Competitive Positioning

competitive-positioning

Triggers

• Competitive positioning
• strategic group mapping
• differentiation analysis
• competitive advantage assessment
• market positioning map
• value chain positioning
• brand positioning
• cost leadership vs differentiation
• niche strategy analysis
• disruptive positioning

Defaults

| Parameter | Default Value | Rationale | |-----------|---------------|-----------| | ticker | (required) | Stock symbol to analyze | | lookback_quarters | 4 | Standard lookback for this skill type |

Methodology

1. Retrieval Scope

This skill operates with retrieval_scope: unstructured_document_search. It performs unstructured document search at scale via the three-layer retrieval protocol (Layer 1→2→2.5→3), escalating to read_source_deep_outline only when lightweight labels cannot disambiguate pages, plus structured XBRL where needed.

2. Retrieval Strategy

Follows the retrieval strategy decision tree in contracts/retrieval.md. Primary branch: (b)/(c) Unstructured Query via the three-layer protocol. Resolve the canonical ticker first (exact → fuzzy alias → share-class) before any data call.

3. Temporal Scope

Default lookback: 4 fiscal quarter(s); maximum: 10. The default balances recency against the trend window this analysis requires.

4. Tool Allowlist

Per frontmatter allowed_tools:

• search_companies — ticker resolution + company context (entity-alias fuzzy match)
• search_xbrl_facts — primary structured financial facts (is_primary default)
• search_documents — Layer 1 document discovery (page-level silver records)
• search_sec_filings — Layer 1 SEC filing metadata index
• get_company_financials — consolidated IS/BS/CF highlights
• list_coverage — universe-level coverage discovery
• get_company_profile — sector/industry classification + metadata
• read_source_outline — Layer 2 lightweight page map (description + keywords)
• read_source_deep_outline — Layer 2.5a deep page map (table_titles/drivers/metrics)
• list_xbrl_concepts — US-GAAP concept discovery for non-standard line items
• read_source_pages — Layer 3 deep read of selected pages with table markers
• search_keyword_in_source — Layer 2.5b keyword page filter for large documents

5. Protocol

1. Pre-flight (mandatory): get_company_fiscal_calendar/{ticker} then get_ticker_coverage/{ticker}; route on coverage.
2. Layer 1 — discovery: search_documents / search_sec_filings to find candidate filings by ticker/form_type/date.
3. Layer 2 — page map: read_source_outline/{ticker}/{citation_id}; skip NULL-description pages; escalate to read_source_deep_outline only when labels can't disambiguate.
4. Layer 2.5 (optional): search_keyword_in_source to narrow documents >50 pages.
5. Layer 3 — deep read: read_source_pages/{ticker}/{citation_id}?row_numbers=page,... for the 3–5 selected pages only.
6. Multi-period (if applicable): search_cross_period after fiscal-calendar resolution.
7. Output: write the deliverable per ## Output File, then append to agentii.md.

Output File

Write the final deliverable to _cross/{descriptive-slug}_{YYYY-MM-DD_HHMM}_competitive-positioning_{affix}.md or _sector/{sector_name}/{YYYY-MM-DD_HHMM}_competitive-positioning_{affix}.md .

Output Structure

1. Executive Summary (≤200 words) — headline conclusions for the analysis.
2. Data Sources — filings + structured endpoints used, with {ticker} {citation_id} page citations.
3. Analysis — the core findings, tables, and commentary for this dimension.
4. Key Metrics — the quantitative results with QoQ/YoY context where relevant.
5. Coverage Gaps & Citations — data not retrievable + citation index.

Citations & memory: follow contracts/citation-and-memory.md — ≥1 citation per 200 words; every material fact, table row, and metric is immediately followed by its inline clickable https://agentii.ai/v/{ticker}/{citation_id}/{N} link; a bottom Citations section provides a non-duplicative roll-up index; the closing TUI reply includes a compact Key Citations list (headline 5–10 facts) of clickable /v/ URLs; and append the run to agentii.md per contracts/agentii-md-schema.md.

Preflight

Run the canonical pre-flight sequence — MCP health probe, ticker resolution, workspace style.md override, memory load, and coverage check. See contracts/preflight.md.

Include the X-Agentii-Trace header on every tool call per contracts/x-agentii-trace-header.md.

Memory & Snapshot

• Memory load (pre-flight): load prior workspace context for the ticker before retrieval — see contracts/memory-load.md.
• Structured output frontmatter: emit the FR-090 block (key_metrics, conclusions, facts_count, deducted_count, views_count, citation_count) per contracts/output-frontmatter-schema.md.
• Snapshot synthesis: after writing the deliverable, update the two-tier snapshot and classify findings as [FACT]/[DEDUCTED]/[VIEW] — see contracts/snapshot-synthesis.md.
• Session archival: record the run under sessions/{YYYY-MM-DD}/ and update sessions/INDEX.md per contracts/session-format.md.

Final Summary (TUI)

End the closing chat reply with a compact Key Citations list (headline 5–10 facts), each a clickable https://agentii.ai/v/{ticker}/{citation_id}/{N} link, so the user can cmd+click straight to the exact SEC page. See contracts/citation-and-memory.md.

Error Handling

| Error | Action | |-------|--------| | Ticker not found | Suggest checking spelling or trying listcoverage | | No data available | Flag in Coverage Gaps, proceed with available data | | API key invalid | Direct user to agentii.ai/api-keys | | MCP server unreachable | Retry once; if persistent, halt with AGENTIIMCP_UNREACHABLE |

References

• Methodology: [references/methodology.md](references/methodology.md) — tool fallbacks, retrieval strategy, analysis framework
• Output Structure: [references/output-structure.md](references/output-structure.md) — detailed deliverable sections and ordering

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: agentii-ai
• Source: agentii-ai/agentii-investment-intelligence
• License: Apache-2.0
• Homepage: https://agentii.ai

Install and usage instructions live in the source repository linked above.