reflect

name	reflect
description	Find connections between notes and update MOCs. Requires semantic judgment to identify genuine relationships. Use after /reduce creates notes, when exploring connections, or when a topic needs synthesis. Triggers on "/reflect", "/reflect [note]", "find connections", "update MOCs", "connect these notes". Use when this capability is needed.
metadata	{"author":"agenticnotetaking"}

Runtime Configuration (Step 0 — before any processing)

Read these files to configure domain-specific behavior:

1. ops/derivation-manifest.md — vocabulary mapping, platform hints

   • Use vocabulary.notes for the notes folder name
   • Use vocabulary.note / vocabulary.note_plural for note type references
   • Use vocabulary.reflect for the process verb in output
   • Use vocabulary.topic_map / vocabulary.topic_map_plural for MOC references
   • Use vocabulary.cmd_reweave for the next-phase suggestion
   • Use vocabulary.inbox for the inbox folder name

2. ops/config.yaml — processing depth, pipeline chaining

   • processing.depth: deep | standard | quick
   • processing.chaining: manual | suggested | automatic

If these files don't exist, use universal defaults.

Processing depth adaptation:

Depth	Connection Behavior
deep	Full dual discovery (MOC + semantic search). Evaluate every candidate. Multiple passes. Synthesis opportunity detection. Bidirectional link evaluation for all connections.
standard	Dual discovery with top 5-10 candidates. Standard evaluation. Bidirectional check for strong connections only.
quick	Single pass — either MOC or semantic search. Accept obvious connections only. Skip synthesis detection.

EXECUTE NOW

Target: $ARGUMENTS

Parse immediately:

• If target contains [[note name]] or note name: find connections for that {vocabulary.note}
• If target contains --handoff: output RALPH HANDOFF block at end
• If target is empty: check for recently created {vocabulary.note_plural} or ask which {vocabulary.note}
• If target is "recent" or "new": find connections for all {vocabulary.note_plural} created today

Execute these steps:

1. Read the target {vocabulary.note} fully — understand its claim and context
2. Throughout discovery: Capture which {vocabulary.topic_map_plural} you read, which queries you ran (with scores), which candidates you evaluated. This becomes the Discovery Trace — proving methodology was followed, not reconstructed.
3. Run Phase 0 (index freshness check)
4. Use dual discovery in parallel:
   • Browse relevant {vocabulary.topic_map}(s) for related {vocabulary.note_plural}
   • Run semantic search for conceptually related {vocabulary.note_plural}
5. Evaluate each candidate: does a genuine connection exist? Can you articulate WHY?
6. Add inline wiki-links where connections pass the articulation test
7. Update relevant {vocabulary.topic_map}(s) with this {vocabulary.note}
8. If task file in context: update the {vocabulary.reflect} section
9. Report what was connected and why
10. If --handoff in target: output RALPH HANDOFF block

START NOW. Reference below explains methodology — use to guide, not as output.

---

Reflect

Find connections, weave the knowledge graph, update {vocabulary.topic_map_plural}. This is the forward-connection phase of the processing pipeline.

Philosophy

The network IS the knowledge.

Individual {vocabulary.note_plural} are less valuable than their relationships. A {vocabulary.note} with fifteen incoming links is an intersection of fifteen lines of thought. Connections create compound value as the vault grows.

This is not keyword matching. This is semantic judgment — understanding what {vocabulary.note_plural} MEAN to determine how they relate. A {vocabulary.note} about "friction in systems" might deeply connect to "verification approaches" even though they share no words. You are building a traversable knowledge graph, not tagging documents.

Quality over speed. Explicit over vague.

Every connection must pass the articulation test: can you say WHY these {vocabulary.note_plural} connect? "Related" is not a relationship. "Extends X by adding Y" or "contradicts X because Z" is a relationship.

Bad connections pollute the graph. They create noise that makes real connections harder to find. When uncertain, do not connect.

