memory-ingest

name	memory-ingest
description	Ingest a source into any consumer's semantic memory by reading the topology contract
namespace	aiwg
category	kernel
platforms	["claude","copilot","cursor","factory","windsurf","warp","codex","opencode","openclaw","hermes"]
triggers	["help my AI remember what we decided","help me preserve project decisions between sessions","choose memory KB or project artifact flow","memory ingest"]

memory-ingest

Ingest an external source into a consumer framework's semantic memory. Reads the consumer's memory.topology contract to know where pages live, then extracts, summarizes, integrates, and cross-references — all topology-agnostic.

When to Use

When new knowledge (a document, paper, URL, config file, or directory of files) needs to enter a consumer's semantic memory. This is the primary write path for external information.

Parameters

source (required)

Path to the source material. Supports: markdown (.md), PDF (.pdf), HTML (.html), YAML (.yaml/.yml), JSON (.json), a directory of files, or a URL.

--consumer (optional)

Consumer ID to ingest into. Resolved via ADR-021 D4 precedence:

1. Explicit — --consumer research-complete
2. Wrapper — set by a calling skill or orchestrator
3. Auto-detect — cwd detection or active framework in .aiwg/frameworks/registry.json

--dry-run (optional)

Preview what would be created/modified without writing any files. Outputs the planned page list, cross-references, and contradiction flags.

--non-interactive (optional)

Skip the discussion step and proceed directly to extraction and page writing. Use for batch ingestion or CI pipelines.

Operation

1. Resolve consumer

Determine which consumer's memory to target using ADR-021 D4 precedence. Fail with a clear error if no consumer can be resolved.

2. Load schema

Read memory.topology from the consumer's manifest.json. Extract:

• rootDir — base path for all memory pages
• derivedPages.summary — where summary pages are written
• pageTemplate — structure the summary must conform to
• crossRefStyle — how cross-references are formatted (e.g., wiki-links, markdown links)
• indexPath — location of the consumer's memory index
• log — path to .log.jsonl
• ingestRequires — optional list of required post-ingest actions (e.g., "provenance")

3. Read source

Parse the source material based on type:

• Markdown/HTML — extract text, headings, and structure
• PDF — extract text content (use page ranges for large documents)
• YAML/JSON — parse structured data, identify key entities
• Directory — recursively read all supported files, treating each as a sub-source
• URL — fetch content, then parse based on content type

4. Discuss (interactive default)

Default behavior (no --non-interactive flag):

1. Present a concise summary of the source to the user
2. Highlight key takeaways, entities, and concepts found
3. Ask the user what to emphasize, de-prioritize, or reframe
4. Incorporate user guidance into the extraction strategy

This discussion-first pattern ensures the memory reflects human judgment, not just mechanical extraction.

5. Extract and summarize

Use LLM to produce a structured summary conforming to the consumer's pageTemplate. The summary captures:

• Key claims and findings
• Named entities (people, systems, concepts)
• Relationships between entities
• Source metadata (title, author, date, URI)

6. Integrate

• Write summary page to derivedPages.summary path
• Update entity/concept pages — for each entity or concept mentioned, update or create the relevant page under the consumer's entity directory, adding the new information with source attribution
• Insert cross-references — link the summary page to entity pages and vice versa, using the consumer's crossRefStyle

7. Contradiction detection

Compare new claims against existing pages. When a contradiction is found:

• Flag inline on the affected existing page using a callout:

  > [!contradiction]
  > Source "paper.pdf" (2026-04-14) claims X, but this page states Y.
  > Ingested via memory-ingest — awaiting human resolution.
  

• Log the contradiction in .log.jsonl with "contradictions" count and details
• Do not auto-resolve — surface contradictions for human judgment

8. Update index

Regenerate the entry for the new summary page in the consumer's index at indexPath. Include title, source reference, date, and cross-ref targets.

9. Append log

Call memory-log-append with:

--consumer <resolved> --op ingest --data '{"source":"<path>","pages_touched":[...],"contradictions":<n>,"cross_refs_added":<n>}'

10. Optional provenance

If ingestRequires includes "provenance", create a W3C PROV record documenting:

• prov:Entity — the new summary page
• prov:Activity — the ingest operation
• prov:wasDerivedFrom — the source material
• prov:wasGeneratedBy — this skill invocation
• prov:wasAttributedTo — the actor (model + user)

11. Report

Output a summary:

• Pages created or updated (with paths)
• Contradictions flagged (count and locations)
• Cross-references added (count)
• Provenance record path (if created)

Error Handling

• Consumer not found — fail with actionable message listing available consumers
• Source unreadable — fail with format-specific guidance (e.g., "PDF extraction requires the Read tool with page ranges")
• Schema missing fields — warn and use sensible defaults; log the gap for memory-lint to catch
• Log write failure — non-blocking; report primary operation result regardless

Examples

# Interactive ingest of a research paper
memory-ingest docs/papers/distributed-consensus.pdf --consumer research-complete

# Batch ingest a directory of meeting notes
memory-ingest .aiwg/working/meeting-notes/ --consumer sdlc-complete --non-interactive

# Dry run to preview what would change
memory-ingest https://example.com/api-spec.html --consumer sdlc-complete --dry-run

# Explicit consumer override
memory-ingest design-doc.md --consumer media-marketing-kit --non-interactive

Related Skills

• memory-log-append — log write primitive (called in step 9)
• memory-lint — validates memory page structure and cross-ref integrity
• memory-query-capture — captures query patterns for memory optimization
• provenance-create — W3C PROV record creation (called in step 10 when required)

Storage Routing (#934, #966)

This skill's persistence flows through resolveStorage('memory'). On the default fs backend the memory subsystem lives at .aiwg/memory/ and behavior is byte-identical to direct file writes. To redirect memory artifacts into Obsidian, Logseq, Fortemi, or another backend without changing this skill, configure .aiwg/storage.config (#934).

When this skill needs to read or write memory artifacts from a Bash step:

aiwg memory path                        # resolved root (fs only)
aiwg memory list --prefix research-complete/
aiwg memory get research-complete/index.md
echo "# index" | aiwg memory put research-complete/index.md
echo '{"op":"ingest","summary":"foo"}' | aiwg memory append-log research-complete/.log.jsonl

The aiwg memory append-log subcommand uses atomic O_APPEND (#976) on the fs backend — concurrent appenders don't race.