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feat: --competitors flag for auto-discovered comparison fan-out (#308)

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Pass `--competitors` on a single-entity topic and the engine auto-discovers
2-6 peer entities via web search, runs the full pipeline on each in
parallel, and returns one N-way comparison reusing the existing 9-axis
Head-to-Head scaffold. `last30days OpenAI --competitors` resolves to
Anthropic + xAI + Google Gemini; `last30days Kanye West --competitors`
resolves to Drake + Kendrick Lamar + one more peer.

- New CLI flags: --competitors, --competitors=N, --competitors-list
- New scripts/lib/competitors.py — mirrors resolve.auto_resolve pattern
  (web search + deterministic text extraction, no internal LLM)
- New scripts/lib/fanout.py — ThreadPoolExecutor orchestrator; per-entity
  failures degrade gracefully as long as >=2 entities survive
- Multi-report render in scripts/lib/render.py reuses the comparison
  scaffold for the synthesis table
- LAW 7-style stderr when no backend and no list, pointing the hosting
  reasoning model at --competitors-list
- 38 new tests across CLI parsing, discovery, fanout, and rendering

Co-authored-by: Matt Van Horn <455140+mvanhorn@users.noreply.github.com>
Co-authored-by: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
This commit is contained in:
Matt Van Horn
2026-04-22 21:28:36 -07:00
committed by GitHub
parent ff21243517
commit 5f054380c5
13 changed files with 1611 additions and 20 deletions
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@@ -114,6 +114,10 @@ When the same story appears on Reddit, X, and YouTube, v3 merges them into one c
"CLI vs MCP" used to run three serial passes (12+ minutes). v3 runs one pass with entity-aware subqueries for both sides simultaneously. Same depth, 3 minutes.
### Auto-discovered competitor comparisons
`/last30days OpenAI --competitors` discovers the top 3 peers via web search (Anthropic, xAI, Google Gemini), runs the full pipeline on each in parallel, and returns one N-way comparison report. Override with `--competitors=N` or `--competitors-list="A,B,C"`.
### GitHub person-mode
When the topic is a person, the engine switches from keyword search to author-scoped queries. Instead of "who mentioned this name in an issue body," it answers: what are they shipping and where is it landing?