LangAlpha is an agent workspace for researching financial markets and supporting investment decisions through persistent research, news analysis, and parallel subagents. It is for investors who want to develop and update trading theses over time, including generating long-short pair-trade ideas. The catalogue entries provide the skills, instructions, MCP servers, and plugin that make up its agent workflow.
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/ginlix-ai/langalpha/run-workflownpx skills add ginlix-ai/LangAlpha --skill run-workflowgit clone --depth 1 https://github.com/ginlix-ai/LangAlphaWrote 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/ginlix-ai/langalpha/run-workflow)<a href="https://agentmods.dev/skills/ginlix-ai/langalpha/run-workflow"><img src="https://agentmods.dev/badge/skills/ginlix-ai/langalpha/run-workflow.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 | $0.00051 | $0.01648 |
| Opus 5 | $0.00026 | $0.00824 |
| Sonnet 5 | $0.00010 | $0.00330 |
| Haiku 4.5 | $0.00005 | $0.00165 |
Grade A, and why
run-workflow 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 — 87 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Programmatic Workflows (RunWorkflow)
Use RunWorkflow when a deterministic pipeline should orchestrate multiple subagents — fan-out research then synthesize, classify then act per item, generate then verify. Prefer it over issuing many Task calls yourself when the dispatches are data-driven (one per ticker, per filing, per finding). Do NOT use it for a single subagent (use Task) or for code that dispatches nothing (use ExecuteCode).
The script
You write JavaScript (ES2020). It executes server-side: the script itself cannot touch the workspace filesystem — the subagents it dispatches can. The script must declare a pure object literal first:
export const meta = { name: 'ticker-briefs', description: 'Fan out research, synthesize' }
name (letters, digits, -, _) and description are required; no variables or function calls inside the literal. The rest of the body is free-form async JS — top-level await and return both work, and the return value (JSON-serializable) becomes the run result. Return a synthesis rather than the raw children: a large result is clipped for display, and a clipped object is unparseable.
Built-ins
await agent(prompt, opts?)— dispatch one subagent, resolve to its result text. The child starts blank: it sees nothing of this conversation, of the script, or of its sibling children, so the prompt must carry everything it needs — and its final text is the whole of what comes back.opts:agentType(default'general-purpose'; same types asTask),label(display name),phase(progress group),schema(JSON Schema — the child answers as matching JSON and the resolved value is the parsed object, ornullif it cannot).await pipeline(items, ...stages)— the default for multi-stage work. Each item flows through every stage independently, with NO barrier between stages: item A can be in stage 3 while item B is still in stage 1, so the run costs the slowest single chain rather than the sum of each stage's slowest item. Each stage receives(prevResult, originalItem, index); a throwing stage nulls that item and skips its remaining stages.await parallel(thunks)— run an array of() => Promisethunks concurrently, resolving to results in order; already-started promises (parallel([agent(...), ...])) work too. Use it for a single fan-out, or where the next step genuinely needs the whole set at once — dedup across all results, an early exit when the count is zero, one child weighing the others. Needing tomap/filterbetween stages is not such a case: do that inside a pipeline stage.phase(title)/log(message)— progress markers streamed live to the user.args— theparamsvalue passed toRunWorkflow, verbatim.
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 · 87 lines · 51 tokens per session scan A f8936a525a2f
run-workflow is a skill published in the GitHub repository ginlix-ai/LangAlpha (1,722 stars, last pushed today), licensed Apache-2.0. It adds 51 tokens to every session and 1,648 once invoked, about $0.0003 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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