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 BaggaT236/AI-Trading-Skills --skill edge-pipeline-orchestratorgit clone --depth 1 https://github.com/BaggaT236/AI-Trading-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/baggat236/ai-trading-skills/edge-pipeline-orchestrator)<a href="https://agentmods.dev/skills/baggat236/ai-trading-skills/edge-pipeline-orchestrator"><img src="https://agentmods.dev/badge/skills/baggat236/ai-trading-skills/edge-pipeline-orchestrator/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/baggat236/ai-trading-skills/edge-pipeline-orchestrator"><img src="https://agentmods.dev/badge/skills/baggat236/ai-trading-skills/edge-pipeline-orchestrator.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00041 | $0.01059 |
| Opus 5 | $0.00020 | $0.00530 |
| Sonnet 5 | $0.00008 | $0.00212 |
| Haiku 4.5 | $0.00004 | $0.00106 |
Grade A, and why
edge-pipeline-orchestrator 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 13d 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.
This is a copy
100% identical to edge-pipeline-orchestrator — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 129 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Edge Pipeline Orchestrator
Coordinate all edge research stages into a single automated pipeline run.
When to Use
- Run the full edge pipeline from tickets (or OHLCV) to exported strategies
- Resume a partially completed pipeline from the drafts stage
- Review and revise existing strategy drafts with feedback loop
- Dry-run the pipeline to preview results without exporting
Workflow
- Load pipeline configuration from CLI arguments
- Run auto_detect stage if --from-ohlcv is provided (generates tickets from raw OHLCV data)
- Run hints stage to extract edge hints from market summary and anomalies
- Run concepts stage to synthesize abstract edge concepts from tickets and hints
- Run drafts stage to design strategy drafts from concepts
- Run review-revision feedback loop:
- Review all drafts (max 2 iterations)
- PASS verdicts accumulated; REJECT verdicts accumulated
- REVISE verdicts trigger apply_revisions and re-review
- Remaining REVISE after max iterations downgraded to research_probe
- Export eligible drafts (PASS + export_ready_v1 + exportable entry_family)
- Write pipeline_run_manifest.json with full execution trace
CLI Usage
# Full pipeline from tickets
python3 scripts/orchestrate_edge_pipeline.py \
--tickets-dir path/to/tickets/ \
--output-dir reports/edge_pipeline/
# Full pipeline from OHLCV
python3 scripts/orchestrate_edge_pipeline.py \
--from-ohlcv path/to/ohlcv.csv \
--output-dir reports/edge_pipeline/
# Resume from drafts stage
python3 scripts/orchestrate_edge_pipeline.py \
--resume-from drafts \
--drafts-dir path/to/drafts/ \
--output-dir reports/edge_pipeline/
# Review-only mode
python3 scripts/orchestrate_edge_pipeline.py \
--review-only \
--drafts-dir path/to/drafts/ \
--output-dir reports/edge_pipeline/
# Dry run (no export)
python3 scripts/orchestrate_edge_pipeline.py \
--tickets-dir path/to/tickets/ \
--output-dir reports/edge_pipeline/ \
--dry-run
Output
All artifacts are written to --output-dir:
What ships with it
5 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.
- 13d ago First seen · 129 lines · 41 tokens per session scan A 1cdf9aab9809
edge-pipeline-orchestrator is a skill published in the GitHub repository BaggaT236/AI-Trading-Skills (121 stars, last pushed 9d ago), licensed MIT. It adds 41 tokens to every session and 1,059 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to edge-pipeline-orchestrator, differing in 0 lines, and is treated as a copy.
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