Invocation Patterns

/reflect (no argument)

Check for recent additions:

1. Look for {vocabulary.note_plural} modified in the last session
2. If none obvious, ask user what {vocabulary.note_plural} to connect

/reflect [note]

Focus on connecting a specific {vocabulary.note}:

1. Read the target {vocabulary.note}
2. Discover related content
3. Add connections and update {vocabulary.topic_map_plural}

/reflect [topic area]

Synthesize an area:

1. Read the relevant {vocabulary.topic_map}
2. Identify {vocabulary.note_plural} that should connect
3. Weave connections, update synthesis

/reflect --handoff [note]

External loop mode for /ralph:

• Execute full workflow as normal
• At the end, output structured RALPH HANDOFF block
• Used when running isolated phases with fresh context per task

Workflow

Phase 0: Verify Index Freshness

Before using semantic search, verify the index is current. This is self-healing: if {vocabulary.note_plural} were created outside the pipeline (manual edits, other skills), reflect catches the drift before searching.

1. Try mcp__qmd__status to get the indexed document count for the target collection
2. If MCP unavailable (tool fails or returns error): fall back to bash:

   LOCKDIR="ops/queue/.locks/qmd.lock"
   while ! mkdir "$LOCKDIR" 2>/dev/null; do sleep 2; done
   qmd_count=$(qmd status 2>/dev/null | grep -A2 '{vocabulary.notes_collection}' | grep 'documents' | grep -oE '[0-9]+' | head -1)
   rm -rf "$LOCKDIR"
   

3. Count actual files:

   file_count=$(ls -1 {vocabulary.notes}/*.md 2>/dev/null | wc -l | tr -d ' ')
   

4. If the counts differ, sync the index:

   qmd update && qmd embed
   

Run this check before proceeding. If stale, sync and continue. If current, proceed immediately.

Phase 1: Understand What You Are Connecting

Before searching for connections, deeply understand the source material.

For each {vocabulary.note} you are connecting:

1. Read the full {vocabulary.note}, not just title and description
2. Identify the core claim and supporting reasoning
3. Note key concepts, mechanisms, implications
4. Ask: what questions does this answer? What questions does it raise?

What you are looking for:

• The central argument (what is being claimed?)
• The mechanism (why/how does this work?)
• The implications (what follows from this?)
• The scope (when does this apply? When not?)
• The tensions (what might contradict this?)

If a task file exists (pipeline execution): read the task file to see what the extraction phase discovered. The reduce notes, semantic neighbor field, and classification provide critical context about why this {vocabulary.note} was extracted and what it relates to.

Phase 2: Discovery (Find Candidates)

Use dual discovery: {vocabulary.topic_map} exploration AND semantic search in parallel. These are complementary, not sequential.

Capture discovery trace as you go. Note which {vocabulary.topic_map_plural} you read, which queries you ran (with scores), which searches you tried. This becomes the Discovery Trace section in output — proving methodology was followed, not reconstructed after the fact.

Primary discovery (run in parallel):

Path 1: {vocabulary.topic_map} Exploration — curated navigation

If you know the topic (check the {vocabulary.note}'s Topics footer), start with the {vocabulary.topic_map}:

• Read the relevant {vocabulary.topic_map}(s)
• Follow curated links in Core Ideas — these are human/agent-curated connections
• Note what is already connected to similar concepts
• Check Tensions and Gaps for context
• What do agent notes reveal about navigation?

{vocabulary.topic_map_plural} tell you what thinking exists and how it is organized. Someone already decided what matters for this topic.

Path 2: Semantic Search — find what {vocabulary.topic_map_plural} might miss

Three-tier fallback for semantic search:

Tier 1 — MCP tools (preferred): Use mcp__qmd__deep_search (hybrid search with expansion + reranking):

• query: "[{vocabulary.note}'s core concepts and mechanisms]"
• limit: 15

Tier 2 — bash qmd with lock serialization: If MCP tools fail or are unavailable:

LOCKDIR="ops/queue/.locks/qmd.lock"
while ! mkdir "$LOCKDIR" 2>/dev/null; do sleep 2; done
qmd query "[note's core concepts]" --collection {vocabulary.notes_collection} --limit 15 2>/dev/null
rm -rf "$LOCKDIR"

The lock prevents multiple parallel workers from loading large models simultaneously.

Tier 3 — grep only: If both MCP and bash fail, log "qmd unavailable, grep-only discovery" and rely on {vocabulary.topic_map} + keyword search only. This degrades quality but does not block work.

Evaluate results by relevance — read any result where title or snippet suggests genuine connection. Semantic search finds {vocabulary.note_plural} that share MEANING even when vocabulary differs. A {vocabulary.note} about "iteration cycles" might connect to "learning from friction" despite sharing no words.

