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/closer-coachnpx skills add DataSift-Ty-Personal/SiftStack --skill closer-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/closer-coach)<a href="https://agentmods.dev/skills/datasift-ty-personal/siftstack/closer-coach"><img src="https://agentmods.dev/badge/skills/datasift-ty-personal/siftstack/closer-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 | $0.00090 | $0.00753 |
| Opus 5 | $0.00045 | $0.00377 |
| Sonnet 5 | $0.00018 | $0.00151 |
| Haiku 4.5 | $0.00009 | $0.00075 |
Grade A, and why
closer-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 5d 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 — 65 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Closer Coach
Grades acquisitions calls (offer presentation, negotiation, renegotiation,
contract talk, in-person appointment recordings) against the DataSift Closer
playbook. Same pipeline as the cold-call-coach skill; this skill applies the
closing rubric to calls the triage step classified as closing.
Requirements, setup, and costs
Identical to the cold-call-coach skill (same scripts, same session, same .env):
python scripts/smrtphone_login.py once, OPENROUTER_API_KEY in .env,
optional roster.json. Transcription costs about $0.002 per audio minute.
Pipeline
python scripts/pull_calls.py --min-seconds 60 --days 14
python scripts/transcribe.py
Then take the closing group with worth_grading: true from
output/call_coaching/review_queue.json. Closing calls are rarer and longer
than cold calls; if the queue is empty, widen the window (--days 30) or drop
--min-seconds to 45. Very long calls (over ~25 min) may need the MP3 split
with ffmpeg (-f segment -segment_time 900) and the parts transcribed in order.
Grading procedure
- Read
references/rubric.mdfully (gate with the in-person variant, auto-fails, 6 categories weighted 15/25/20/25/10/5, anchors, template, the required SCORES JSON footer). - Read the whole transcript once; verify AGENT/SELLER labels by content.
- Score criterion by criterion against the 0/3/5 anchors (quote required),
compute the weighted /100 and band, and write one report per call to
output/call_coaching/reports/closing/{call_id}_{caller}.mdwith the CRITERION SCORES table and SCORES JSON footer. Closing calls deserve depth: include a negotiation timeline (each price or term mentioned, who moved, what triggered the move) and a "the moment it was won or lost" section quoting the pivotal exchange. - Export:
python scripts/export_excel.py --dir output/call_coaching/reports/closing
Grading quality protocol (mandatory verify pass)
Same as cold-call-coach: verbatim quotes only, evidence per criterion, N/A discipline with listed redistribution (N/A when the situation never arose, e.g. renegotiation criteria on a first-offer call), math that foots exactly with a matching JSON footer, and call-audible content only.
What ships with it
7 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.
- 5d ago First seen · 65 lines · 90 tokens per session scan A 5c95878bae1c
closer-coach is a skill published in the GitHub repository DataSift-Ty-Personal/SiftStack (21 stars, last pushed 2d ago), licensed MIT. It adds 90 tokens to every session and 753 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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