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 agentmods add skills/datasift-ty-personal/siftstack/kpi-enginenpx skills add DataSift-Ty-Personal/SiftStack --skill kpi-enginegit clone --depth 1 https://github.com/DataSift-Ty-Personal/SiftStackWhat 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 | $0.00125 | $0.01196 |
| Opus 5 | $0.00063 | $0.00598 |
| Sonnet 5 | $0.00025 | $0.00239 |
| Haiku 4.5 | $0.00013 | $0.00120 |
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.
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.
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
- Log into app.reisift.io. Open DevTools (F12) -> Network tab -> click any request to
apiv2.reisift.io-> copy theauthorization: Bearer <token>value (the long JWT, without the word Bearer). - Save it: set env var
REISIFT_TOKEN, or paste it into a file namedreisift_token.txtnext to the script. Tokens last about 48 hours; repeat when it expires. - 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_leadandNo Contact New Lead- so leads read 0 while your callers produce 2-3 a day. This skill counts the full lead-status set (configurable inbenchmarks.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.
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.
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.
- 2d ago First seen · 59 lines · 125 tokens per session scan A 547314f08a71
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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