commoner-probe

Sousveillance infrastructure for the state's mandatory disclosure systems.

A commoner probes the state's own paperwork — parliamentary questions, committee reports, state assembly records — and turns it into evidence. commoner-probe automates the acquisition so you can focus on the analysis.

pip install "commoner-probe[all]"

import commoner_probe as probe   # alias used throughout these docs

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Why this exists

Parliamentary questions, committee reports, floor debates, bills, state assembly records, CSR exports, public mining-district disclosures, Union Budget files, and faculty-recruitment ads from public universities are mandatory or official public disclosures. The data exists. The problem is that it lives across undocumented portals with inconsistent APIs, no bulk export, and PDFs that require extraction to read programmatically.

commoner-probe handles the entire acquisition pipeline:

public disclosure portals  →  manifest.jsonl  →  files/PDFs  →  extracted records  →  your analysis
                               (metadata)        (raw source)      (structured text)

Classification, topic modelling, and dossier generation are intentionally out of scope. This library does one thing: acquire public disclosure data into provenance-rich, schema-validated JSONL and source files.

---

Install

Requires Python 3.10+. Released on PyPI.

pip install "commoner-probe[all]"          # everything needed for acquisition + extraction
pip install "commoner-probe[all,dev]"      # + schema validation, tests, lint

The core package has zero required dependencies; each capability is an extra:

Extra	Pulls in	Needed for
http	requests	any network acquisition
pdf	pdfminer.six	extract-answers, PDF text extraction
budget	lxml	budget (RBI page discovery)
academia	beautifulsoup4, pdfminer.six	academic-jobs
pandas	pandas	Corpus.to_dataframe()
all	requests, pdfminer.six, lxml, beautifulsoup4	everything above except pandas
dev	jsonschema, pytest, ruff, lxml, beautifulsoup4	validate, running the test suite

---

Five-minute quickstart

Step 1 — Write a topic profile

{
  "name": "climate",
  "description": "Climate change and environmental policy",
  "search_groups": {
    "climate": ["climate change", "global warming", "net zero"],
    "air_quality": ["air pollution", "AQI", "particulate matter"]
  },
  "lok_sabha_ministries": ["ENVIRONMENT", "POWER", "PETROLEUM"],
  "rajya_sabha_ministry_likes": ["ENVIRONMENT", "POWER", "PETROLEUM"]
}

Step 2 — Probe parliamentary questions

commoner-probe sansad \
  --topic topic.json \
  --out data/climate \
  --house both \
  --from-date 2019-01-01

Writes data/climate/manifest.jsonl — one record per question from both houses.

Step 3 — Probe committee reports

commoner-probe committees \
  --topic topic.json \
  --out data/climate-committees \
  --house both

One record per standing committee report (LS and RS DRSCs).

Step 4 — Extract text from PDFs

commoner-probe extract-answers --out data/climate
commoner-probe extract-answers --out data/climate-committees

Parses downloaded PDFs into answers.jsonl: Q/A pairs, committee recommendations, and government responses.

Step 5 — Load in Python

import commoner_probe as probe

c = probe.Corpus("data/climate")

for r in c.manifest_qa():
    print(r.date, r.house, r.ministry, r.title)

for pair in c.join_qa():
    if pair.answers:
        print(pair.manifest.title)
        print(pair.answers[0].question_text[:200])

---

What you can study

Parliamentary questions (Lok Sabha + Rajya Sabha)

Each record carries who asked (MP name, party, state), which ministry answered, question number, type (starred / unstarred), date, session, and the full PDF. After extract-answers — extracted question and answer text.

Typical research questions: ministry responsiveness rates, which MPs ask the most questions by topic, how the same policy question evolves across sessions, party-level questioning patterns.

import commoner_probe as probe
from collections import Counter

c = probe.Corpus("data/climate")
ministry_counts = Counter(r.ministry for r in c.manifest_qa())
for ministry, n in ministry_counts.most_common(10):
    print(f"{ministry}: {n}")

Standing committee reports (LS + RS DRSCs)

Committee reports come in four shapes:

report_type	What it is
demands_for_grants	Annual budget scrutiny — the committee dissects ministry spending
bill	The committee's examination of a pending bill before it passes
subject	Own-initiative policy investigation — deepest substantive record
action_taken	The government's formal response to the committee's recommendations

Action Taken Reports (ATRs) are the government's formal written responses to committee recommendations. The atr-linkage command connects each ATR back to the original report, enabling lifecycle analysis: recommendation → government rejection/acceptance → follow-up.

