ABS Data API Skill#

Query live ABS datasets, return data + citations, optional tables/charts/reports.

Bundled Resources#

FilePurposescripts/abs_cache.pyMetadata cache manager — refresh catalog, search all 1,200+ dataflows, generate structured metadatascripts/abs_search.pyNL → dataset mapper — curated lookup + fuzzy fallback + ambiguity detectionscripts/abs_query.pyQuery engine — fetches data, formats output, summary/report/describe modesscripts/test_presets.pyPreset validation — tests all presets against live API, pass/fail summarypresets.json20 validated preset queries for common indicatorsmetadata.overrides.jsonManual overrides for discontinued datasets and nicer labelsreferences/dataset-catalog.md~55 curated datasets with IDs, versions, notes (human reference)references/api-guide.mdABS API URL patterns, response structure, example queriesreferences/sdmx-patterns.mdDimension codes (REGION, TSEST, FREQ, MEASURE) per dataset

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Quick Start#

bash

# 1. Warm the cache (do once; auto-refreshes after 24h)
python3 scripts/abs_cache.py refresh
python3 scripts/abs_cache.py gen-metadata

# 2. Search for a dataset (with ambiguity hints)
python3 scripts/abs_search.py "unemployment rate"

# 3. List presets
python3 scripts/abs_query.py --list-presets

# 4. Describe a preset
python3 scripts/abs_query.py --describe-preset cpi-annual-change

# 5. Query latest
python3 scripts/abs_query.py --preset cpi-annual-change --latest --format table

# 6. Summary brief (latest + change context)
python3 scripts/abs_query.py --preset cpi-annual-change --summary latest

# 7. Macro snapshot
python3 scripts/abs_query.py --report macro-snapshot

# 8. Chart
python3 scripts/abs_query.py --preset gdp-chain-volume --start-period 2020-Q1 --chart

---

Workflow#

Step 1 — Identify the dataset#

1. Check references/dataset-catalog.md for the dataflow ID and version
2. If not found, run python3 scripts/abs_search.py "<user query>" for fuzzy match + ambiguity hints
3. If still not found, run python3 scripts/abs_cache.py search "<term>" (searches all 1,200+ dataflows)

Step 2 — Determine dimension key#

1. Check presets.json — if a preset exists, use it directly

2. Read references/sdmx-patterns.md for common dimension codes

3. For an unfamiliar dataset, fetch its structure: bash

   python3 scripts/abs_cache.py structure <ID> <VERSION>
   
   

Step 3 — Query the data#

bash

python3 scripts/abs_query.py <ID> [KEY] [--version V] [--start-period P] [--end-period P] [--latest] [--format text|csv|json|table] [--chart] [--out FILE]

Step 4 — Format and deliver#

• Default text format includes citation. Use --format table for markdown tables.
• For charts, requires matplotlib; gracefully falls back if not installed.
• Use --summary latest for quick briefs with change context.
• Use --report macro-snapshot for a full multi-indicator briefing.
• Always include the citation line in any response to the user.

---

Presets (20 validated)#

Common indicator queries are bundled in presets.json. All validated live March 2026.

bash

# List all available presets
python3 scripts/abs_query.py --list-presets

# Describe a preset (shows what it measures and when to use it)
python3 scripts/abs_query.py --describe-preset unemployment-rate

# Run a preset
python3 scripts/abs_query.py --preset cpi-annual-change --latest --format table
python3 scripts/abs_query.py --preset unemployment-rate --latest
python3 scripts/abs_query.py --preset gdp-annual-change --chart
python3 scripts/abs_query.py --preset wage-annual-change --start-period 2020-Q1
python3 scripts/abs_query.py --preset population-national --format csv
python3 scripts/abs_query.py --preset dwelling-prices-mean --format table
python3 scripts/abs_query.py --preset trade-balance --start-period 2024-01
python3 scripts/abs_query.py --preset household-spending-change --summary latest

Key presets: cpi-annual-change, unemployment-rate, participation-rate, employment-level, underemployment-rate, labour-force-size, gdp-annual-change, wage-annual-change, population-national, dwelling-prices-mean, trade-balance, goods-exports, goods-imports, household-spending-change.

