induct-research

namespace	aiwg
platforms	["all"]
name	induct-research
description	Induct research sources (issue, file, directory, or URI) into a research corpus — reads, annotates, and files structured induction tasks. Like address-issues but for research.
commandHint	{"argumentHint":"<target> [--repo <dest>] [--dry-run] [--priority high|medium|low] [--tag <topic>] [--recursive]","allowedTools":"Read, Write, Glob, Grep, Bash, Agent, WebFetch, WebSearch, mcp__gitea__issue_write, mcp__gitea__issue_read, mcp__gitea__list_issues, mcp__hound__authenticate","model":"sonnet","category":"research","modelRole":"coding","modelTier":"standard"}

Induct Research

Process one or more research sources — an issue, a file, a directory of papers, or a URI — and file structured induction tasks into a research repository so nothing gets lost. The analogue of address-issues for research corpora.

Kernel Delegation

As of ADR-021, induct-research delegates core ingest mechanics to the semantic memory kernel.

Delegation pattern:

1. induct-research retains its public name and interactive research-induction UX
2. Internal ingest mechanics delegate to memory-ingest --consumer research-complete
3. Research-specific layers remain in this wrapper:
   • GRADE quality assessment (via ingestRequires: ["grade-quality"])
   • Citation validation (via ingestRequires: ["provenance"])
   • Research-specific page templates
4. Cross-references written as @-mentions per consumer schema

What changed: The ingest pipeline (source reading, page creation, index update, log append) is now handled by memory-ingest. This skill adds the research-specific quality and citation layers on top.

Backward compatibility: No UX changes. Existing invocations work identically.

@agentic/code/addons/semantic-memory/skills/memory-ingest/SKILL.md

Triggers

• "induct this paper" → single file induction
• "induct the research queue" → batch directory induction
• "add these references to the research repo" → URI or file-path induction
• "process the research from issue-planner" → induct .aiwg/research/queue/
• "induct research into gitea" → named MCP service target
• /induct-research <target> → direct invocation

Parameters

<target> (required)

What to induct. Three formats accepted:

Format	Example	Behavior
File path	.aiwg/research/queue/	Read all .md files in the directory
Single file	.aiwg/research/queue/ref-dapper.md	Induct one source
URI	https://arxiv.org/abs/2307.09288	Fetch and induct the paper at that URL
Directory glob	papers/**/*.pdf	Induct all matched files recursively
Issue reference	gitea:roctinam/research#42	Read the issue body as a research stub

--repo <dest> (optional)

Where to file induction tasks. Accepts the same three formats as --induct-research in issue-planner:

Format	Example	Behavior
File path	--repo .aiwg/research/inducted/	Write task .md files locally
URI	--repo https://git.integrolabs.net/roctinam/research	File issues to that Gitea/GitHub/Jira instance
Named MCP	--repo gitea	Use mcp__gitea__issue_write directly
Named MCP	--repo codehound	Register in Hound search index

Falls back to AIWG_RESEARCH_REPO env var if --repo is omitted.

--dry-run (optional)

List what would be inducted and where, without writing or filing anything.

--priority high|medium|low (optional)

Override the suggested priority for all inducted items. Default: assessed per source.

--tag <topic> (optional)

Apply a topic tag to all inducted items. Repeatable: --tag llm --tag evaluation.

--recursive (optional)

When target is a directory, recurse into subdirectories. Default: top-level only.

---

Execution Flow

Phase 1: Source Discovery

1. Parse <target> — determine input type (file, directory, URI, issue ref)
2. Collect sources:
   • File/directory: glob for .md, .pdf, .txt, .yaml files
   • URI: fetch the resource; detect type (paper, doc page, repo, issue)
   • Issue reference: fetch issue body and all comments via MCP or CLI
3. Deduplicate — skip sources already present in the destination repo (if queryable)
4. Report discovery:

Found 9 sources to induct:
  3 Markdown stubs (.aiwg/research/queue/)
  4 PDF papers (papers/2024/)
  2 URI references
  Skipping 1 (already inducted: REF-042)

---

Phase 2: Source Acquisition (acquire before analyze)

CRITICAL: Never write analysis docs from metadata or abstracts alone. The pipeline is: acquire full content → read full content → write analysis doc.

This was learned from a session where 88 of 120 papers were inducted as shallow stubs written from arXiv abstract pages — not the actual papers. See #817.

For each source, ensure full content is available before analysis:

For PDFs / full papers:

1. Acquire the PDF — call /research-acquire <url> --extract-text to download the PDF to sources/pdfs/full/ and extract full text to sources/text/
2. Verify acquisition — confirm the PDF exists at the expected path and is non-empty
3. If PDF unavailable (paywall, dead link): mark as acquisition-failed in frontmatter, file a stub with status: pending-acquisition, and skip to next source. Do NOT write a full analysis doc from the abstract alone.