import commoner_probe as probe

c = probe.Corpus("data/climate-committees")

for chain in c.join_atr_chain():
    print(f"Report: {chain.original and chain.original.title}")
    print(f"  Recommendations: {len(chain.original_observations)}")
    print(f"  Government responses: {len(chain.atr_answers)}")

Floor debates (Lok Sabha)

debates acquires the Lok Sabha "text of debate" record: one PDF transcript per sitting day. It enumerates sitting dates per Lok Sabha / session, then fetches each day's transcript (optionally downloading the PDF with a SHA-256). It is a day-by-day document acquisition — verbatim text and per-speaker segmentation are left to a downstream extraction step. The richest longitudinal record of what is said on the floor.

commoner-probe debates \
  --out data/debates \
  --loksabhas 18 \
  --download

Bills and legislation

bills fetches the sansad.in legislation catalog — every bill with its introduction date, stage dates, and status — deduplicated by a stable key (no topic profile needed; the bill list is an exhaustive catalog). Enables tracking legislative velocity, committee-scrutiny rates, and private-member-bill outcomes.

commoner-probe bills \
  --out data/bills \
  --house both \
  --bill-type "Private Member"

State assembly records (NeVA portals)

From 2020, sub-national governments have been adopting NIC's NeVA (National e-Vidhan Application) infrastructure under a centrally sponsored scheme run by the Ministry of Parliamentary Affairs. Most state assemblies are onboarding, though coverage varies. The state-assembly command probes any NeVA portal:

commoner-probe state-assembly \
  --portal gujarat \
  --state GJ \
  --out data/gujarat-assembly \
  --assemblies 15

State Acts, amendments, rules, and notifications (India Code)

India Code (indiacode.nic.in) is the government's own statutory-instrument archive: every state's Acts plus their amendments, rules, regulations, notifications, orders, circulars, ordinances, and statutes, each with a downloadable PDF. indiacode enumerates a state's full Act catalog and parses every instrument found on each Act's page.

commoner-probe indiacode --out data/indiacode --states "West Bengal"

import commoner_probe as probe

c = probe.Corpus("data/indiacode")
for r in c.manifest_indiacode():
    if r.is_amendment:
        print(r.state, r.short_title, r.instrument_date, r.description)

MCA CSR company-spend exports

The Ministry of Corporate Affairs CDM CSR data page exposes downloadable CSV exports by financial year. These records compare reporting/spending companies and project-sector amounts. They do not identify CSR consultants or implementing agencies unless MCA publishes that in the source export.

commoner-probe mca-csr \
  --out data/mca-csr \
  --years 2022-23,2021-22

import commoner_probe as probe

c = probe.Corpus("data/mca-csr")
for r in c.manifest_mca_csr():
    print(r.financial_year, r.status, r.filename)

Mines DMFT / PMKKKY disclosures

mines-dmft acquires raw Ministry of Mines and Odisha DMFT public disclosure files. Ministry CSVs are current cumulative snapshots timestamped by the source; treat them as snapshots, not fiscal-year series.

commoner-probe mines-dmft \
  --out data/mines-dmft \
  --sources mines-gov-in,odisha

Pair the executive disclosure snapshots with Sansad oversight records without flattening the source families:

commoner-probe evidence dmft \
  --mines-dmft-dir data/mines-dmft \
  --sansad-dir data/sansad/mines-dmft-pmkkky \
  --out data/evidence/dmft.json

Union Budget & RBI State-Finances

budget acquires fiscal source files: Union Budget SBE (Statement of Budget Estimates) spreadsheets — a static table of per-fiscal-year URL templates expanded across the requested demand numbers — and RBI State-Finances documents discovered from the RBI publication page. Each file is downloaded with existence-skip and a SHA-256, one budget_source_file record per file. Acquisition only: the spreadsheet→rows parsing stays downstream (it needs pandas).

commoner-probe budget \
  --out data/budget \
  --sources union-budget,rbi-state-finances \
  --demands 101,1,33

Academic faculty-recruitment ads

academic-jobs crawls Indian higher-education-institution (HEI) career pages for faculty-recruitment advertisements, driven by a bundled institution registry. Each ad becomes one academic_job_posting record; fetch/parse failures and empty-result cases are recorded so coverage gaps are visible rather than silent. (Migrated from an earlier academic-jobs scraper.)

commoner-probe academic-jobs \
  --out data/academic-jobs \
  --institutions iit-kharagpur,iit-bombay

---

Commands

36 subcommands across parliament, courts, budgets, census and archives. commoner-probe --help lists them; docs/CLI.md documents each one with a worked example.

