Pp Shodhganga

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Shodhganga — Printing Press CLI

Prerequisites: Install the CLI

This skill drives the shodhganga-pp-cli binary. You must verify the CLI is installed before invoking any command from this skill. If it is missing, install it first:

1. Install via the Printing Press installer. It defaults binaries to $HOME/.local/bin on macOS/Linux and %LOCALAPPDATA%\Programs\PrintingPress\bin on Windows:

   npx -y @mvanhorn/printing-press-library install shodhganga --cli-only

2. Verify: shodhganga-pp-cli --version
3. Ensure the reported install directory is on $PATH for the agent/runtime that will invoke this skill.

If the npx install fails (no Node, offline, etc.), fall back to a direct Go install (requires Go 1.26.6 or newer). This installs into $GOPATH/bin (default $HOME/go/bin), so add that directory to $PATH instead:

go install github.com/mvanhorn/printing-press-library/library/productivity/shodhganga/cmd/shodhganga-pp-cli@latest

If --version reports "command not found" after install, the runtime cannot see the binary directory on $PATH. Do not proceed with skill commands until verification succeeds.

Shodhganga hosts 600,000+ Indian doctoral theses from 900+ universities but exposes no OAI, REST, or OpenSearch API — only HTML. This CLI turns every thesis into a structured Dublin Core record you can search and export as JSON, then mirror a research area into a local database for instant offline analysis with guide-centric indexes, university profiles, and subject trends the web UI can't produce.

When to Use This CLI

Use this CLI when an agent or script needs structured metadata about Indian PhD/MPhil theses — literature reviews, supervisor or university research profiles, subject-trend analysis, or building a citable corpus. It is the only programmatic path to Shodhganga, whose web UI returns HTML with no API.

Anti-triggers

Do not use this CLI for:

• Downloading full-text thesis PDFs — those are login-gated on Shodhganga and out of scope for this CLI.
• Theses outside India or non-doctoral works — Shodhganga only holds Indian PhD/MPhil ETDs.
• Real-time citation counts or impact metrics — Shodhganga carries no citation data.

Unique Capabilities

These capabilities aren't available in any other tool for this API.

Local state that compounds

• harvest — Mirror a research area's theses into a local database so you can search and analyze offline without re-hitting the site.

  Reach for this first when an agent will run many follow-up queries over the same research area — harvest once, then everything else is instant and offline.

  shodhganga-pp-cli harvest "machine learning" --limit 5

• guide — List every thesis supervised by a given research guide across the harvested corpus.

  Use when mapping a supervisor's body of work or building academic-lineage graphs the site can't produce.

  shodhganga-pp-cli guide "Ghosh, Sushant G" --json

Corpus analytics

• university stats — Aggregate a university's theses in the local store into a profile: thesis count, subject spread, and year range.

  Use to compare institutions' doctoral output or profile a university's research strengths.

  shodhganga-pp-cli university stats "Jamia Millia Islamia" --json

• trends — Show how thesis counts for a subject change over completion year across the harvested corpus.

  Use to spot rising or declining research areas in Indian doctoral output.

  shodhganga-pp-cli trends --subject Physics --json

• similar — Find theses in the local store that share the most subject keywords with a given thesis.

  Use to expand a literature review outward from one relevant thesis.

  shodhganga-pp-cli similar 10603/305247 --json

Command Reference

browse — Browse theses by facet (title, author, subject, date)

• shodhganga-pp-cli browse — Browse theses by title, author, subject, keyword, or date

thesis — Search and retrieve Indian PhD theses

• shodhganga-pp-cli thesis get — Get a thesis item page by handle number (the NNNNN in 10603/NNNNN)
• shodhganga-pp-cli thesis search — Search theses by keyword across all of Shodhganga

university — Look up universities and collections (DSpace communities)

• shodhganga-pp-cli university <id> — Get a university/community page by handle number

Finding the right command

When you know what you want to do but not which command does it, ask the CLI directly:

shodhganga-pp-cli which "<capability in your own words>"

which resolves a natural-language capability query to the best matching command from this CLI's curated feature index. Exit code 0 means at least one match; exit code 2 means no confident match — fall back to --help or use a narrower query.

Recipes

Structured metadata for a known thesis

shodhganga-pp-cli thesis get 305247 --json --select title,researcher,guides,university,keywords

Pull just the fields you need from the full Dublin Core record; deeply nested output narrows cleanly with --select.

Harvest a research area for offline analysis

shodhganga-pp-cli harvest "quantum computing" --limit 200

Mirror a topic once; then trends, guide, similar, and 'search --data-source local' all run instantly against the local store.

