen · 82 lines · 38 tokens per session scan A b26902c2157b
company-data-normalization is a skill published in the GitHub repository spiralcrew-ou/profilespider-agent-skills (11 stars, last pushed 4mo ago), licensed MIT. It adds 38 tokens to every session and 409 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 2026-08-31.
company-data-normalization compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| company-data-normalizationspiralcrew-ou/profilespider-agent-skills | Skill | 11 | 4mo ago | MIT | A | 38 | Claude Code, Codex |
| squeeze-page-builderAffitor/affiliate-skills | Skill | 767 | 18d ago | MIT | A | 114 | Claude Code |
| deepline-ads-audiencesgetaero-io/gtm-eng-skills | Skill | 73 | yesterday | MIT | A | 133 | Claude Code, Codex |
| deepline-enginegetaero-io/gtm-eng-skills | Skill | 73 | yesterday | MIT | A | 43 | Claude Code, Codex |
| deepline-plays-reviewgetaero-io/gtm-eng-skills | Skill | 73 | yesterday | MIT | A | 121 | Codex |
| b2b-marketing-playbookGingiris-1031/gingiris-skills | Skill | 89 | 2d ago | MIT | A | 53 | Claude Code, Codex |
| deepline-crm-cleaninggetaero-io/gtm-eng-skills | Skill | 73 | yesterday | MIT | A | 73 | Claude Code, Codex |