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Output Formats#

FlagOutput_(default)_Human-readable text with friendly labels + citation--format tableMarkdown table with friendly labels and rendered periods--format csvCSV with raw codes + citation comment--format jsonJSON with raw codes + *_label fields + TIME_PERIOD_rendered``--chartPNG chart with dataset title, subtitle, latest-point annotation--summary latestLatest value + previous + absolute/percentage-point deltas + textual summary--report macro-snapshotCompact multi-indicator macro briefing (7 key economic indicators)--citation-style analystAnalyst-style source footnote block--flat-viewAllDimensions format (wider; may be large)

---

Period Rendering#

All output modes now render periods in human-readable format:

• 2026-01 → January 2026
• 2025-Q4 → December quarter 2025
• 2025-Q1 → March quarter 2025
• Ranges: March quarter 2024 to December quarter 2025

This applies to table headers, text output, citations, chart labels, and summary/report output.

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JSON Output with Labels#

--format json returns both raw dimension codes and friendly *_label fields:

json

{
  "TSEST": "20",
  "TSEST_label": "Seasonally Adjusted",
  "TIME_PERIOD": "2026-02",
  "TIME_PERIOD_rendered": "February 2026",
  "value": 4.277
}

Backward compatible — raw codes are preserved.

---

Ambiguity Detection#

abs_search.py classifies ambiguity when multiple datasets match:

• frequency — monthly vs quarterly
• geography — national vs state vs SA2/LGA
• measure — index vs % change vs level
• series — original vs seasonally adjusted
• dataset — distinct series cover the same topic

Prints clarifying questions to help the user or agent narrow the query.

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Cache and Metadata#

CommandDescriptionabs_cache.py refreshFetch all dataflows from ABS, save to ~/.cache/abs-data-api/catalog.json``abs_cache.py gen-metadataGenerate metadata.generated.json from presets + catalog + overridesabs_cache.py statusShow cache age, dataflow count, structure count, metadata statusabs_cache.py search <term>Search across all cached dataflowsabs_cache.py structure <ID> [VER]Fetch and cache DSD for a specific dataflow

Runtime metadata priority: metadata.generated.json > catalog.json > dataset-catalog.md. Override quirks (discontinued datasets, nicer labels) in metadata.overrides.json.

---

Validation#

bash

python3 scripts/test_presets.py             # test all presets
python3 scripts/test_presets.py --verbose   # with timing
python3 scripts/test_presets.py --preset unemployment-rate  # single

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Ambiguity Rules#

• Multiple matching datasets: prefer the most specific. E.g. for "inflation", CPI beats CPI_M beats PPI.
• No dimension key provided: use all — the API will return everything; then filter. If the response is large (>100 observations), the tool warns you.
• Version unknown: look up from generated metadata, then catalog; try 1.0.0 as last resort.
• User asks for "latest": always add --latest flag (uses lastNObservations=1).
• Census data requested: redirect to the census-database skill; this skill handles ABS time-series only.
• Chart requested but matplotlib missing: output text/table format and note how to install matplotlib.
• Retail Trade (RT) requested: DISCONTINUED after June 2025. Use HSI_M or BUSINESS_TURNOVER instead.
• RPPI requested: note the API only has data to ~2021-Q4. Use RES_DWELL_ST for current dwelling prices.

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Citation Format#

All responses include a citation:

Source: Australian Bureau of Statistics, <Full Dataset Name> (Cat. <catalogue-number>; dataset <ID>; v <version>). <human-readable-period>. Retrieved via ABS Data API: <url>.

Example:

Source: Australian Bureau of Statistics, Consumer Price Index (Cat. 6401.0; dataset CPI; v2.0.0). January 2026. Retrieved via ABS Data API: https://data.api.abs.gov.au/rest/data/ABS,CPI,2.0.0/.