Map a supervisor's students

shodhganga-pp-cli guide "Ghosh, Sushant G" --json

After harvesting the relevant area, list every thesis a research guide supervised.

Expand a literature review

shodhganga-pp-cli similar 305247

Find theses sharing the most subject keywords with a relevant one, ranked by overlap.

Auth Setup

No authentication required.

Run shodhganga-pp-cli doctor to verify setup.

Agent Mode

Add --agent to any command. Expands to: --json --compact --no-input --no-color --yes.

• Pipeable — JSON on stdout, errors on stderr

• Filterable — --select keeps a subset of fields. Dotted paths descend into nested structures; arrays traverse element-wise. Critical for keeping context small on verbose APIs:

  shodhganga-pp-cli browse --agent --select id,name,status

• Previewable — --dry-run shows the request without sending

• Offline-friendly — sync/search commands can use the local SQLite store when available

• Non-interactive — never prompts, every input is a flag

• Read-only — do not use this CLI for create, update, delete, publish, comment, upvote, invite, order, send, or other mutating requests

Response envelope

Commands that read from the local store or the API wrap output in a provenance envelope:

{
  "meta": {"source": "live" | "local", "synced_at": "...", "reason": "..."},
  "results": <data>
}

Parse .results for data and .meta.source to know whether it's live or local. A human-readable N results (live) summary is printed to stderr only when stdout is a terminal AND no machine-format flag (--json, --csv, --compact, --quiet, --plain, --select) is set — piped/agent consumers and explicit-format runs get pure JSON on stdout.

Paths and state

Agents should treat the CLI's path resolver as part of the runtime contract:

• Use --home <dir> for one invocation, or set SHODHGANGA_HOME=<dir> to relocate all four path kinds under one root.

• Use per-kind env vars only when a specific kind must diverge: SHODHGANGA_CONFIG_DIR, SHODHGANGA_DATA_DIR, SHODHGANGA_STATE_DIR, SHODHGANGA_CACHE_DIR.

• Resolution order is per-kind env var, --home, SHODHGANGA_HOME, XDG (XDG_CONFIG_HOME, XDG_DATA_HOME, XDG_STATE_HOME, XDG_CACHE_HOME), then platform defaults.

• config contains settings like config.toml and profiles. data contains credentials.toml, data.db, cookies, and auth sidecars. state contains persisted queries, jobs, and teach.log. cache contains regenerable HTTP/cache files.

• Stored secrets live in credentials.toml under the data dir. Existing legacy config.toml secrets are read for compatibility and leave config.toml on the first auth write.

• Run shodhganga-pp-cli doctor --fail-on warn to surface path and credential-location warnings. agent-context exposes a schema v4 paths block for agents that need the resolved dirs.

• For MCP, pass relocation through the MCP host config. The MCP binary does not inherit CLI flags:

  {
    "mcpServers": {
      "shodhganga": {
        "command": "shodhganga-pp-mcp",
        "env": {
          "SHODHGANGA_HOME": "/srv/shodhganga"
        }
      }
    }
  }

Fleet precedence: an inherited per-kind env var overrides an explicit --home for that kind. Use SHODHGANGA_HOME or per-kind vars as durable fleet levers, and use --home only for a single invocation. Relocation is not reversible by unsetting env vars; move files manually before clearing SHODHGANGA_HOME, or doctor will not find credentials left under the former root.

Automatic learning

This CLI ships a self-capturing learning loop. The CLI does its own bookkeeping: every invocation is journaled locally, a failed flag followed by a corrected retry auto-derives a flag_alias candidate, and a teach on a query family without a playbook auto-synthesizes a playbook_candidate from the session's journal. Your job is judgment only: recall first, act on surfaced candidates, teach the final answer, playbook amend when you observe a correction. You never record failures by hand.

Step 1: recall before any discovery

Before list/search/drill commands on a new user question, run:

shodhganga-pp-cli recall "<user's question>" --agent

The response envelope:

{
  "query": "...",
  "normalized": "<normalized form>",
  "query_entities": ["..."],
  "found": true | false,
  "match_score": 0.0,
  "results": [
    { "resource_id": "...", "resource_type": "...", "venue": "...",
      "confidence": 2, "entity_match": "exact|partial|unknown",
      "source": "taught|preseed|pattern", "warnings": ["..."] }
  ],
  "mismatches": [ /* only when --debug-mismatches */ ],
  "warnings": [ /* top-level */ ],
  "candidates": [
    { "id": 12, "class": "flag_alias | playbook_candidate",
      "summary": "...", "sightings": 3, "last_seen": "...",
      "rationale": "...",
      "next_action": ["<trial command>", "shodhganga-pp-cli learnings confirm 12"] }
  ],
  "playbook": {
    "query_family": "...",
    "playbook": {
      "steps": [ { "cmd": "<command with {slot} substitution>", "purpose": "..." } ],
      "entity_slots": ["$ENTITY"],
      "expected_tool_calls": 3
    },
    "slots_resolved": { "$ENTITY": { "token": "<live token>", "canonical": "<canonical>" } },
    "notes": "<workarounds + gotchas for this query family>"
  },
  "notes": "<duplicate surface for non-playbook callers>"
}