Why both paths:

{vocabulary.topic_map} = what is already curated as relevant semantic search = neighbors that have not been curated yet

Using only search misses curated structure. Using only {vocabulary.topic_map} misses semantic neighbors outside the topic. Both together catch what either alone would miss.

Secondary discovery (after primary):

Step 3: Keyword Search

For specific terms and exact matches:

grep -r "term" {vocabulary.notes}/ --include="*.md"

Use grep when:

• You know the exact words that should appear
• Searching for specific terminology or phrases
• Finding all uses of a named concept
• The vocabulary is stable and predictable

Choosing between semantic and keyword:

Situation	Better Tool	Why
Exploring unfamiliar territory	semantic	vocabulary might not match meaning
Finding synonyms or related framings	semantic	same concept, different words
Known terminology	keyword	exact match, no ambiguity
Verifying coverage	keyword	ensures nothing missed
Cross-domain connections	semantic	concepts bridge domains, words do not
Specific phrase lookup	keyword	faster, more precise

Step 4: Description Scan

Use ripgrep to scan {vocabulary.note} descriptions for edge cases:

• Does this extend the source {vocabulary.note}?
• Does this contradict or create tension?
• Does this provide evidence or examples?

Flag candidates with a reason (not just "related").

Step 5: Link Following

From promising candidates, follow their existing links:

• What do THEY connect to?
• Are there clusters of related {vocabulary.note_plural}?
• Do chains emerge that your source {vocabulary.note} should join?

This is graph traversal. You are exploring the neighborhood.

Phase 3: Evaluate Connections

For each candidate connection, apply the articulation test.

The Articulation Test:

Complete this sentence:

[[note A]] connects to [[note B]] because [specific reason]

If you cannot fill in [specific reason] with something substantive, the connection fails.

Valid Relationship Types:

Relationship	Signal	Example
extends	adds dimension	"extends [[X]] by adding temporal aspect"
grounds	provides foundation	"this works because [[Y]] establishes..."
contradicts	creates tension	"conflicts with [[Z]] because..."
exemplifies	concrete instance	"demonstrates [[W]] in practice"
synthesizes	combines insights	"emerges from combining [[A]] and [[B]]"
enables	unlocks possibility	"makes [[C]] actionable by providing..."

Reject if:

• The connection is "related" without specifics
• You found it through keyword matching alone with no semantic depth
• Linking would confuse more than clarify
• The relationship is too obvious to be useful

Agent Traversal Check:

Ask: "If an agent follows this link, what do they gain?"

Agent Benefit	Keep Link
Provides reasoning foundation (why something works)	YES
Offers implementation pattern (how to do it)	YES
Surfaces tension to consider (trade-off awareness)	YES
Gives concrete example (grounds abstraction)	YES
Just "related topic" with no decision value	NO

The vault is built for agent traversal. Every connection should help an agent DECIDE or UNDERSTAND something. Connections that exist only because they feel "interesting" without operational value are noise.

Synthesis Opportunity Detection:

While evaluating connections, watch for synthesis opportunities — two or more {vocabulary.note_plural} that together imply a higher-order claim not yet captured.

Signs of a synthesis opportunity:

• Two {vocabulary.note_plural} make complementary arguments that combine into something neither says alone
• A pattern appears across three or more {vocabulary.note_plural} that has not been named
• A tension between two {vocabulary.note_plural} suggests a resolution claim

When you detect a synthesis opportunity:

1. Note it in the output report
2. Do NOT create the synthesis {vocabulary.note} during reflect — flag it for future work
3. Describe what the synthesis would argue and which {vocabulary.note_plural} contribute

Phase 4: Add Inline Connections

Connections live in the prose, not just footers.

Inline Links as Prose:

The wiki link IS the argument. The title works as prose when linked.

Good patterns:

Since [[other note]], the question becomes how to structure that memory for retrieval.

The insight that [[throughput matters more than accumulation]] suggests curation, not creation, is the real work.

This works because [[good systems learn from friction]] — each iteration improves the next.

Bad patterns:

This relates to [[other note]].

See also [[throughput matters more than accumulation]].

As discussed in [[good systems learn from friction]], systems improve.

If you catch yourself writing "this relates to" or "see also", STOP. Restructure so the claim does the work.

