kpi-engine

A reporting workflow that pulls cold-calling and lead-generation activity from DataSift, a real-estate marketing platform, and turns it into graded performance reports.

In plain words
What is it for?
Use it to report dials, conversations, contact rates, correct numbers, dispositions, leads, talk time, and progress toward appointments and contracts.
Why use it?
It uses the account's dated activity records to show what happened by caller and period, including activity that standard dashboard summaries may miss.

Skill for Claude CodeCodex

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.

agentmods
npx agentmods add skills/datasift-ty-personal/siftstack/kpi-engine
Any agent
npx skills add DataSift-Ty-Personal/SiftStack --skill kpi-engine
Clone the repo
git clone --depth 1 https://github.com/DataSift-Ty-Personal/SiftStack

Made for: Claude Code, Codex.

Per session 125 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,196 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
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 $0.00125 $0.01196
Opus 5 $0.00063 $0.00598
Sonnet 5 $0.00025 $0.00239
Haiku 4.5 $0.00013 $0.00120

Measured 2d ago against content hash 547314f08a71, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

kpi-engine 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 2d ago.

The scan reads SKILL.md. This mod also ships 1 executable file (scripts/pull_kpis.py), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

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/kpi-engine/SKILL.md · 59 lines

How it starts

The opening of the file, as written. The whole thing — 59 lines — stays where its author put it; the contents beside it link to each section on GitHub.

KPI Engine - universal DataSift KPI reporting

Pulls your real calling activity from DataSift's per-record activity log (every call, text, and disposition event, caller-attributed and timestamped) and turns it into a graded KPI report: per caller, per day, and account-wide, with funnel pacing toward deals.

Why the activity log: DataSift's dashboard widgets are not exposed to scripts and cannot cleanly isolate "what happened on the phones this week." The per-record log is the one date-accurate, caller-attributed source.

Quick start

  1. Log into app.reisift.io. Open DevTools (F12) -> Network tab -> click any request to apiv2.reisift.io -> copy the authorization: Bearer <token> value (the long JWT, without the word Bearer).
  2. Save it: set env var REISIFT_TOKEN, or paste it into a file named reisift_token.txt next to the script. Tokens last about 48 hours; repeat when it expires.
  3. Run:
python scripts/pull_kpis.py --days 7                 # trailing week
python scripts/pull_kpis.py --from 2026-07-06 --to 2026-07-16
python scripts/pull_kpis.py --days 1                 # today
python scripts/pull_kpis.py --days 7 --xlsx          # also build an Excel workbook
python scripts/pull_kpis.py --days 7 --detail        # + record-level CSV (one row per record worked)
python scripts/pull_kpis.py --days 7 --slack <webhook-url>   # post digest to Slack

No dependencies for markdown/CSV output (pure standard library). Excel output needs pip install openpyxl. Expect roughly 2-5 minutes per week of data (it reads each worked record's log).

What you get

  • Volume: dials, answered, no-answer, records touched, talk time, first/last call window, dials per hour.
  • Three rates, never collapsed into one "connect rate":
    • answer rate = answered / dials (loosest - includes voicemail pickups)
    • conversation rate = answered calls of 60s+ / dials (meaningful = 120s+)
    • contact rate = correct numbers / dials (right party confirmed)
  • Dispositions: correct / wrong / dead / DNC numbers; not interested, follow-ups, dead leads.
  • Leads that actually count: most reports only count Cold/Warm/Hot Lead statuses and miss that first-touch leads land in new_lead and No Contact New Lead - so leads read 0 while your callers produce 2-3 a day. This skill counts the full lead-status set (configurable in benchmarks.json).
  • Funnel pacing: dials per correct number vs the ~9 (phone-scored) / ~32 (blind) benchmarks, correct numbers toward the 100-correct ~= 1-deal ratio, projected appointments from your appointment-take rate, projected contracts from your leads-per-contract ratio.
  • Per-caller scorecards: dial floor (150/day) and conversation floor (5/day) MET or BELOW per caller, scaled by days active, plus lead targets.

Read the full file on GitHub · 59 lines

Files

What ships with it

2 files 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.

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. 2d ago First seen · 59 lines · 125 tokens per session scan A 547314f08a71

Subscribe to this mod's changes

kpi-engine is a skill published in the GitHub repository DataSift-Ty-Personal/SiftStack (21 stars, last pushed 4d ago), licensed MIT. It adds 125 tokens to every session and 1,196 once invoked, about $0.0006 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 2026-08-30.

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