Topic profile

Controls what the probe acquires:

{
  "name": "libraries",
  "description": "Public library infrastructure and policy",
  "search_groups": {
    "public_libraries": ["public library", "rural library"],
    "policy": ["National Mission on Libraries", "RRRLF"]
  },
  "lok_sabha_ministries": ["CULTURE", "EDUCATION"],
  "rajya_sabha_ministry_likes": ["CULTURE", "EDUCATION"]
}

• search_groups — keyword groups for LS full-text search. Each query runs independently; results are union-deduped on key.
• lok_sabha_ministries — exact ministry filter for LS (case-sensitive).
• rajya_sabha_ministry_likes — ministry LIKE filter for RS (prefix match).

See examples/topics/ for working examples.

---

Output files

File	Contents
manifest.jsonl	One record per question or committee report
_runs.jsonl	Audit log: scope, topic hash, errors, per-bucket counts
answers.jsonl	Extracted Q/A and recommendation/response pairs
vacancy_rows.jsonl	Typed sanctioned/in-position/vacant rows from vacancy-disclosure answers
atr_linkage.jsonl	ATR → original report linkages
source CSV/JSON/HTML files	Raw source files for source-specific probes such as MCA CSR and DMFT
pdfs/ls/	Downloaded LS PDFs
pdfs/rs/	Downloaded RS PDFs
probe.log	Human-readable probe progress log

For complete field-level documentation see docs/SCHEMAS.md.

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Entity resolution (--with-entities)

Pass --with-entities to commoner-probe sansad to resolve asker names to stable entity_id values. On first run the entity store is populated from the sansad.in MP roster; subsequent runs reuse the local cache.

Resolved entity IDs join across corpora and sessions — useful for studying the same MP's questioning behaviour over time or across houses.

---

Python API

import commoner_probe as probe

c = probe.Corpus("data/climate")

# Typed iterators
for r in c.manifest_qa():                 # ManifestQaRecord
    ...
for r in c.manifest_committee_reports():  # ManifestCommitteeReportRecord
    ...
for r in c.answers_qa():                  # AnswerQaResponse
    ...
for r in c.answers_atr():                 # AnswerAtrResponse
    ...
for r in c.answers_dfg():                 # AnswerDfgRecommendation
    ...
for r in c.atr_linkages():                # AtrLinkageRecord
    ...
for r in c.manifest_mca_csr():            # ManifestMcaCsrRecord
    ...
for r in c.manifest_mines_dmft():         # ManifestMinesDmftRecord
    ...
for r in c.manifest_doe_pay_allowances(): # ManifestDoePayAllowancesRecord
    ...
for r in c.vacancy_rows():                # VacancyRowRecord
    ...
for r in c.runs():                        # RunRecord
    ...

# Join helpers
for pair in c.join_qa():                  # manifest + extracted answers
    ...
for chain in c.join_atr_chain():          # ATR + original report + observations
    ...

# pandas (pip install commoner-probe[pandas])
df = c.to_dataframe("manifest_committee_reports")

See examples/usage.py for a runnable walkthrough. See docs/ENDPOINTS.md for source-family endpoint notes. See docs/GOV_SITE_PLATFORMS.md for which Union ministry websites are scrapeable (and which are JS-rendered SPAs, WAF- blocked, or unreachable) — read before adding a new ministry-ddg portal.

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Contributing

Bug reports, portal breakage reports, and pull requests are welcome at github.com/CommonerLLP/commoner-probe. See CONTRIBUTING.md for development setup and conventions, and CODE_OF_CONDUCT.md for community expectations. Release history lives in CHANGELOG.md.

Government portals change without notice — if a probe stops working, an issue with the failing command and its probe.log output is the most useful report.

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License

MIT License — see LICENSE.

commoner-probe is sousveillance infrastructure, built for the commons. It is released under the permissive MIT license so it can serve as a shared acquisition floor that any downstream project — including the other repos in the CommonerLLP federation, whatever their own licenses — can build on without copyleft friction.

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Upcoming

MP profiles and career timelines

Structured biographical data for each member: constituency, state, party, terms served, educational background, declared profession. Pairs with the Q/A corpus for studies of how MP background predicts parliamentary participation.