Where to add links:

1. Inline in the body where the connection naturally fits the argument
2. In the relevant_notes YAML field with context phrase
3. BOTH when the connection is strong enough

Relevant Notes Format:

relevant_notes:
  - "[[note title]] — extends this by adding the temporal dimension"
  - "[[another note]] — provides the mechanism this claim depends on"

Context phrases use standard relationship vocabulary: extends, grounds, contradicts, exemplifies, synthesizes, enables.

Bidirectional Consideration:

When adding [[A]] to [[B]], ask: should [[B]] also link to [[A]]?

Not always. Relationships are not always symmetric:

• "extends" often is not bidirectional
• "exemplifies" usually goes one direction
• "contradicts" is often bidirectional
• "synthesizes" might reference both sources

Add the reverse link only if following that path would be useful for agent traversal.

Reweave Task Filtering (when adding bidirectional links):

When you edit an older {vocabulary.note} to add a reverse link, you MAY flag it for full reconsideration via reweave. But SKIP reweave flagging if ANY of these apply:

Skip Condition	Rationale
Note has >5 incoming links	Already a hub — one more link does not warrant full reconsideration
Note has type: tension in YAML	Structural framework, not content that evolves
Note was reweaved in current batch	Do not re-reweave what was just reweaved
Note is a {vocabulary.topic_map}	{vocabulary.topic_map_plural} are navigation, not claims to reconsider

Check incoming links:

grep -r '\[\[note name\]\]' {vocabulary.notes}/*.md | wc -l

If >= 5, skip reweave flagging.

Phase 5: Update {vocabulary.topic_map_plural}

{vocabulary.topic_map_plural} are synthesis hubs, not just indexes.

When to update a {vocabulary.topic_map}:

• New {vocabulary.note} belongs in Core Ideas
• New tension discovered
• Gap has been filled
• Synthesis insight emerged
• Navigation path worth documenting

{vocabulary.topic_map} Size Check:

After updating Core Ideas, count the links:

grep -c '^\- \[\[' "{vocabulary.notes}/[moc-name].md"

If approaching the split threshold (configurable, default ~40): note in output "{vocabulary.topic_map} approaching split threshold (N links)" If exceeding: warn "{vocabulary.topic_map} exceeds recommended size — consider splitting"

Splitting is a human decision (architectural judgment required), but /reflect should surface the signal.

{vocabulary.topic_map} Structure:

# [Topic Name]

[Opening synthesis: Claims about the topic. Not "this {vocabulary.topic_map} collects {vocabulary.note_plural}" but "the core insight is Y because Z." This IS thinking, not meta-description.]

## Core Ideas

- [[claim note]] — what it contributes to understanding
- [[another claim]] — how it fits or challenges existing ideas

## Tensions

- [[claim A]] and [[claim B]] conflict because... [genuine unresolved tension]

## Gaps

- nothing about X aspect yet
- need concrete examples of Y
- missing: comparison with Z approach

---

Agent Notes:
- YYYY-MM-DD: [what was explored]. [the insight or dead end].

Updating Core Ideas:

Add new {vocabulary.note_plural} with context phrase explaining contribution:

- [[new note]] — extends the quality argument by showing how friction teaches you what to check

Order matters. Place {vocabulary.note_plural} where they fit the logical flow, not alphabetically.

Updating Tensions:

If the new {vocabulary.note} creates or resolves tension:

## Tensions

- [[composability]] demands small notes, but [[context limits]] means traversal has overhead. [[new note]] suggests the tradeoff depends on expected traversal depth.

Document genuine conflicts. Tensions are valuable, not bugs.

Updating Gaps:

Remove gaps that are now filled. Add new gaps discovered during reflection.

Phase 6: Add Agent Notes

Agent notes are breadcrumbs for future navigation.

Add agent notes when:

• Non-obvious navigation path discovered
• Dead end worth documenting
• Productive {vocabulary.note} combination found
• Insight about topic cluster emerged

Format:

Agent Notes:
- YYYY-MM-DD: [what was explored]. [the insight or finding].

Good agent notes:

- 2026-02-15: tried connecting via "learning" — too generic. better path: friction -> verification -> quality. the mechanism chain is tighter.
- 2026-02-15: [[claim A]] and [[claim B]] form a tight pair. A sets the standard, B teaches the method.

Bad agent notes:

- 2026-02-15: read the {vocabulary.topic_map} and added some links.
- 2026-02-15: connected [[note A]] to [[note B]].

The test: would this help a future agent navigate more effectively?