For URIs (web sources):

1. Fetch the full page (WebFetch) — save to sources/web/<slug>.html
2. Classify: paper, blog post, official docs, repo README, specification, news
3. If paper: call /research-acquire to get the actual PDF — do not analyze from the landing page HTML
4. If non-paper web source: the fetched HTML/text is the full content — proceed to analysis

For Markdown stubs (from issue-planner queue files):

• Read the stub content and relevance summary
• If the stub references a paper URL: acquire the PDF first (same as above)
• If the stub is a research brief with no external source: proceed as-is

For issue references:

• Read full issue body and comments
• Extract referenced URLs, files, or topics
• If URLs point to papers: acquire PDFs before analysis
• If no external sources: treat as a research brief stub

Phase 2.5: Per-Source Analysis (on full content)

Only after full content is acquired, run analysis:

For PDFs / full papers (with full text available):

• Read the full extracted text, not just the abstract
• Extract title, authors, year, abstract, methodology, key findings, limitations
• Identify key claims with specific evidence (quotes, figures, tables)
• Assess relevance to existing corpus (check .aiwg/research/ for related REF-XXX files)
• Assign GRADE quality level (A–D) based on source type and peer-review status
• Target: analysis docs should be 150-300 lines with substantive content from the paper

For web sources (with full content saved):

• Read the full saved page content
• Extract key points, methodology if applicable, credibility indicators
• Assess relevance and quality

Quality gate: If the resulting analysis doc is under 80 lines, flag it as a potential stub. Either the source content wasn't fully read or the analysis was superficial. Consider re-running with explicit instructions to read the full text.

---

Phase 3: Induction Task Filing

For each analyzed source, file one induction task using the standard template.

Induction task body:

## Reference Induction

**Source**: <URL, file path, or issue reference>
**Type**: <paper | blog | docs | repo | spec | stub | issue>
**GRADE**: <A | B | C | D | unassessed>
**Priority**: <high | medium | low>
**Tags**: <topic1>, <topic2>

## Summary
<2–3 sentences: what this source covers and why it's relevant>

## Key Claims / Findings
- <Specific claim or finding>
- <Specific claim or finding>
- <Specific claim or finding>

## Relevance to Corpus
<How this relates to   —     >


 [ ] Read full source
 [ ] Extract key insights as Zettelkasten notes
 [ ] Cross-reference with existing corpus
 [ ] Assign REF-XXX identifier
 [ ] Tag with topic taxonomy
 [ ] Assess with /research-quality
 [ ] Archive with /research-archive (if paper/PDF)
 [ ] Add to citation graph with /research-cite


 Surfaced by: 
 Surfaced for: 
 Induction date: 

Filing based on --repo target:

• File path: write induct-<slug>.md to destination directory
• Gitea URI/MCP: mcp__gitea__issue_write with label research-induction
• GitHub URI: gh issue create --label research-induction
• Jira URI: REST POST /rest/api/2/issue with issue type Task
• Codehound MCP: register URI in search index, create stub document

---

Phase 3.5: Cross-Reference Fan-Out

After creating each new literature note, update the broader corpus with bidirectional cross-references. This is what makes a corpus compound rather than just accumulate.

For each newly inducted source:

1. Search existing findings for topically related REF-XXX notes:

   • Match by shared tags
   • Match by overlapping key claims or methodologies
   • Match by citation overlap (both cite the same sources)

2. Add "Related Sources" cross-references:

   • In the new note: add a ## Related Sources section listing existing REF-XXX notes and how they relate (confirms, contradicts, extends, prerequisite)
   • In existing notes: append the new REF-XXX to their ## Related Sources section with relationship type

3. Flag contradictions or confirmations:

   • If the new source contradicts an existing finding, add a contradiction marker to both notes
   • If it confirms an existing finding, add a confirms marker

4. Update synthesis documents in .aiwg/research/synthesis/:

   • If a relevant synthesis document exists, append a note that new evidence is available

Example cross-reference entry:

## Related Sources

- **REF-034** — Confirms: both identify prompt injection as the primary attack vector for LLM agents
- **REF-042** — Extends: this source adds quantitative benchmarks missing from REF-042's qualitative analysis
- **REF-067** — Contradicts: claims agent sandboxing overhead is <5%, while REF-067 measured 15-20%

Batch optimization: When inducting multiple sources in a batch, defer cross-referencing until all new notes are created, then run a single fan-out pass across all new + existing notes. This avoids redundant searches.

Skip conditions: Skip cross-referencing when:

• --dry-run is set
• Source is filed as a stub (not yet fully documented)
• Fewer than 3 existing REF-XXX notes in the corpus (too early for meaningful cross-refs)

---

Phase 4: Summary Report

## Induction Summary

| # | Source | Type | Priority | Filed At |
|---|--------|------|----------|----------|
| 1 | RFC 9110 HTTP Semantics | spec | high | gitea#301 |
| 2 | "Dapper" Google Tracing Paper | paper | high | gitea#302 |
| 3 | opentelemetry.io/docs | docs | medium | gitea#303 |
| 4 | github.com/jaegertracing/jaeger | repo | medium | gitea#304 |
| 5 | arxiv.org/abs/2012.15161 | paper | low | gitea#305 |
...

