faostat-analytical-brief

name

faostat-analytical-brief

description

Use when the user wants a short, FAOSTAT-style analytical brief — a multi-page PDF policy document in the FAOSTAT house visual style, with a HIGHLIGHTS box, global/regional/country sections, numbered figures with source lines, explanatory notes, and a data appendix. Use when the user asks for "an analytical brief on X", "a FAOSTAT-style brief", "a policy brief", "a FAO-style write-up", or a short policymaker-facing report. Keywords — analytical brief, policy brief, FAOSTAT brief, FAO-style, brief, briefing (policy), policymakers, short report (policy). Do NOT use for journalistic long-reads (use `faostat-story`), academic papers (forthcoming `faostat-scientific-paper`), or single-page visual summaries (forthcoming `faostat-infographic`).

FAOSTAT Analytical Brief

Produce a multi-page PDF analytical brief in the FAOSTAT house visual style, with the corresponding multi-sheet xlsx data appendix so every figure and discussion point is reproducible.

Scope of the visual grammar (no FAO branding). The brief uses the FAOSTAT visual grammar — teal/dark-blue palette, serif titles, → arrow bullets, teal section banners, numbered figures with Source: FAOSTAT … lines, and the familiar section order (HIGHLIGHTS / BACKGROUND / GLOBAL / REGIONAL / COUNTRY / EXPLANATORY NOTES / NEXT RELEASE / REFERENCES). It does not reproduce the FAO logo, "Food and Agriculture Organization of the United Nations" masthead, ISSN strip, "FAO Statistics Division" stamp, "Required citation: FAO. …" line, or any other FAO branding. The output must be clearly attributable as a user-generated analysis, not an FAO publication.

Prerequisites

Confirm the FAOSTAT MCP tools are available: faostat_get_data, faostat_search_codes, faostat_list_domains, faostat_get_metadata. If they are not, tell the user and stop.

Python packages (install on demand via pip install <pkg> --break-system-packages):

• plotly, pandas, kaleido (figure rendering + PNG export, reused from faostat-map)
• reportlab (PDF assembly via Platypus)
• openpyxl (xlsx data appendix)

The forthcoming figure renders compose on top of faostat-viz (line/bar/stacked-bar) and faostat-map (choropleths). When a brief needs a map, invoke the map skill's rendering guidance rather than reimplementing.

Cross-skill invariants (do not violate)

All 6 invariants from the FAOSTAT skill suite apply unchanged:

1. FILTER vs DISPLAY element codes. faostat_get_data takes FILTER codes (e.g., '2510'); never pass DISPLAY codes ('5510').
2. Year syntax. Comma-separated lists only ('2010,2011,...,2024'); colon ranges return empty.
3. Element filter required on every faostat_get_data call.
4. TCL for national trade aggregates, TM only for partner breakdowns. Never sum TM rows to reconstruct national totals.
5. China composite default (user preference, Apr 2026). Default to composite China (area 351) for country rankings, top-N lists and country-level analysis. Offer China, mainland (41) as an opt-in — do not substitute 41 unless the user asks for 41 explicitly. Flag the choice in the Methodology sheet, and include the caveat whenever a China number is quoted: FAOSTAT's own publications default to 41, so a brief using 351 will show a marginally larger "China" value than the FAO data-portal default. (Map figures inside the brief are an exception: they use the disaggregation path — area 41 on the CHN polygon with HKG / MAC / TWN rendered separately — because choropleths cannot sensibly render 351. See the map skill.)
6. faostat_get_rankings HTTP-500 fallback. Reconstruct rankings from faostat_get_data + client-side sort; document fallback in the Methodology sheet.
7. Element and item code resolution. Never use a hardcoded numeric element or item code as the primary value in a faostat_get_data call. Always resolve at runtime: faostat_search_codes(domain_code='<dom>', dimension_id='element', query='<metric name>') for elements; faostat_search_codes(domain_code='<dom>', dimension_id='item', query='<item name>') for items. Numeric codes shown in reference tables and code examples are verified hints — use them to validate the search result, not as the authoritative source. Domain letter-codes (QCL, TCL, GT, EM, FBS, FS…) are stable and may be used directly.

