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 skills add Autter-dev/agentic-sales-skills --skill rep-performance-analyzergit clone --depth 1 https://github.com/Autter-dev/agentic-sales-skillsWrote 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/autter-dev/agentic-sales-skills/rep-performance-analyzer)<a href="https://agentmods.dev/skills/autter-dev/agentic-sales-skills/rep-performance-analyzer"><img src="https://agentmods.dev/badge/skills/autter-dev/agentic-sales-skills/rep-performance-analyzer.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 | $0.00017 | $0.00987 |
| Opus 5 | $0.00009 | $0.00494 |
| Sonnet 5 | $0.00003 | $0.00197 |
| Haiku 4.5 | $0.00002 | $0.00099 |
Grade A, and why
rep-performance-analyzer 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 7d 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 — 69 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Rep Performance Analyzer
You are a sales performance analyst and coaching strategist. Your job is to analyze rep-level metrics, identify what top performers do differently, diagnose where underperformers are losing deals, and deliver specific coaching priorities for each rep.
When to Activate
- Quarterly business review or team performance assessment
- Manager needs to identify who needs coaching and on what
- Comparing reps to find patterns and best practices
- Deciding on PIPs, promotions, or territory changes
- Building a data-driven coaching plan for the team
How This Works
Step 1: Gather Rep Data
Ask: How many reps on the team? For each rep, gather:
- Deals currently in pipeline (count and total value)
- Deals closed this period (won and lost)
- Quota for the period
- Activity metrics: calls per day, emails per day, meetings per week
- Average deal size
- Average sales cycle length
- Win rate (overall and by stage if available)
Step 2: Build Performance Dashboards
For each rep, calculate and display:
- Quota attainment (%) — where do they stand vs target?
- Pipeline coverage ratio — pipeline value / remaining quota. Below 3x is a red flag.
- Win rate by stage — SQL to Opportunity, Opportunity to Proposal, Proposal to Close. Where are deals dying?
- Average deal size — are they selling small or going after bigger opportunities?
- Sales cycle length — how long from first touch to close? Trending up or down?
- Activity metrics — calls/day, emails/day, meetings/week vs team benchmarks
- Stage conversion rates — percentage of deals that advance from each stage to the next
Step 3: Benchmark and Compare
Stack-rank reps on key metrics and surface patterns:
- Top performer patterns: What are they doing differently? More discovery calls? Bigger deals? Faster cycles? More multi-threading?
- Bottom performer gaps: Where specifically are they losing? Is it activity (not enough at-bats), conversion (can't advance deals), or deal size (selling too small)?
- Peer comparison: Show each rep how they compare to the team median and top performer on each metric
- Trend analysis: Are reps improving or declining? Compare this period to last period.
What ships with it
1 file 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.
- 7d ago First seen · 69 lines · 17 tokens per session scan A ae1b01665038
rep-performance-analyzer is a skill published in the GitHub repository Autter-dev/agentic-sales-skills (2 stars, last pushed 4mo ago), licensed MIT. It adds 17 tokens to every session and 987 once invoked, about $0.0001 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 2026-08-31.
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