Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/evoelsewhere/evofluxnpx agentmods add skills/evoelsewhere/evoflux/enrich-company-and-contact-dataWrote 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/evoelsewhere/evoflux/enrich-company-and-contact-data)<a href="https://agentmods.dev/skills/evoelsewhere/evoflux/enrich-company-and-contact-data"><img src="https://agentmods.dev/badge/skills/evoelsewhere/evoflux/enrich-company-and-contact-data/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/evoelsewhere/evoflux/enrich-company-and-contact-data"><img src="https://agentmods.dev/badge/skills/evoelsewhere/evoflux/enrich-company-and-contact-data.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 | $0.00084 | $0.02245 |
| Opus 5 | $0.00042 | $0.01123 |
| Sonnet 5 | $0.00017 | $0.00449 |
| Haiku 4.5 | $0.00008 | $0.00225 |
Grade A, and why
enrich-company-and-contact-data 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 5d 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.
This is a copy
100% identical to enrich-company-and-contact-data — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 175 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Enrich Company And Contact Data
Context-Gathering Intake
Whenever this skill asks for context, strongly prefer using the answers-ask-user-input skill and the ask_user_input tool over other tools such as request_user_input; otherwise ask directly in the conversation.
Prepare a sales person with a trusted, decision-ready view of companies or contacts: what is known, what is a strong match, what is still uncertain, and what action the data supports. This skill owns evidence resolution, ICP-fit and coverage comparisons, and source-grounded signal analysis; it does not own rep-work priority, outreach execution, or CRM writes.
Common Skill Instructions
MANDATORY: If not already in context, read and adhere closely to plugins/sales/skills/index/SKILL.md## Cross-Skill Best Practices.
Enrichment Ranking
Use this priority order when the request is broad or the working set is ambiguous.
- Explicit user-provided rows, domains, contacts, companies, ICP criteria, requested fields, or stated ranking rule
- The named CRM account set, territory, target list, or documented ICP that the user points to
- Entities that satisfy every hard filter, such as geography, industry, company size, technology, role, or seniority
- Entities with high-confidence identity resolution and enough comparable evidence to support the requested output
- Entities with a clear fit implication, reachable buying-team path, or source-grounded external signal
- Near matches and unresolved entities, clearly separated from qualified results
Do not silently broaden a supplied list into discovery, merge ambiguous entities, or rank by a criterion the user cannot inspect.
If the user asks to score or tier a list, keep the judgment limited to ICP fit, enrichment completeness, identity confidence, or defined signal strength. If the user asks which accounts to work now, where to focus, or what rep action deserves priority, route to prioritize-accounts.
Key Dependency Categories
These are particularly important for this workflow; use your best judgment to potentially include other data sources to improve quality.
What ships with it
1 file 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.
- 5d ago First seen · 175 lines · 84 tokens per session scan A 072fb8571e9e
enrich-company-and-contact-data is a skill published in the GitHub repository evoelsewhere/evoflux (7 stars, last pushed yesterday), licensed Apache-2.0. It adds 84 tokens to every session and 2,245 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to enrich-company-and-contact-data, differing in 0 lines, and is treated as a copy.
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