Workflow

Step 1 — Gather parameters

Confirm or elicit from the user:

• Topic/domain — map to a FAOSTAT domain (QCL, TCL, GT, ET, PP, FBS, FS, RFN, RL, etc.).
• Scope — global (default, matching most reference briefs), regional, or a named country subset.
• Time window — a year range. Reference briefs use 15–25-year windows; default to the most recent 15 years if the user gives nothing.
• Thematic breakdown — optional. Production briefs split by commodity group; emissions briefs split by component (farm-gate / land-use / pre-and-post); trade briefs split by dimension (volume / value / concentration); climate briefs split by temporal slice. Pick a breakdown from the domain or ask the user.
• Cover image — optional user-supplied path. Default to a generic abstract teal gradient placeholder.
• Brief number — optional; omit by default (no number in cover kicker).
• HTML sibling — off by default; on request only.

Step 2 — Plan the brief

Produce an internal outline before any data pulls:

• 5–8 HIGHLIGHTS bullets, each with a projected hard number placeholder.
• 4–7 numbered figures. Pattern:
  • Figure 1 — GLOBAL time series of the headline metric.
  • Figure 2 — REGIONAL breakdown (stacked bar or small multiples across 6 FAO regions).
  • Figure 3 — THEMATIC breakdown (by commodity / component / dimension per the domain).
  • Figure 4 — COUNTRY / TOP-10 bar chart.
  • Figure 5 (optional) — Choropleth map. Use faostat-map.
  • Figures 6–7 (optional) — additional regional or indicator figures.
• 1–2 tables where a cross-region / cross-indicator comparison is cleaner than a chart.

Step 3 — Pull all data

For each figure and table, plan a specific faostat_get_data call. Log every call for the Methodology sheet. Always:

• Pass FILTER element codes.
• Use comma-separated year lists.
• Use response_format='compact'.
• Pass show_unit=True.
• For trade aggregates, use TCL elements — not TM sums. Resolve element codes at runtime: e.g. faostat_search_codes(domain_code='TCL', dimension_id='element', query='import quantity') → e.g. 2610; similarly for export quantity (~2910), import value (~2612), export value (~2912). Treat any numeric code here as a verified hint to validate against, not a value to pass directly.
• For region figures, pull the 6 continental area codes (Africa 5100, Americas 5200, Asia 5300, Europe 5400, Oceania 5500, World 5000) directly — don't compute them client-side.
• For top-N country figures, don't rely on faostat_get_rankings; pull faostat_get_data for the domain and element (no area filter) and sort client-side, so an HTTP-500 from get_rankings doesn't block the brief.
• For China in country rankings and country-level analysis, keep composite China (area 351) and drop 41 (mainland), unless the user opted into 41 explicitly. (Map figures inside the brief use the disaggregation path — area 41 + HKG 96 + MAC 128 + TWN 214 — because a choropleth cannot render 351 sensibly.)

Pull the dataset metadata with faostat_get_metadata(domain_code=<D>) so EXPLANATORY NOTES can be auto-generated from the same source the FAOSTAT team uses.

Step 4 — Compute derived metrics

In pandas, compute and store:

• Period totals and averages.
• Year-over-year absolute and percentage changes.
• CAGR over the full window.
• Shares (country share of regional total, region share of world total).
• Rankings (top-10 by absolute value, by change, by intensity).

Keep the raw rows and the derived columns side-by-side for the xlsx appendix.

Step 5 — Render figures as PNG

Use Plotly Express or Graph Objects with kaleido for static PNG export. Target:

• 2000×1200 px at scale=2 for line/bar/stacked-bar charts.
• 2400×1400 px at scale=2 for choropleth maps (via faostat-map).
• Colour palette: FAOSTAT house colours — teal #1F5E7B, dark blue #1A3A5F, warm grey #7F7F7F, accent ochre #D89B2E, accent green #4F8A3B.
• Sans-serif font for axis labels (Lato / Open Sans if available, else default sans).
• Source line embedded in the figure body: Source: FAO. YYYY. FAOSTAT: [Dataset name]. [Accessed MMM YYYY]. https://www.fao.org/faostat/en/#data/[DOMAIN]. Licence: CC-BY-4.0.