Quality Gates

Gate 1: Articulation Test

For every connection added, can you complete:

[[A]] connects to [[B]] because [specific reason]

If any connection fails this test, remove it.

Gate 2: Prose Test

For every inline link, read the sentence aloud. Does it flow naturally? Would you say this to a friend explaining the idea?

Bad: "this is related to [[note]]" Good: "since [[note]], the implication is..."

Gate 3: Bidirectional Check

For every A -> B link, explicitly decide: should B -> A exist? Document your reasoning if the relationship is asymmetric.

Gate 4: {vocabulary.topic_map} Coherence

After updating a {vocabulary.topic_map}, read the opening synthesis. Does it still hold? Do new {vocabulary.note_plural} extend or challenge it?

If the synthesis is now wrong or incomplete, update it.

Gate 5: Link Verification

Verify every wiki link target exists. Never create links to non-existent files.

# Check that a link target exists
ls {vocabulary.notes}/"target name.md" 2>/dev/null

Handling Edge Cases

No Connections Found

Sometimes a {vocabulary.note} genuinely does not connect yet. That is fine.

1. Ensure it is linked to at least one {vocabulary.topic_map} via Topics footer
2. Note in {vocabulary.topic_map} Gaps that this area needs development
3. Do not force connections that are not there

Too Many Connections (Split Detection)

If a {vocabulary.note} connects to 5+ {vocabulary.note_plural} across different domains, it might be too broad.

Split detection criteria:

1. Domain spread: Connections span 3+ distinct {vocabulary.topic_map_plural}/topic areas
2. Multiple claims: The {vocabulary.note} makes more than one assertion that could stand alone
3. Linking drag: You would want to link to part of the {vocabulary.note} but not all of it

How to evaluate:

Ask: "If I link to this {vocabulary.note} from context X, does irrelevant content Y come along?"

If yes, the {vocabulary.note} bundles multiple ideas that should be separate.

Split detection output:

### Split Candidate: [[broad note]]

**Indicators:**
- Connects to 7 {vocabulary.note_plural} across 3 domains
- Makes distinct claims about: (1) capture workflows, (2) synthesis patterns, (3) tool selection
- Linking from [[note A]] would drag in unrelated content about tool selection

**Proposed split:**
- [[capture workflows matter less than synthesis]] — the first claim
- [[tool selection follows from workflow needs]] — the third claim
- Keep original {vocabulary.note} focused on synthesis patterns

**Action:** Flag for human decision, do not auto-split

When NOT to split:

• {vocabulary.note} is genuinely about one thing that touches many areas
• Connections are all variations of the same relationship
• Splitting would create {vocabulary.note_plural} too thin to stand alone

Conflicting Notes

When new content contradicts existing {vocabulary.note_plural}:

1. Document the tension in both {vocabulary.note_plural}
2. Add to {vocabulary.topic_map} Tensions section
3. Do not auto-resolve — flag for judgment

Orphan Discovery

If you find {vocabulary.note_plural} with no connections:

1. Flag them in your output
2. Attempt to connect them
3. If genuinely orphaned, note in relevant {vocabulary.topic_map} Gaps

Output Format

After reflecting, report:

## Reflection Complete

### Discovery Trace

**Why this matters:** Shows methodology was followed. Blind delegation hides whether dual discovery happened. Trace enables verification.

**{vocabulary.topic_map} exploration:**
- Read [[moc-name]] — found candidates: [[note A]], [[note B]], [[note C]]
- Followed link from [[note A]] to [[note D]]

**Semantic search:** (via MCP | bash fallback | grep-only)
- query "[core concept from note]" — top hits:
  - [[note E]] (0.74) — evaluated: strong match, mechanism overlap
  - [[note F]] (0.61) — evaluated: weak, only surface vocabulary
  - [[note G]] (0.58) — evaluated: skip, different domain

**Keyword search:**
- grep "specific term" — found [[note H]] (already in {vocabulary.topic_map} candidates)

### Connections Added

**[[source note]]**
- -> [[target]] — [relationship type]: [why]
- <- [[incoming]] — [relationship type]: [why]
- inline: added link to [[note]] in paragraph about X

### {vocabulary.topic_map} Updates

**[[moc-name]]**
- Added [[note]] to Core Ideas — [contribution]
- Updated Tensions: [[A]] vs [[B]] now includes [[C]]
- Removed from Gaps: [what was filled]
- Agent note: [what was learned]

