edge-pipeline-orchestrator

What the source does

• FilesystemNone observedNo reads or writes in the source
• NetworkNone observedNo outbound calls in the source
• ShellNone observedNo commands run in the source
• DependenciesNot pinnedSome versions float
• SecretsNone foundNo keys in source

Five audit layers

Gates applied: no behavioural pass.

examples/daily-market-dashboard/.claude/skills

PyYAML

Audit 0.4.1 · source sha c91a4ba0d6ec · 31 Oct 2026Report an issue or request a re-scan

• Publishertradermonty GitHub userOwner of the repository this skill is read from
• Source foldertradermonty/claude-trading-skills/skills/edge-pipeline-orchestratorThe exact folder we audited
• LicenceMITAs the repository states it
• Commit pinned for this verdictc91a4ba0d6ecA new audit runs when the source changes
• Repository statusNot archivedLast push 4 days ago
• Sandboxed runNot runSkills are read, not executed; layer L4 is skipped

Its 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.

---
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

Excerpt, first 4,291 characters of SKILL.md, verbatim from the audited commit. It is text from someone else’s repository, quoted so you can judge it before your agent reads it.

1. 2026-10-31
   ✓SAFE B 89 / 100

   first audit · source c91a4ba0d6ec

Questions about edge-pipeline-orchestrator

• github.com/tradermonty/claude-trading-skills/tree/main/skills/ed — the audited source at commit c91a4ba0d6ec and every finding aboveObserved · 31 Oct 2026

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