verify

name	verify
description	Assess note quality and verify claims against cited sources. Usage: /learning-loop:verify "note-name" | inbox | permanent | "topic" (defaults to inbox). Scores quality, checks source integrity, detects duplicates: produces fix plan.

Verify: Note Quality and Source Integrity

Overview

Assesses vault notes on two dimensions: structural quality (depth, sourcing, linking, voice, atomicity) and source truthfulness (are URLs real, do claims match citations). Produces a combined report and fix plan that feeds into /deepen.

When to Use

• /verify "note-name": single note
• /verify inbox: everything in 0-inbox/
• /verify permanent: everything in 3-permanent/
• /verify "topic": all notes matching a topic
• /verify: defaults to 0-inbox/
• After a burst of captures to check what landed well
• Before promoting notes from inbox to permanent
• When deciding where to invest /deepen effort
• When you suspect fabricated or stale references

Provenance

This skill emits provenance events for pipeline observability.

At session start (after scope identified):

node "${CLAUDE_PLUGIN_ROOT}/scripts/provenance-emit.js" '{"agent":"verify","skill":"verify","action":"session-start","intent":"SCOPE","config":{"note_count":N}}'

After scoring and verification, emit each finding via provenance-emit.js:

For each note with issues, run:

node "PLUGIN/scripts/provenance-emit.js" '{"agent":"verify","skill":"verify","action":"score","target":"note-filename.md","result":"fail","finding_type":"overclaim","finding_detail":"single RCT stated as consensus","trigger":"verify-manual","confidence":"clear","ambiguous_alt":""}'

Where:

• finding_type is one of: url-fabrication, author-swap, number-reassignment, overclaim, source-missing, stale, logical-gap, conflation. See agents/_skills/capture-rules.md → Finding-Type Discriminator for the source-missing vs logical-gap boundary rule.
• trigger is one of: verify-auto (URL/source check), verify-manual (human review), cross-note (pattern across notes), retrieval (found during search), stale-scan (date check)
• confidence is clear (obvious classification) or ambiguous (could be another type)
• ambiguous_alt is the alternative type considered when confidence is ambiguous, empty string when clear

For quality scores, emit one event per note:

node "PLUGIN/scripts/provenance-emit.js" '{"agent":"verify","skill":"verify","action":"score","target":"note-filename.md","tier":"deep","gate":"6/6","claim_specificity":2,"source_grounded":2}'

A note with no finding events is a pass.

Then emit session-end:

node "${CLAUDE_PLUGIN_ROOT}/scripts/provenance-emit.js" '{"agent":"verify","skill":"verify","action":"session-end","notes_checked":N,"notes_flagged":N,"findings_total":N,"fixes_applied":N}'

A note with zero score records is a pass.

Process

Step 0: Parameter Resolution

No argument (/verify): Use AskUserQuestion:

What would you like to verify?

• inbox: check all inbox notes (default)
• permanent: check all permanent notes
• "note-name": check a specific note
• "topic": check all notes matching a topic

Argument provided: Proceed immediately.

Step 1: Identify Scope

Input	Scope
/verify "note-name"	Single note
/verify inbox	Everything in 0-inbox/
/verify permanent	Everything in 3-permanent/
/verify "topic"	All notes matching the topic across the vault
/verify	Default to 0-inbox/

Step 2: Gather Notes

• For single note: Glob for **/<note-name>*.md in {{VAULT}}/, Read it
• For folder-based: Glob for *.md in the target folder
• For topic-based: Grep with path: "{{VAULT}}/" and pattern: "<topic>" + Glob for filenames + node PLUGIN/scripts/vault-search.mjs search "<topic>" --rerank for semantic matches. Deduplicate results.

Read each note.

Step 3: Quality Scoring (Parallel Subagents)

Spawn note-scorer agent(s) to assess the gathered notes. When spawning multiple agents, dispatch them all in the same turn (a single message with multiple Agent tool calls):

• < 10 notes: Single note-scorer agent with all file paths.
• 10-99 notes: Split into batches of ~10. Spawn one note-scorer agent per batch in the same turn.
• >= 100 notes (sweep): Split into batches of ~50. Spawn one note-scorer agent per batch in the same turn. Haiku handles 50 notes per batch; the bottleneck is Read calls, not reasoning.

Each agent reads its own notes, applies promote-gate assessment (6-criterion pass/fail + scoring mode dimensions), and returns per-note gate results (N/6 pass count, claim_specificity 0-2, source_grounded 0-2) with a maturity tier (shallow/medium/deep).

Wait for all scoring agents to complete before proceeding.

