edge-pipeline-orchestrator
Orchestrate the full edge research pipeline from candidate detection through strategy design, review, revision, and export.
Skill by mphinance
Install
Works with Claude Code, Claude apps, Codex, Cursor, Gemini CLI, GitHub Copilot, VS Code, OpenCode, Goose, Windsurf. Read the repository before you give any tool access to your data.
npx agentmarket install skill/mphinance-edge-pipeline-orchestrator
Asks which of your tools to add it to (Claude Code, Codex, Cursor, VS Code, Windsurf, Gemini CLI, Claude Desktop) and shows the change first. See https://agentmart.dev/cli.
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About
Use cases: design, data, research
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What this skill can do and what it asks for, from its SKILL.md and code. Checked Oct 25, 2026.
- Tools
- No tools found
- Secrets
- None asked for
- Install scripts
- None found
- Runs as
- Python package
Network hosts it talks to 1
codeload.github.com· found in install command
Files it touches
~/.claude/skills/edge-pipeline-orchestrator
What's inside
- Skill 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.
- License
- MIT
- Instructions
- 125 lines in SKILL.md · view on GitHub
Before installing
- Ships executable scripts
Files (7)
- README.md
- SKILL.md
- pipeline_flow.md · references/
- revision_loop_rules.md · references/
- orchestrate_edge_pipeline.py · scripts/
- conftest.py · scripts/tests/
- test_orchestrate_edge_pipeline.py · scripts/tests/
SKILL.md (first lines)
# 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.From mphinance/alpha-skills, MIT.
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Signals
- Stars
- 29
- Forks
- 6
- Open issues
- 0
- Last commit (this folder)
- Aug 28, 2026 (2 months ago)
From GitHub (fetched Oct 24, 2026).
Details
- License
- MIT
- Repository
- github.com/mphinance/alpha-skills
- First seen
- Oct 24, 2026
- Last checked
- Oct 24, 2026
- Source
- GitHubgithub-graphql
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Cite this page
edge-pipeline-orchestrator skill. Agent Mart. https://agentmart.dev/skills/mphinance-edge-pipeline-orchestrator (last updated 2026-10-24)