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/carta/plugins/deal-flow-analyticsnpx skills add carta/plugins --skill deal-flow-analyticsgit clone --depth 1 https://github.com/carta/pluginsWhat 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 | $0.00140 | $0.02137 |
| Opus 5 | $0.00070 | $0.01069 |
| Sonnet 5 | $0.00028 | $0.00427 |
| Haiku 4.5 | $0.00014 | $0.00214 |
Grade A, and why
deal-flow-analytics 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 — 182 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Overview
Every tenant configures its own custom deal fields, so the dimensions worth
reporting on differ per org. Discover them at runtime instead of assuming a
fixed list, then aggregate with crm:aggregate_deals — never tally deals by
hand from crm:search_deals.
aggregate_deals groups by one field at a time. Cover multiple
dimensions with multiple calls, not one call with multiple group_by values.
Step 1 — Discover and classify fields
crm_call_tool({ "name": "crm:get_deal_fields", "arguments": {} })
Classify every field using its type alone — no probing needed:
- Groupable (
dropdown,multiselect,boolean, or a standardsetfield likestage,tags,people.*,dealLead) — the value space is bounded by the field'soptionslist, so it's always safe toaggregate_dealson directly. - Continuous (
number,input,textarea,datepicker, or a relational reference likecompany/contacts-list) — unbounded values. Nevergroup_bythese — on this plugin's test tenant, fields likeEBITDA,ARR, andTeam_sizereturned 100+ distinct groups each and nearly hit the response size limit that broke a city-level location query. - Free-text standard fields (
company.company_location,company.company_industry, or any other standard field of typetext) — same unbounded risk as Continuous, but always relevant, so they don't get excluded by fill rate. Group by them, then roll the raw values up client-side (e.g. city → country) before presenting — never show the raw breakdown. Do not put these in an unconditional "always include" list; classify them by type like everything else.
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.
- 2d ago First seen · 182 lines · 140 tokens per session scan A 0b66fdda8455
deal-flow-analytics is a skill published in the GitHub repository carta/plugins (12 stars, last pushed today), licensed Apache-2.0. It adds 140 tokens to every session and 2,137 once invoked, about $0.0007 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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