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edge-pipeline-orchestrator — Claude Code skill: setup

Runs edge research from candidate detection through strategy review and export. Use it when several market-data stages need one coordinated workflow.

Install into Claude Code · Skills CLI (Vercel) All methods and platforms ↓
npx skills add tradermonty/claude-trading-skills --skill edge-pipeline-orchestrator -a claude-code
Open on GitHub

What edge-pipeline-orchestrator does

This skill connects the research stages into one run. It can start with tickets or OHLCV, extract hints, synthesize edge concepts, design strategy drafts, and send them through a review loop. PASS and REJECT verdicts accumulate; REVISE triggers changes and another review for up to two iterations.

The scripts/orchestrate_edge_pipeline.py script supports a full run, resuming from drafts, review-only mode, and a dry run without export. Artifacts go to --output-dir, including the execution manifest, YAML files for tickets, hints, concepts, and drafts, review results, and strategy directories. The workflow is invoked with python3.

In the LLM-assisted flow, Claude Code analyzes market_summary.json and anomalies.json, then the orchestrator reads ideas through --llm-ideas-file and --promote-hints. Check the selected mode carefully: --resume-from drafts and --review-only skip the hints and concepts stages, so those idea flags are ignored.

What it helps with

  • run research from OHLCV to exported strategies
  • resume processing from the drafts stage
  • send strategy drafts through review and revision
  • preview a pipeline run without exporting
  • store eligible strategies with a run manifest

Example prompts for the agent

  • Run the full ticket pipeline with python3 scripts/orchestrate_edge_pipeline.py --tickets-dir path/to/tickets/ --output-dir reports/edge_pipeline/.
  • Build tickets from path/to/ohlcv.csv and write the results to reports/edge_pipeline/.
  • Review the drafts in path/to/drafts/ only, then run the pipeline as a dry run without exporting.

How to install edge-pipeline-orchestrator

1Skills CLI (Vercel)

npx skills add tradermonty/claude-trading-skills --skill edge-pipeline-orchestrator -a claude-code

2Manually: put the files in place

~/.claude/skills/edge-pipeline-orchestrator/SKILL.md

Copy the whole skill folder; for one project use .claude/skills/edge-pipeline-orchestrator/

Step by step, with commands for each platform: How to install skills in Claude Code: five ways

Questions about edge-pipeline-orchestrator

How do I install edge-pipeline-orchestrator in Claude Code?

Run npx skills add tradermonty/claude-trading-skills --skill edge-pipeline-orchestrator -a claude-code, then run the same command once more. The installed skill is located at ~/.claude/skills/edge-pipeline-orchestrator/SKILL.md.

What does edge-pipeline-orchestrator do?

It coordinates the stages from candidate detection and hints through concepts, strategy drafts, review, revision, and export of eligible strategies. Each run writes pipeline_run_manifest.json.

What happens with --resume-from drafts?

The pipeline continues from the drafts stage and skips hints and concepts. As a result, --llm-ideas-file and --promote-hints do not apply in this mode.

Source text

SKILL.md · MIT
---
name: edge-pipeline-orchestrator
description: Orchestrate the full edge research pipeline from candidate detection through strategy design, review, revision, and export. Use when coordinating multi-stage edge research workflows end-to-end.
---

# 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

1. Load pipeline configuration from CLI arguments
2. Run auto_detect stage if --from-ohlcv is provided (generates tickets from raw OHLCV data)
3. Run hints stage to extract edge hints from market summary and anomalies
4. Run concepts stage to synthesize abstract edge concepts from tickets and hints
5. Run drafts stage to design strategy drafts from concepts
6. 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
7. Export eligible drafts (PASS + export_ready_v1 + exportable entry_family)
8. Write pipeline_run_manifest.json with full execution trace

## CLI Usage

```bash
# 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`:

```
output-dir/
├── pipeline_run_manifest.json
├── tickets/          (from auto_detect)
├── hints/hints.yaml  (from hints)
├── concepts/edge_concepts.yaml
├── drafts/*.yaml
├── exportable_tickets/*.yaml
├── reviews_iter_0/*.yaml
├── reviews_iter_1/*.yaml  (if needed)
└── strategies/<candidate_id>/
    ├── strategy.yaml
    └── metadata.json
```

## Claude Code LLM-Augmented Workflow

Run the LLM-augmented pipeline entirely within Claude Code:

1. Run auto_detect to produce `market_summary.json` + `anomalies.json`
2. Claude Code analyzes data and generates edge hints
3. Save hints to a YAML file:

```yaml
- title: Sector rotation into industrials
  observation: Tech underperforming while industrials show relative strength
  symbols: [CAT, DE, GE]
  regime_bias: Neutral
  mechanism_tag: flow
  preferred_entry_family: pivot_breakout
  hypothesis_type: sector_x_stock
```

4. Run orchestrator with `--llm-ideas-file` and `--promote-hints`:

```bash
python3 scripts/orchestrate_edge_pipeline.py \
  --tickets-dir path/to/tickets/ \
  --llm-ideas-file llm_hints.yaml \
  --promote-hints \
  --as-of 2026-02-28 \
  --max-synthetic-ratio 1.5 \
  --strict-export \
  --output-dir reports/edge_pipeline/
```

### Optional Flags

- `--as-of YYYY-MM-DD` — forwarded to hints stage for date filtering
- `--strict-export` — export-eligible drafts with any warn finding get REVISE instead of PASS
- `--max-synthetic-ratio N` — cap synthetic tickets to N × real ticket count (floor: 3)
- `--overlap-threshold F` — condition overlap threshold for concept deduplication (default: 0.75)
- `--no-dedup` — disable concept deduplication

Note: `--llm-ideas-file` and `--promote-hints` are effective only during full pipeline runs.
`--resume-from drafts` and `--review-only` skip hints/concepts stages, so these flags are ignored.

## Resources

- `references/pipeline_flow.md` — Pipeline stages, data contracts, and architecture
- `references/revision_loop_rules.md` — Review-revision feedback loop rules and heuristics

© tradermonty · MIT · tradermonty/claude-trading-skills

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