meta-competitive-intel

meta-competitive-intel

Connector / competitive-intel meta-skill. Monitors one to a handful of target companies across pricing, product, leadership, hiring, and news dimensions; extracts grounded signals; classifies a verdict; produces a brief with concrete actions. Always read-only — never sends emails, posts, or modifies tracker data.

Composition philosophy — multi-skill bundled orchestration

This meta-skill uses only OpenSquilla-bundled atomic skills plus the five built-in step kinds — no external dependencies. The DAG calls into 5 distinct bundled atomic skills:

Skill	Step(s)	Role in the DAG
multi-search-engine	web_research, web_research_target_1..3, web_research_retry	Primary, target-level, and fallback research source, fed by LLM-generated short search queries rather than raw target grids
deep-research	deep_dive	Extra rounds for SINGLE_DEEP mode only
memory	recall_baseline, store_brief	Cross-session continuity. recall_baseline pulls the last brief from durable memory automatically so baseline_diff works even when the user didn't paste a baseline; store_brief writes this run for next time. This pair effectively delivers what the proposed state: primitive would give us.
xlsx	signals_xlsx	When the user explicitly asks for a spreadsheet / xlsx / download / export, export the signal table + baseline diff as a workbook
docx	export_docx	Optional final DOCX export

Step kinds used: llm_chat, llm_classify, user_input, skill_exec, agent.

Before search, search_strategy uses the LLM to translate structured intel context into compact SEARCH_QUERY lines with target aliases and product names. The flow then adds focused target-specific searches for the first three monitored targets, so a broad competitive-intel request does not depend on one generic multi-company result. This keeps generalized inputs such as Chinese company names, baseline-diff requests, and "all dimensions" replies from becoming brittle YAML-like search strings.

Direct URL fetching of competitor pages is intentionally out of scope for this skill — the search results from web_research are normally enough, and adding URL-scrape would require a compliance-aware fetcher not currently bundled. If the user wants page-level detail, they can paste the page content into the next turn.

Persistence to Notion / external knowledge bases is also out of scope: the deliverable is the markdown emitted by deliver_intel_brief; the user copies it wherever they want.

Mode design

Four depth labels via llm_classify: depth:

• SINGLE_DEEP — one target, multi-round deep-research, full signal table, full actions. Best for "tell me everything about $competitor's last quarter."
• MULTI_QUICK — 2-5 accounts, scan-level coverage, top-10 signal table. Best for "what did these 5 do this month?"
• DIFF_VS_BASELINE — baseline text was pasted, the run leads with the diff section. Best for "what's new since I last looked?"
• EXEC_BRIEF — 5-bullet executive summary + verdict + top 3 actions, nothing else. Best for "give me something to forward to the CEO."

Honest limitations (first-wave)

• No state: primitive. Baselines are pasted each turn; the proposed state: would persist last run's signals automatically.
• No foreach. The first three targets get focused search lanes; beyond that, remaining targets share the broad query context. With foreach, each target would get its own isolated step + audit trail per target.
• No alerting. The skill produces a brief on demand; it doesn't push notifications. A future combination with cron (bundled) + the proposed event_trigger primitive would give a "monitor this every Monday" mode.