databar-waterfall

databar-waterfall is a skill for Claude Code, Codex from databar-ai/databar-mcp-server. It costs 70 tokens per session (1,191 once invoked), scanned A, original, MIT.

A Databar lookup workflow that tries several data providers in sequence and stops when one finds the requested information.

In plain words
What is it for?
Finding contact details or social profiles for one person or many records when the user wants multiple sources tried automatically.
Why use it?
It increases the chance of finding an email address, phone number, or profile when a single data source has no result.

Skill for Claude CodeCodex

Which agent this was written for is unclear — built for openclaw. Also seen: mentions Claude Code; mentions Codex; built for openclaw.

Good fit Finding contact details or social profiles for one person or many records when the user wants multiple sources tried automatically.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/databar-ai/databar-mcp-server/databar-waterfall
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 databar-ai/databar-mcp-server --skill databar-waterfall
Clone the repo
git clone --depth 1 https://github.com/databar-ai/databar-mcp-server

Made for: Claude Code, Codex.

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 databar-waterfall

README.md
[![agentmods](https://agentmods.dev/badge/skills/databar-ai/databar-mcp-server/databar-waterfall/github.svg)](https://agentmods.dev/skills/databar-ai/databar-mcp-server/databar-waterfall)
Your own site
<a href="https://agentmods.dev/skills/databar-ai/databar-mcp-server/databar-waterfall"><img src="https://agentmods.dev/badge/skills/databar-ai/databar-mcp-server/databar-waterfall/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 databar-waterfall

Your own site · 80×15
<a href="https://agentmods.dev/skills/databar-ai/databar-mcp-server/databar-waterfall"><img src="https://agentmods.dev/badge/skills/databar-ai/databar-mcp-server/databar-waterfall.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 70 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,191 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.
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 $0.00070 $0.01191
Opus 5.5 $0.00028 $0.00476
Sonnet 5.5 $0.00014 $0.00238
Haiku 4.5 $0.00007 $0.00119

Measured 2mo ago against content hash 09ffd8686382, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

databar-waterfall 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.

skills/databar-waterfall/SKILL.md · 115 lines

How it starts

The opening of the file, as written. The whole thing — 115 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Databar Waterfall Enrichment

Waterfalls try multiple data providers in sequence until one returns a result. This maximizes the chance of finding the data. For example, an "email finder" waterfall might try Provider A first, and if it fails, try Provider B, then C — stopping as soon as one succeeds.

When to use this vs a regular enrichment

  • Waterfall: User wants the highest chance of finding the data and doesn't care which provider returns it. Best for email finding, phone lookups, and contact discovery.
  • Regular enrichment (databar-enrichment skill): User wants data from a specific provider, or the task only has one relevant provider.

Workflow

  1. Identify the user's goal. Common waterfall use cases:

    • Finding someone's email address (given name + company)
    • Looking up a phone number
    • Finding social profiles
    • Any task where the user wants to "try everything"
  2. Search for available waterfalls. Call search_waterfalls with a descriptive query (e.g. "email finder", "phone lookup").

  3. Pick the best waterfall. Review the results. Check:

    • identifier — the string you'll pass to run it
    • input_params — required parameters
    • available_enrichments count — more providers = higher success rate
  4. Determine single vs bulk.

    • If the user provides one record: use run_waterfall
    • If the user provides multiple records: use run_bulk_waterfall
  5. Confirm cost with the user. Waterfall pricing varies by provider. Tell the user the waterfall name and that costs depend on which provider succeeds.

    • For bulk: "Running {waterfall_name} on {count} records. Cost depends on which providers succeed."
  6. Run the waterfall.

    Single record:

    run_waterfall({
      waterfall_identifier: "email_getter",
      params: { full_name: "John Smith", company_name: "Google" }
    })
    

    Multiple records:

    run_bulk_waterfall({
      waterfall_identifier: "email_getter",
      params_list: [
        { full_name: "John Smith", company_name: "Google" },
        { full_name: "Jane Doe", company_name: "Microsoft" }
      ]
    })
    

Read the full file on GitHub · 115 lines

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. 2mo ago First seen · 115 lines · 70 tokens per session scan A 09ffd8686382

Subscribe to this mod's changes

databar-waterfall is a skill published in the GitHub repository databar-ai/databar-mcp-server (5 stars, last pushed 7mo ago), licensed MIT. It adds 70 tokens to every session and 1,191 once invoked, about $0.0003 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.

Related

databar-waterfall compared with skills from other repositories

databar-waterfall next to 6 skills from other repositories
ModTypeStarsLast pushLicenceScanTokensWorks with
databar-waterfalldatabar-ai/databar-mcp-serverSkill57mo agoMITA70Claude Code, Codex
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playwright-climicrosoft/playwrightSkill99ktodayApache-2.0A19Claude Code
local-ai-agentsmicrosoft/ai-agents-for-beginnersSkill79ktodayMITA200Claude Code, Codex
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