Inducted: 9
Skipped: 1 (already present)
Destination: gitea:roctinam/research

Next steps:
- /research-acquire <URL> for any paper that needs PDF download
- /research-document to annotate inducted sources
- /research-quality to score GRADE for each inducted item

---

Target Resolution Logic

resolve_target(target):
  if target starts with "http://" or "https://":
    host = extract_host(target)
    if host matches known_gitea_instances: use mcp__gitea__issue_write
    if host == "github.com": use gh CLI
    if host matches jira pattern: use Jira REST API
    else: fetch as web resource, induct as URI reference

  elif target matches "gitea:<owner>/<repo>#<n>":
    fetch issue via mcp__gitea__issue_read

  elif target is a named MCP service ("gitea", "codehound", "github"):
    use that service's write/register tool directly

  elif target is a file path:
    if path is directory: glob for .md/.pdf/.txt files
    if path is a file: induct single source

---

Batch Mode — Directory of Papers

When target is a directory, process all supported files:

/induct-research papers/2024/ --repo gitea --tag llm --recursive

⏳ Scanning papers/2024/ (recursive)...
  Found 23 PDF files
  Found 7 Markdown stubs
  Found 2 YAML records
  Deduplicating against gitea:roctinam/research...
    Skipping 4 (already inducted)

⏳ Analyzing 28 sources (parallel agents)...
  ✓ Batch A (7 sources): complete
  ✓ Batch B (7 sources): complete
  ✓ Batch C (7 sources): complete
  ✓ Batch D (7 sources): complete

⏳ Filing 28 induction tasks to gitea:roctinam/research...
✓ Inducted: 28 | Skipped: 4 | Total: 32

---

Integration with issue-planner

issue-planner --induct-research <target> calls this skill's Phase 3 (filing) logic directly after Phase 2 research synthesis. The references are the URLs and sources discovered during the parallel research pass.

/induct-research can also be invoked standalone to process:

• Pre-existing queues: /induct-research .aiwg/research/queue/
• Ad-hoc papers: /induct-research https://arxiv.org/abs/2307.09288
• Full directories: /induct-research ~/Downloads/papers/ --repo gitea

---

Composition

induct-research <target>
    │
    ├── Phase 1: Source discovery
    │   ├── File/directory: glob + read
    │   ├── URI: WebFetch + classify
    │   └── Issue ref: mcp__gitea__issue_read or gh CLI
    ├── Phase 2: Source acquisition (acquire before analyze)
    │   ├── PDF/paper → /research-acquire --extract-text
    │   ├── URI → WebFetch full page → /research-acquire if paper
    │   ├── Stub with URL → acquire referenced source
    │   └── Skip analysis if acquisition fails (mark pending-acquisition)
    ├── Phase 2.5: Per-source analysis (on full content only)
    │   ├── PDF agent → read full text, extract claims + GRADE
    │   ├── Web agent → read full saved page, assess credibility
    │   ├── Stub agent → parse relevance summary
    │   └── Quality gate: flag docs under 80 lines as potential stubs
    ├── Phase 3: Induction task filing
    │   ├── File path → write .md task files
    │   ├── Gitea URI/MCP → mcp__gitea__issue_write
    │   ├── GitHub URI → gh issue create
    │   └── Codehound MCP → register in search index
    ├── Phase 3.5: Cross-reference fan-out
    │   ├── Search existing findings by tags + claims
    │   ├── Add bidirectional Related Sources sections
    │   ├── Flag contradictions / confirmations
    │   └── Update synthesis documents
    └── Phase 4: Summary report

References

• @$AIWG_ROOT/agentic/code/frameworks/sdlc-complete/skills/issue-planner/SKILL.md — Calls induct-research during Phase 2b
• @$AIWG_ROOT/agentic/code/frameworks/research-complete/skills/research-acquire/SKILL.md — Full PDF acquisition (called for paper URIs)
• @$AIWG_ROOT/agentic/code/frameworks/research-complete/skills/research-document/SKILL.md — Annotate inducted sources
• @$AIWG_ROOT/agentic/code/frameworks/research-complete/skills/research-quality/SKILL.md — GRADE scoring for inducted items
• @$AIWG_ROOT/agentic/code/frameworks/sdlc-complete/skills/address-issues/SKILL.md — Analogous pattern for code issues
• @$AIWG_ROOT/agentic/code/addons/aiwg-utils/rules/subagent-scoping.md — Parallel batch analysis constraints

Storage Routing (#934, #968)

This skill's persistence flows through resolveStorage('research'). On the default fs backend the research corpus lives at .aiwg/research/. Heavy artifacts (papers, archived sources) can move to a secondary drive by setting roots.research in .aiwg/storage.config (one of the headline #934 use cases).

aiwg research-store path                            # resolved root
aiwg research-store list --prefix sources/
aiwg research-store get sources/paper-123.md