Empty-store short-circuit: if the store has no learnings, playbooks, or candidates yet (recall finds nothing and learnings list and learnings candidates are both empty), skip recall for the rest of this session instead of taxing every query; resume recall-first once something has been taught.

Step 2: decision tree

Read candidates, playbook, notes, results[0], and warnings in that order:

if Candidates present (warnings include "candidates_present"):
    -> candidates are try-then-confirm, never facts. Follow each candidate's
       two-step next_action verbatim: run the trial command first, then run
       `learnings confirm <id>` only after the trial verified the behavior.
       Reject a wrong candidate with `learnings reject <id>`.
    -> NEVER re-teach something recall surfaced as a candidate; confirm or
       reject that candidate instead of teaching a duplicate.
    -> candidates ride alongside playbooks and resource hits, not instead of
       them; continue with the branches below after acting on them.

if Playbook present:
    -> READ Playbook.notes verbatim FIRST (workarounds + gotchas the CLI surface doesn't expose)
    -> replay Playbook.steps in order, substituting Playbook.slots_resolved entries
       for the entity slot tokens. If a step's slot is unresolved, fall back to
       discovery for that step only.
    -> the Playbook's expected_tool_calls is a budget; if you find yourself running
       materially more, record the divergence via `shodhganga-pp-cli playbook amend`
       at end-of-session.

elif Notes present (no Playbook):
    -> read Notes verbatim before any discovery step; they carry known gotchas
       for this query family even when no structured choreography exists yet.

elif Found AND Results[0].EntityMatch == "exact" AND Results[0].Confidence >= 2:
    -> skip discovery; fetch live data for Results[*].ResourceID in parallel

elif Found AND Results[0].EntityMatch == "partial":
    -> candidate hint, NOT a hit; read the resource title to validate before trusting

elif (any row in Mismatches[] when --debug-mismatches was passed):
    -> treat as cold start; the stored learning is for a different entity
       (different canonical resolved from query_entities)

else:  // Found == false, no playbook, no notes
    -> cold start; run discovery normally; teach the answer afterward (Step 4).
       If the family has no playbook yet, that teach auto-synthesizes a
       playbook candidate from this session's journal - you do not need to
       record one by hand.

Playbook and Notes are orthogonal to the per-resource path. A recall response can carry both a Playbook AND a Results[] hit - use both: the Playbook tells you which choreography to run; the resource hits short-circuit specific steps. Default to skipping mismatches; pass --debug-mismatches only when investigating cold-start surprises.

Candidate judgment details: learnings confirm <id> prints the candidate's full payload before materializing it - check that the printed payload matches the behavior you verified. learnings reject <id> tombstones the derivation signature so the same candidate does not resurface. The envelope carries only the few candidates worth acting on now; shodhganga-pp-cli learnings candidates lists the full open set.

Graceful degradation: if learnings confirm is an unknown command, you are driving an older binary - ignore the candidates guidance and follow the rest of the protocol.

Step 3: always read warnings

• low_confidence: row exists at confidence<2. Treat as a hint, not a skip-discovery hit.
• resource_not_in_store: the local store doesn't have the resource the learning points at. The match validator couldn't classify entities — direct-fetch and re-evaluate.
• cross_alias_match (per-result): the row was taught under a different alias and matched the live query's canonical via entity_lookups (e.g., a "USA" teach satisfying a "United States" recall). Trust the resource_id.
• similar_shape_different_entity:<canonical> (top-level): a structurally matching row exists but its canonical entity differs from the live query's. Treated as cold start; the warning carries the conflicting canonical as a hint, but the row is NOT promoted into Results.
• ambiguous_alias (top-level): a single query entity resolved to multiple canonicals (e.g., "Cards" → Arizona Cardinals + St. Louis Cardinals). Surface the ambiguity from context before committing to a resource.
• candidates_present (top-level): the envelope carries a candidates section. Handle it via the candidates branch in Step 2 before anything else.
• lookup_refresh_available (top-level): an entity in the query has no lookup row yet, but synced data could provide one. Run shodhganga-pp-cli sync to refresh entity lookups.
• Top-level no_learnings_for_query_family: the table had no rows above the Jaccard floor. Pure cold start.