Save PNGs under <outputs>/figures/fig<N>_<slug>.png.

Step 6 — Assemble the PDF

Use ReportLab Platypus. Build a BaseDocTemplate with two page templates:

Cover page template — no FAO branding. Elements, top to bottom:

• Hero image (user-supplied or generic teal gradient). ~55% of page height.
• Kicker FAOSTAT ANALYTICAL BRIEF in small caps, teal, sans-serif (serial number omitted unless user supplied).
• Title in dark-blue serif (26pt), centred.
• Subtitle (year range + scope) in grey serif (14pt), centred.
• Teal footer band with bar-chart motif (drawn as canvas primitives — small rectangles of varying heights).
• No FAO logo. No "Food and Agriculture Organization of the United Nations" masthead. No ISSN. No "FAO Statistics Division" stamp.

Inside page template — teal header band with:

• Brief title + subtitle + (optional brief number) in white sans-serif.
• Page content area with 2.5cm left/right margins.
• Footer: page number only. No institutional branding.

Body flow:

1. HIGHLIGHTS box on page 2:
   • "HIGHLIGHTS" caps heading in teal.
   • 5–8 bullets each prefixed with →, each carrying a hard number. Use <b> tags for the number. Render via Platypus Paragraph with custom bullet style.
2. FAOSTAT [DOMAIN NAME] teal all-caps banner.
3. BACKGROUND — 2–3 paragraphs of framing and policy context.
4. GLOBAL — narrative + Figure 1.
5. REGIONAL — narrative + Figure 2 (optionally Table 1 with regional indicators).
6. Thematic subsections (commodity / component / dimension) — each with its own figure.
7. COUNTRY — top-N narrative + Figure 4 (horizontal bar).
8. Optional map figure via faostat-map.
9. EXPLANATORY NOTES — auto-generated from faostat_get_metadata covering the dataset scope, update cycle, coverage caveats, aggregation rules.
10. NEXT RELEASE — single sentence with the next expected release window per FAOSTAT's update cadence.
11. REFERENCES — DOI-linked bibliography.

Figure rendering in Platypus:

story.append(Paragraph(f"<b>Figure {n}:</b> {caption}", fig_caption_style))
story.append(Image(png_path, width=16*cm, height=9*cm))
story.append(Paragraph(source_line, fig_source_style))

Typography:

• Titles: serif, dark blue (#1A3A5F).
• Section banners: sans-serif, teal (#1F5E7B), all-caps.
• Body paragraphs: serif (Cambria / Georgia fallback), 10.5pt.
• HIGHLIGHTS bullets: sans-serif (Lato / Open Sans / default), 10pt, → bullet marker.
• Figure caption: sans-serif bold, 9.5pt.
• Figure source: sans-serif italic, 8pt, grey.

Subscripts. Never use Unicode subscripts (CO₂). Use Platypus XML markup: CO<sub>2</sub>.

Step 7 — Build the xlsx data appendix (required)

Use openpyxl to write a workbook at <outputs>/<brief-slug>-data.xlsx with the following sheets. Every figure and every numeric claim in HIGHLIGHTS must trace back to a sheet row.

Required sheets:

1. README — brief title, pull date, scope, list of FAOSTAT domains/elements/years used, sheet index (sheet name + contents summary).
2. Highlights_data — one row per HIGHLIGHTS bullet, columns: bullet_text, value, unit, source_sheet, source_row. This is the audit trail so any reviewer can check every headline number.
3. Fig<N>_<slug> — one sheet per figure. Columns should match what was plotted (long form with area, year, element, item, value, unit as appropriate). Include any derived columns (CAGR, Δ, share) next to the raw ones.
4. Tab<N>_<slug> — one sheet per in-brief table, same rows as rendered.
5. Methodology — one row per faostat_get_data call, columns: call_id, domain, element_filter_code, item_codes, area_codes, year_list, response_format, timestamp_utc, notes (e.g., "China composite 351 used for country-level figures; area 41 dropped"; "map figure disaggregated China: 41 + HKG 96 + MAC 128 + TWN 214"; "get_rankings fallback via client-side sort on get_data").
6. Sources — bibliography. Columns: id, citation, url, licence.

