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Supply chain

Skill agentii-ai/agentii-investment-intelligence/plugins/vertical-plugins/industry-analysis/skills/agentii/supply-chain

Supply chain mapping, supplier dependency analysis, customer concentration, geographic concentration, bottleneck identification, supply chain risk, logistics network, sourcing strategy, inventory management, vertical integration analysisFrom its SKILL.md

Install
npx -y skills add agentii-ai/agentii-investment-intelligence --skill supply-chain

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SKILL.md

6.7 KB, ~1.4k tokens by cl100k_base, as published. Nobody here has run it

supply-chain

Triggers

  • Supply chain mapping
  • supplier dependency analysis
  • customer concentration
  • geographic concentration
  • bottleneck identification
  • supply chain risk
  • logistics network
  • sourcing strategy
  • inventory management
  • vertical integration analysis

Defaults

ParameterDefault ValueRationale
ticker(required)Stock symbol to analyze
lookback_quarters4Standard 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
  • 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<N>,... 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}_supply-chain_{affix}.md or _sector/{sector_name}/{YYYY-MM-DD_HHMM}_supply-chain_{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<N> 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

ErrorAction
Ticker not foundSuggest checking spelling or trying list_coverage
No data availableFlag in Coverage Gaps, proceed with available data
API key invalidDirect user to agentii.ai/api-keys
MCP server unreachableRetry once; if persistent, halt with AGENTII_MCP_UNREACHABLE

References

What ships with it: 2 files

2.0 KB alongside SKILL.md

references/

Gives 0 of the 12 instructions most operations skills give in ~1.4k tokens

Counted across 483 of the 484 authors here whose files we hold, read 2026-08-07

  • Collect monitoring data throughout the simulationin 14 of 483, across 6 files
  • Set the random seed for reproducibilityin 14 of 483, across 6 files
  • Validate simulations against analytical solutionsin 12 of 483, across 4 files
  • Clarify goals, constraints, and inputsin 11 of 483, across 2 files
  • Implement contract tests for integration pointsin 11 of 483, across 2 files
  • Implement strangler fig infrastructure with API gatewayin 11 of 483, across 2 files
  • Audit modernized components for security vulnerabilitiesin 11 of 483, across 2 files
  • Avoid Python blocking calls in processesin 10 of 483, across 3 files
  • Use resource context managers for automatic cleanupin 9 of 483, across 2 files
  • Maintain consistent time unitsin 9 of 483, across 2 files
  • Validate outcomes against success criteriain 8 of 483, across 1 file
  • Analyze the legacy codebase for technical debtin 8 of 483, across 1 file

Said here and by no other author read

  • resolve the canonical ticker first
  • load prior workspace context for the ticker
  • include the X-Agentii-Trace header on every tool call
  • run the mandatory pre-flight sequence and route on coverage
  • discover candidate filings via layer 1 search
  • read the layer 2 page map and skip null-description pages

Grouped from the skills themselves: near-identical wordings counted once, and counted by distinct author, so one author publishing three of these counts once. Length counted with cl100k_base; the agent that loads this file may tokenize it differently.

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