Finta SDK Patterns

What it does

When your agent uses it

• Building automated investor outreach
• Syncing data from Finta exports
• Creating custom fundraising dashboards
• With phrases like finta integration

Example prompts

• “finta integration”
• “finta patterns”
• “finta automation”
• “/finta-sdk-patterns”

Requirements

• Python 3
• Compatibility (from SKILL.md): Designed for Claude Code
• Pre-approved tools (allowed-tools): Read, Write, Edit

Workflow steps

5 steps, taken from the first numbered list in SKILL.md.

1. 1Start with a read-only, de-identified export and verify its schema before automating a transformation.
2. 2Map only approved fields into the destination; retain an explicit exclusion list for contact details, document links, and financial terms.
3. 3Use an idempotency key or import ledger so a rerun cannot create duplicate outreach, CRM records, or payment actions.
4. 4Verify webhook signatures at the edge, queue work with bounded retries, and send exhausted failures to a reviewed exception queue.
5. 5Stage integration changes with fictitious records, then canary the approved production workflow with a documented rollback switch.

What it can do on your machine

Read from SKILL.md and the folder at commit cfae287. It shows what the files ask for, not the result of running them.

• Tool permissions

  Pre-approves these tools, so the agent can use them without asking each time:

  • Read
  • Write
  • Edit

  From allowed-tools in the SKILL.md frontmatter.

• Runs code

  No scripts in the folder and no shell commands in SKILL.md (its code samples are python).

  From the folder's file list and the shell code blocks in SKILL.md.

• Network

  Links to these hosts (documentation or services it may open):

  • trustfinta.com

  From URLs in SKILL.md, links to its own repository left out.

• Credentials

  Names no API keys, tokens, secrets or passwords.

  From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

• Compatibility

  Designed for Claude Code

  From compatibility in the SKILL.md frontmatter.

Context cost

Finta SDK Patterns loads about 1.2k tokens when it runs. Until then it costs about 82 tokens; SKILL.md has 392 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it

~82

When it runs · the whole SKILL.md, loaded when a task matches

~1.2k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from jeremylongshore/tons-of-skills-marketplace at commit cfae287, republished under its MIT licence (© jeremylongshore). 392 words, ~1,193 tokens.

name

finta-sdk-patterns

description

Integration patterns for Finta fundraising CRM with email and calendar APIs. Use when building automated investor outreach, syncing data from Finta exports, or creating custom fundraising dashboards. Trigger with phrases like "finta integration", "finta patterns", "finta automation", "finta data pipeline".

allowed-tools

Read, Write, Edit

compatibility

Designed for Claude Code

version

1.7.0

license

MIT

author

Jeremy Longshore <jeremy@intentsolutions.io>

tags

saas, fundraising-crm, investor-management, finta

import pandas as pd
from pathlib import Path

class FintaPipelineTracker:
    def __init__(self, export_path: str):
        self.df = pd.read_csv(export_path)

    def investors_by_stage(self) -> dict:
        return self.df.groupby("Stage")["Name"].apply(list).to_dict()

    def conversion_funnel(self) -> list[dict]:
        stages = self.df["Stage"].value_counts()
        return [{"stage": s, "count": c} for s, c in stages.items()]

    def overdue_followups(self, days: int = 7) -> pd.DataFrame:
        self.df["Last Contact"] = pd.to_datetime(self.df["Last Contact"])
        cutoff = pd.Timestamp.now() - pd.Timedelta(days=days)
        return self.df[
            (self.df["Stage"].isin(["Follow-up", "Due Diligence"]))
            & (self.df["Last Contact"] < cutoff)
        ]

    def total_committed(self) -> float:
        closed = self.df[self.df["Stage"] == "Closed"]
        return closed["Check Size"].sum()

# Track investor email responses via Gmail API
from googleapiclient.discovery import build

def get_investor_emails(service, investor_email: str, after_date: str):
    query = f"from:{investor_email} after:{after_date}"
    results = service.users().messages().list(
        userId="me", q=query
    ).execute()
    return results.get("messages", [])

© jeremylongshore, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in skills/.curated/finta-sdk-patterns of jeremylongshore/tons-of-skills-marketplace.

Open the folder on GitHubat commit cfae287