Step 3.5: Emit Scores to Provenance

After all scorer agents return, parse their results and emit one provenance event per note via provenance-emit.js. Run all emit calls in a single Bash command (chained with &&) to avoid excessive tool calls:

node "PLUGIN/scripts/provenance-emit.js" '{"agent":"verify","skill":"verify","action":"score","target":"note-1.md","tier":"deep","gate":"6/6","claim_specificity":2,"source_grounded":2}' && \
node "PLUGIN/scripts/provenance-emit.js" '{"agent":"verify","skill":"verify","action":"score","target":"note-2.md","tier":"shallow","gate":"2/6","claim_specificity":0,"source_grounded":0}'

This closes the subagent provenance gap -- scorer agents return text results, the main thread emits them to the provenance system.

Step 4: Consistency Detection

Two-source check: embeddings find topical similarity; NLI finds logical relation. NLI is the stronger signal when present; embeddings are the fallback.

1. NLI edges (high-confidence first). Open edges.db from PLUGIN_DATA/edges.db (see scripts/lib/edges.mjs). For each assessed note, call getNliEdgesForNote(db, notePath, 0.75). Filter to edgeType === 'challenges_rebuttal'. Bucket:

   • confidenceScore >= NLI_HARD_THRESHOLD (default 0.95) → high-confidence contradiction
   • NLI_TENSION_THRESHOLD <= confidenceScore < NLI_HARD_THRESHOLD (default 0.75–0.95) → advisory tension
   • edgeType === 'nli_supports' → silent (entailment is not a /verify concern)

2. Embedding similarity (fallback). For each note, run node PLUGIN/scripts/vault-search.mjs similar "<note-path>" --top 5. Read the top similar notes (score > 0.7). Flag two types:

   • Near-duplicates (similarity > 0.85): notes covering the same insight with different wording. Recommend merge candidate (flag for user).
   • Potential contradiction (similarity 0.7–0.85, conflicting claims, no NLI edge): notes that look related but opposed. Recommend review.

3. Deduplicate. If a pair surfaces from both sources, present the NLI verdict and append the cosine for context: (NLI p=0.97, cosine 0.84). NLI hits sort above cosine-only hits because the signal is stronger.

If getNliEdgesForNote throws or returns nothing (DB locked, fresh vault, daemon offline), log via the existing hook-error pattern and proceed with embedding-only. Graceful degrade. Note the absence in the report so the user knows nothing was checked.

Step 5: Source Verification (Parallel Subagents)

Filter notes to those with sources/citations. Skip sourceless notes (report as "no sources: skipped").

Spawn note-verifier agent(s). When spawning multiple, dispatch them all in the same turn:

• < 5 notes with sources: Single agent with all note contents
• >= 5 notes with sources: Split into batches of ~5. Spawn one agent per batch in the same turn. (Verification involves URL fetching which is slow.)

Each agent receives the note content and returns the structured verification report (source checks, claim checks, missing citations, corrections).

Step 6: Present Report

Merge outputs from all agents into a single report:

## Verify: [scope]

### Summary
- N notes assessed
- Quality: Deep N | Medium N | Shallow N
- Sources: Pass N | Issues N | Skipped (no sources) N

### Quality Scores
| Note | Tier | Gate | Specificity | Grounded | Issues |
|------|------|------|-------------|----------|--------|
| [[note]] | shallow | 2/6 (missing: sourcing, voice, source integrity, depth) | 0 | 0 | no sources, topic-as-title |

### Consistency
- [[note-A]] ↔ [[note-B]] (NLI contradiction p=0.97, cosine 0.84): high-confidence conflict, run /rewrite or mark as deliberate
- [[note-C]] ↔ [[note-D]] (NLI tension p=0.81): advisory, may be worth reading together
- [[note-E]] ↔ [[note-F]] (cosine 0.91): near-duplicate, merge candidate
- [[note-G]] ↔ [[note-H]] (cosine 0.78): potential contradiction (embedding-only): [specific conflict]

### Source Issues
#### [[note-name]]: N issues
| Type | Detail | Severity |
|------|--------|----------|
| Dead URL | [url] returns 404 | high |
| Unsupported claim | "[claim]": source actually says [what] | high |
| Missing citation | "[claim]" has no source | medium |

### Clean
- [[note-name]]: all sources verified

Notes with wrong_author or fabricated sources should be flagged in the top section regardless of quality score: a well-written note with fabricated sources is worse than a thin note with real ones.