---

What's New in v1.0.2#

1. Metadata Generation#

• gen-metadata command: Builds unified metadata from presets + live catalog + manual overrides
• Auto-refresh: Generated metadata automatically updates when older than 24 hours
• Ensures all datasets are findable and correctly labeled, even as ABS API evolves

bash

python3 scripts/abs_cache.py gen-metadata

2. Smart Ambiguity Detection#

• Classifies ambiguity when multiple datasets match a user query (frequency, geography, measure, series, dataset)
• Provides clarifying questions grouped by intent (prices, wages, employment, housing, etc.)
• Flags discontinued datasets with replacement suggestions (e.g., RT → HSI_M)
• Uses curated intent groups + ambiguity tags to guide disambiguation

bash

python3 scripts/abs_search.py "inflation"  # May suggest CPI, CPI_M, PPI with clarifying Qs

3. Summary Mode with Change Context#

• --summary latest: Shows latest value + previous + absolute deltas + brief summary

• Automatically detects rates/growth measures and uses percentage-point notation instead of misleading relative % changes

  • Example: Unemployment rate rises from 4.0% to 4.3% → "change of +0.3 percentage points" (NOT "+7.5% relative change")
  • Applies to: unemployment, participation, inflation rates, growth measures

• Ideal for quick briefings and executive summaries

bash

python3 scripts/abs_query.py --preset unemployment-rate --summary latest
# Output: Current: 4.3% | Previous: 4.0% (Feb) | Change: +0.3pp | [Brief context]

4. Macro-Snapshot Report#

• --report macro-snapshot: Single-command economic briefing covering 7 key indicators
• Fetches CPI, unemployment, participation, employment, GDP growth, wage growth, household spending
• All with change context and period rendering
• Perfect for media snippets or executive briefings

bash

python3 scripts/abs_query.py --report macro-snapshot

5. Percentage-Point Delta Fix#

• Smart detection: Automatically recognizes rates and growth measures via keyword matching

• Applies percentage-point notation to avoid confusion with relative % changes

• Examples:

  • Unemployment: 4.0% → 4.3% = +0.3 percentage points (not +7.5%)
  • CPI: 3.5% → 3.2% = -0.3 percentage points
  • Wage growth: 4.1% → 4.0% = -0.1 percentage points

• Applies to all output modes: text, table, JSON, summary

6. Metadata Overrides (metadata.overrides.json)#

• Discontinued datasets (RT → HSI_M, RPPI stale warning)
• Friendly names for complex dataset IDs
• Replacement hints with explanations
• Easy to extend for future dataset changes

The query engine appends this automatically. Do not strip it from tool output.

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Changelog#

v1.0.2 (March 2026)#

New Features:

• ✨ Metadata generation ( gen-metadata command) — builds unified metadata from presets + catalog + overrides with auto-refresh
• ✨ Smart ambiguity detection — classifies multiple matches by type (frequency, geography, measure, series, dataset) and provides grouped clarifying questions
• ✨ Summary mode with change context ( --summary latest) — shows latest + previous + absolute deltas + brief summary
• ✨ Macro-snapshot report ( --report macro-snapshot) — single-command economic briefing covering 7 key indicators
• ✨ Percentage-point delta fix — rates/growth measures automatically use pp notation instead of misleading relative % changes
• ✨ Intent grouping — curated entries now include intent_group and ambiguity_tags for smarter disambiguation

Improvements:

• Discontinued dataset detection (RT → HSI_M, RPPI stale warning)
• Better metadata overrides system for dataset quirks
• Enhanced search with ambiguity classification
• All output modes now respect percentage-point notation where applicable

Affected Scripts:

• abs_cache.py — added gen-metadata command and generate_metadata() function
• abs_search.py — added ambiguity detection, intent grouping, and clarifying questions
• abs_query.py — added --summary latest, --report macro-snapshot, percentage-point delta detection
• metadata.overrides.json — new file for manual dataset overrides

v1.0.1 (Previous)#

• Base preset system with 20 validated queries
• Curated dataset catalog and SDMX dimension references
• Cache refresh and fuzzy search capabilities