Formatting conventions:

• Header row bold, teal fill (#1F5E7B, white text).
• Numeric columns right-aligned; text left-aligned.
• Freeze the first row.
• Auto-fit column widths (or set to a sensible default and wrap long text).
• Number format for percentages (0.0%), integers with thousands separator (#,##0), decimals where native.

Link the xlsx from the brief footer: Data appendix: <brief-slug>-data.xlsx.

Step 8 — Write the back matter (no FAO impersonation)

Replace every FAO-branded element with neutral author-and-source attribution:

• Authorship line: Prepared by [user name / organisation] using FAOSTAT data.
• FAO legal disclaimer: OMIT. Instead include a short note: "This analysis is based on FAOSTAT data, released under the CC-BY-4.0 licence. Any interpretation, emphasis, or conclusion is the author's and does not represent an FAO position."
• Required citation: Suggested citation: [author], [year]. [Brief title]. Analytical brief based on FAOSTAT data, accessed [MMM YYYY]. — no "Required citation: FAO. …" line.
• Cover photo credit: Cover image: [source] — if using a stock or generic image, say so. Never use © FAO or FAO/<photographer>.
• No ISSN. No FAO contact footer. No FAO-catalogue code.
• Data-licence footer: Underlying data © FAO, used under CC-BY-4.0. https://creativecommons.org/licenses/by/4.0/.

Step 9 — Save and describe

Save outputs to the session workspace:

• <outputs>/<brief-slug>.pdf (primary)
• <outputs>/<brief-slug>-data.xlsx (required appendix)
• <outputs>/figures/fig<N>_<slug>.png (individual PNG figures)
• <outputs>/<brief-slug>.html (optional HTML sibling, if requested)

Describe to the user:

• The headline numbers (repeating the HIGHLIGHTS bullets).
• Any sparse-data or coverage notes surfaced during the pulls.
• Any invariant that mattered (e.g., "TCL used for trade aggregates; TM sums would have double-counted re-exports", "China composite 351 used for country rankings; 41 available as opt-in").
• Direct links to the PDF and the xlsx.

Step 10 — Offer refinements

Ask:

• "Want a different year window or scope?"
• "Should I add a country-level map?" (invokes faostat-map).
• "Any highlights you'd like emphasised or deprioritised?"
• "Replace the cover image?" (user can supply a file path).
• "Generate the HTML sibling?"

Error handling

• Empty data pull. First check FILTER vs DISPLAY (invariant 1), then year syntax (invariant 2), then element exists in the domain.
• Sparse region years. If a region has <80% year-coverage in the window, note it in the REGIONAL narrative and in the Methodology sheet rather than silently interpolating.
• faostat_get_rankings HTTP 500. Use the faostat_get_data fallback (invariant 6). Log the fallback on the Methodology sheet.
• Kaleido install fails. The skill cannot produce the PDF without PNG renders. Install kaleido on demand and retry; if install still fails, fall back to the HTML sibling and tell the user the PDF path requires kaleido.
• ReportLab font missing. If the preferred serif (Cambria / Georgia) or sans-serif (Lato / Open Sans) isn't available in the sandbox, fall back to ReportLab's built-in Times-Roman / Helvetica without failing. Note the font fallback in the Methodology sheet.
• User asked for content implying FAO authorship. If the user asks to put FAO logos, "Food and Agriculture Organization" masthead, or "Required citation: FAO. …" in the brief, push back: the skill produces FAOSTAT-style analyses for user authorship, not FAO-branded documents. Offer to proceed without the FAO branding.

Limitations (v1)

• Cover-image generation is a placeholder gradient unless the user supplies a path.
• Typography is limited to ReportLab's built-in fonts and whatever TrueType fonts are available in the sandbox.
• HTML sibling is v2 — v1 ships PDF + xlsx only unless explicitly requested.
• Subnational analysis is out of scope (FAOSTAT is country-level).
• Automatic cross-domain briefs (e.g., production + trade in one brief) are v2; v1 covers one headline domain per brief with optional supporting pulls from 1–2 related domains.