Step 7: Fix Plan

Prioritize notes by combined quality + source issues:

1. High priority: fabricated references, dead URLs, unsupported claims, NLI hard contradictions (p ≥ 0.95)
2. Medium priority: shallow notes with potential, missing citations, NLI advisory tensions (0.75 ≤ p < 0.95)
3. Low priority: minor voice issues, weak links

## Fix Plan (prioritized)
1. [[note-name]]: fabricated source + shallow → `/deepen "note-name"`
2. [[note-name]]: 2 dead URLs → `/deepen "note-name"`
3. [[note-name]]: shallow, no links → `/deepen "note-name"`

For each note needing work, suggest the right tool:

Issue	Recommendation
Thin, needs research	/deepen "note-name"
Missing sources	/literature to capture, then link
Covers multiple ideas	Split (manual or via /deepen)
Wrong folder for maturity	Promote or demote
Duplicate of another note	Merge candidate: flag for user

Step 7.5: Detect Promotion Clusters

Group scored notes by filename prefix to find coherent knowledge clusters ready for batch promotion.

Detection method:

1. Extract prefix from each filename: the portion before the first hyphen that follows a recognizable word (e.g., zustand-*, gemini-*, cbc-*, apollo-*, concept-creep-*)
2. Group notes by prefix. Keep only clusters with 3+ notes.
3. For each cluster, compute: total notes, count at deep tier, percentage deep.
4. Surface clusters where >80% of notes score deep.

Present as:

### Promotion Clusters Detected
| Cluster | Notes | Deep | % | Action |
|---------|-------|------|---|--------|
| zustand | 12 | 12 | 100% | promote all? |
| gemini | 6 | 6 | 100% | promote all? |
| cbc | 4 | 3 | 75% | review 1 outlier |

On approval, mv all qualifying files from the main thread (not subagents) so the PostToolUse hook captures the promotions. Log a batch provenance event:

node "${CLAUDE_PLUGIN_ROOT}/scripts/provenance-emit.js" '{"agent":"verify","skill":"verify","action":"batch-promote","cluster":"CLUSTER_NAME","count":N,"from":"1-fleeting","to":"3-permanent"}'

Clusters below the 80% threshold are reported but not offered for batch promotion. Individual deep notes within those clusters can still be promoted in the normal batch actions step.

Step 8: Offer Batch Actions

Quick actions available:
- Promote N deep notes to 3-permanent/ (say "promote all")
- Fix N notes with source issues (say "fix all" or pick specific notes)
- Review N consistency issues (contradictions/duplicates)
- Flag N shallow notes for /deepen queue

Execute promotions freely. Merges and deletions require user approval.

If user approves fixing:

• Run /deepen sequentially on each flagged note
• Pass the verification report as context so deepen knows what to focus on

Subagent Usage

note-scorer

Scores a batch of vault notes using promote-gate scoring mode.

Launch pattern:

Agent (subagent_type: "learning-loop:note-scorer"):
  "Score these notes: <file-path-1>, <file-path-2>, ...
   Return per-note: dimension scores + maturity tier (shallow/medium/deep) + specific issues found."

Batching rules:

• One agent handles up to ~10 notes.
• For larger sets, split evenly and launch multiple agents in parallel.

note-verifier

Verifies source URLs, checks claims against cited sources, catches fabrication.

Launch pattern:

Agent (subagent_type: "learning-loop:note-verifier"):
  "Verify these notes:
   Note 1: <path>
   <content>

   Note 2: <path>
   <content>

   Return per-note: source checks, claim checks, missing citations, corrections."

Batching rules:

• One agent handles up to ~5 notes (URL fetching is slow)
• For larger sets, split evenly and launch multiple agents in parallel

Verification Markers

Notes processed by the updated note-writer may contain inline markers from write-time verification:

• [unresolved] -- source not found in any database. Verify manually: search the web, check if it's a non-academic source.
• [unverified] -- source found but metadata mismatch persisted. Run verify-note on the note and inspect the specific issue.
• [not in abstract] -- number not confirmable from abstract alone. Fetch the full text if accessible, or check the number against the source page via web fetch.

When reporting, include marker counts in the summary. These markers indicate the write-time check already ran. Focus verification effort on resolving the markers rather than re-checking what already passed.

Key Principles

• Verify, don't rewrite. Report issues. Fixing is /deepen's job.
• Read every note. No scoring from titles alone.
• Skip sourceless notes for verification. Notes with no citations have nothing to verify: still score their quality.
• URL checks are mandatory. Every URL gets fetched. Dead links are high severity.
• Be specific. "Source doesn't support claim" is useless. Say what the source actually says.
• Shallow is not bad. A shallow note is a seed. Verify identifies where to invest, not what to discard.
• Promotions are free. If a note meets the bar for permanent, move it without asking.
• Merges and deletes need approval. Always ask before combining or removing notes.