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 JoelLewis/finance_skills --skill stp-automationgit clone --depth 1 https://github.com/JoelLewis/finance_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/joellewis/finance_skills/stp-automation)<a href="https://agentmods.dev/skills/joellewis/finance_skills/stp-automation"><img src="https://agentmods.dev/badge/skills/joellewis/finance_skills/stp-automation/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/joellewis/finance_skills/stp-automation"><img src="https://agentmods.dev/badge/skills/joellewis/finance_skills/stp-automation.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk pass
- NVIDIA SkillSpector pass
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 | $0.00118 | $0.05845 |
| Opus 5.5 | $0.00047 | $0.02338 |
| Sonnet 5.5 | $0.00024 | $0.01169 |
| Haiku 4.5 | $0.00012 | $0.00585 |
Grade A, and why
stp-automation 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 1mo 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 — 261 lines — stays where its author put it; the contents beside it link to each section on GitHub.
STP & Automation
Core Concepts
1. STP Fundamentals
Straight-through processing is the end-to-end automated completion of a business process without manual intervention: a transaction enters the system at one end and exits as a completed, booked, and confirmed event at the other with no human touching it along the way. True STP means zero manual intervention for the happy path; a process with a human review or approval at a midpoint is partially automated, not STP.
STP rate calculation. The fundamental metric is:
STP Rate = Automated Completions / Total Volume * 100
An "automated completion" is a transaction or workflow instance that passed through every step without manual intervention. If a trade requires a human to confirm a counterparty identifier before it can settle, that trade is not STP even though every other step was automated. The denominator is total volume, including both automated and exception items.
Industry benchmarks by process type. STP rates vary significantly by domain and firm maturity:
- Equity trade processing (listed, domestic): 90-98% for mature firms
- Fixed income trade processing: 70-85% (lower due to less standardized identifiers and settlement conventions)
- Account opening (simple individual/joint accounts): 60-80%
- Account opening (complex entity/trust accounts): 20-40%
- Corporate actions (mandatory events): 80-90%
- Corporate actions (voluntary events): 30-50%
- Reconciliation (position and cash): 85-95% auto-match rates
- Settlement instruction matching: 75-90%
These ranges reflect the spectrum from mid-tier broker-dealers to large custodian banks. A firm's position within the range depends on data quality, system integration maturity, and the complexity of its product mix.
The business case for STP compounds across four dimensions: cost reduction (per-transaction labor cost becomes fixed infrastructure cost), speed (seconds instead of queuing and handoff delays — directly reducing settlement risk), error reduction (consistent rules instead of re-keying and judgment variance), and scalability (volume spikes at quarter-end or corporate action clusters do not require proportional staffing).
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.
- 1mo ago First seen · 261 lines · 118 tokens per session scan A 251610f223ec
stp-automation is a skill published in the GitHub repository JoelLewis/finance_skills (218 stars, last pushed 3mo ago), licensed MIT. It adds 118 tokens to every session and 5,845 once invoked, about $0.0005 per session on Opus 5.5. 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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