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/cold-call-coachnpx skills add DataSift-Ty-Personal/SiftStack --skill cold-call-coachgit clone --depth 1 https://github.com/DataSift-Ty-Personal/SiftStackWrote 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/datasift-ty-personal/siftstack/cold-call-coach)<a href="https://agentmods.dev/skills/datasift-ty-personal/siftstack/cold-call-coach"><img src="https://agentmods.dev/badge/skills/datasift-ty-personal/siftstack/cold-call-coach.svg" alt="Measured on agentmods" 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 | $0.00103 | $0.01262 |
| Opus 5 | $0.00051 | $0.00631 |
| Sonnet 5 | $0.00021 | $0.00252 |
| Haiku 4.5 | $0.00010 | $0.00126 |
Grade A, and why
cold-call-coach 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 6d 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 — 111 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Cold Call Coach
Grades real first-touch cold calls against the DataSift cold-calling playbook.
The pipeline pulls recordings straight from your SmrtPhone web session (no API
exists for this), transcribes them with an audio-capable model so tonality
observations are heard rather than guessed, then Claude grades each gradeable
conversation against references/rubric.md and exports everything to Excel.
Requirements and setup (once)
- Python 3.10+ with:
pip install playwright openpyxlthenplaywright install chromium - SmrtPhone login:
python scripts/smrtphone_login.py(headed window opens, log in once; the session is saved tosmrtphone_state.jsonand lasts weeks) - OpenRouter key for transcription: copy
scripts/env.exampleto.envand setOPENROUTER_API_KEY(get one at openrouter.ai) - Optional team roster: copy
scripts/roster.example.jsontoroster.jsonto pin caller roles (a dedicated cold caller's calls never route to another rubric) and exclude departed callers
Costs (transparent)
- Transcription: about $0.002 per audio minute (Gemini 2.5 Flash via OpenRouter). A week of coaching for a 3-caller team (say 60 gradeable calls averaging 2 minutes) costs roughly $0.25 in transcription.
- Recording downloads and the call log are free (your existing SmrtPhone plan).
- Grading runs inside your Claude session.
Pipeline
Run from your project folder:
python scripts/pull_calls.py --min-seconds 60 --days 7 # call log + MP3s
python scripts/transcribe.py # audio -> transcripts + triage
Outputs land in output/call_coaching/:
call_log.json/calls_to_review.json- every call, then the review settranscripts/{call_id}.md- diarized transcript with inline delivery notes ([long pause 4s], [rushed], [warm tone]) plus a DELIVERY SUMMARY block (pace, agent energy, talk balance, notable audio moments) and a label check linereview_queue.json- calls grouped: cold_call / lead_management / closing / not_gradeable
What ships with it
12 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.
- references/calibration/example_full_needswork.md 11 KB
- references/calibration/example_full_strong.md 9.0 KB
- references/calibration/example_short_call.md 4.6 KB
- references/calibration/README.md 980 B
- references/reliability.md 2.3 KB
- references/rubric.md 29 KB
- scripts/env.example 231 B
- scripts/export_excel.py 19 KB runs code
- scripts/pull_calls.py 8.2 KB runs code
- scripts/roster.example.json 414 B
- scripts/smrtphone_login.py 2.3 KB runs code
- scripts/transcribe.py 11 KB runs code
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.
- 6d ago First seen · 111 lines · 103 tokens per session scan A 997cbc4ffe99
cold-call-coach is a skill published in the GitHub repository DataSift-Ty-Personal/SiftStack (21 stars, last pushed 2d ago), licensed MIT. It adds 103 tokens to every session and 1,262 once invoked, about $0.0005 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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