### Synthesis Opportunities

[{vocabulary.note_plural} that could be combined into higher-order insights, with proposed claim]

### Flagged for Attention

- [[orphan note]] — could not find connections
- [[broad note]] — might benefit from splitting
- Tension between [[X]] and [[Y]] needs resolution

What Success Looks Like

Successful reflection:

• Every connection passes the articulation test
• Inline links read as natural prose
• {vocabulary.topic_map_plural} gain synthesis, not just entries
• Agent notes reveal non-obvious paths
• The knowledge graph becomes more traversable
• Future agents will navigate more effectively

The test: if someone follows the links you added, do they find genuinely useful context? Does the path illuminate understanding?

Critical Constraints

Never:

• Create wiki links to non-existent files
• Add "related" connections without specific reasoning
• Force connections that are not there
• Auto-generate without semantic judgment
• Skip the articulation test

Always:

• Verify link targets exist
• Explain WHY connections exist
• Consider bidirectionality
• Update relevant {vocabulary.topic_map_plural}
• Add agent notes when navigation insights emerge
• Capture discovery trace as you work

The Network Grows Through Judgment

This skill is about building a knowledge graph that compounds in value. Every connection you add is a traversal path that future thinking can follow. Every connection you do not add keeps the graph clean.

Quality beats quantity. One genuine connection is worth more than ten vague ones.

The graph is not just storage. It is an external thinking structure. Build it with care.

---

Handoff Mode (--handoff flag)

When invoked with --handoff, output this structured format at the END of the session. This enables external loops (/ralph) to parse results and update the task queue.

Detection: Check if $ARGUMENTS contains --handoff. If yes, append this block after completing normal workflow.

Handoff format:

=== RALPH HANDOFF: {vocabulary.reflect} ===
Target: [[note name]]

Work Done:
- Discovery: {vocabulary.topic_map} [[moc-name]], query "[query]" (MCP|bash|grep-only), grep "[term]"
- Connections added: N (articulation test: PASS)
- {vocabulary.topic_map} updates: [[moc-name]] Core Ideas section
- Synthesis opportunities: [count or NONE]

Files Modified:
- {vocabulary.notes}/[note name].md (inline links added)
- {vocabulary.notes}/[moc-name].md (Core Ideas updated)
- [task file path] ({vocabulary.reflect} section)

Learnings:
- [Friction]: [description] | NONE
- [Surprise]: [description] | NONE
- [Methodology]: [description] | NONE
- [Process gap]: [description] | NONE

Queue Updates:
- Advance phase: {vocabulary.reflect} -> {vocabulary.reweave}
- Reweave candidates (if any pass filter): [[note]] | NONE (filtered: hub/tension/recent)
=== END HANDOFF ===

Task File Update (when invoked via ralph loop)

When running in handoff mode via /ralph, the prompt includes the task file path. After completing the workflow, update the ## {vocabulary.reflect} section of that task file with:

• Connections added and why
• {vocabulary.topic_map} updates made
• Articulation test results
• Discovery trace summary

Critical: The handoff block is OUTPUT, not a replacement for the workflow. Do the full reflect workflow first, update task file, then format results as handoff.

Queue Update (interactive execution)

When running interactively (NOT via /ralph), YOU must advance the phase in the queue. /ralph handles this automatically, but interactive sessions do not.

After completing the workflow, advance the phase:

# get timestamp
TIMESTAMP=$(date -u +"%Y-%m-%dT%H:%M:%SZ")

# advance phase (current_phase -> next, append to completed_phases)
jq '(.tasks[] | select(.id=="TASK_ID")).current_phase = "{vocabulary.reweave}" |
    (.tasks[] | select(.id=="TASK_ID")).completed_phases += ["{vocabulary.reflect}"]' \
    ops/queue/queue.json > tmp.json && mv tmp.json ops/queue/queue.json

The handoff block's "Queue Updates" section is not just output — it is your own todo list when running interactively.

Pipeline Chaining

After connection finding completes, output the next step based on ops/config.yaml pipeline.chaining mode:

• manual: Output "Next: {vocabulary.cmd_reweave} [note]" — user decides when to proceed
• suggested: Output next step AND advance task queue entry to current_phase: "{vocabulary.reweave}"
• automatic: Queue entry advanced and backward pass proceeds immediately

The chaining output uses domain-native command names from the derivation manifest.

---

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