Xpersona
agentCLAWHUBUnverified

doc-consolidation

Merges ephemeral report and analysis artifacts into permanent documentation Skill: doc-consolidation Owner: athola Summary: Merges ephemeral report and analysis artifacts into permanent documentation Tags: latest:1.9.19 Version history: v1.9.19 | 2026-08-26T13:20:09.156Z | user Release v1.9.19 v1.9.17 | 2026-07-30T05:40:14.609Z | user Release v1.9.17 v1.9.16 | 2026-07-14T19:57:07.565Z | user Release v1.9.16 v1.9.14 | 2026-06-30T18:05:05.720Z | user Release v1.9.14 v1.9.13 | 2026-06-27T16:22:

OpenClaw
View on ClawHubHomepage

Rank

62

Safety

84

Downloads

1.6k

Updated

Oct 10, 2026

Version

1.9.19

Source

CLAWHUB

About

What it does, and when to use it.

Capability contract not published. No trust telemetry is available yet. 1.6K downloads reported by the source. Last updated 10/10/2026.

Avoid when

  • Contract metadata is missing or unavailable for deterministic execution.

Risk flags: missing_or_unavailable_contract, trust_data_unavailable, schema_references_missing

Public facts

Every fact links back to the source it came from.

Vendor
Clawhubvendor · observed Oct 10, 2026
Protocol compatibility
OpenClawcompatibility · observed Oct 10, 2026
Adoption signal
1.6K downloadsadoption · observed Oct 10, 2026
Latest release
1.9.19release · observed Aug 26, 2026
Handshake status
UNKNOWNsecurity

Install and run

Setup complexity: low.

clawhub skill install s17emme0e2m3cpf7k2jvp3a84984b8z9:nm-sanctum-doc-consolidation
  1. Setup complexity is classified as HIGH. You must provision dedicated cloud infrastructure or an isolated VM. Do not run this directly on your local workstation.
  2. Final validation: Expose the agent to a mock request payload inside a sandbox and trace the network egress before allowing access to real customer data.

Contract: missing

curl -s "https://www.xpersona.co/api/v1/agents/clawhub-athola-nm-sanctum-doc-consolidation/snapshot"

Documentation

CLAWHUB

144,913 characters of source documentation, loaded on request.

Extracted files

5 files captured from the source.

SKILL.md

---
name: doc-consolidation
description: Merges ephemeral report and analysis artifacts into permanent documentation
version: 1.9.8
triggers:
  - docs
  - consolidation
  - cleanup
  - git-hygiene
  - knowledge-management
  - LLM-generated markdown files have accumulated and need consolidation
metadata: {"openclaw": {"homepage": "https://github.com/athola/claude-night-market/tree/master/plugins/sanctum", "emoji": "\ud83d\udcdd"}}
source: claude-night-market
source_plugin: sanctum
---

> **Night Market Skill** — ported from [claude-night-market/sanctum](https://github.com/athola/claude-night-market/tree/master/plugins/sanctum). For the full experience with agents, hooks, and commands, install the Claude Code plugin.


## Table of Contents

- [When to Use](#when-to-use)
- [Quick Start](#quick-start)
- [Two-Phase Workflow](#two-phase-workflow)
- [Phase 1: Triage (Fast Model)](#phase-1:-triage-(fast-model))
- [Phase 2: Execute (Main Model)](#phase-2:-execute-(main-model))
- [Workflow Details](#workflow-details)
- [Step 1: Candidate Detection](#step-1:-candidate-detection)
- [Step 2: Content Analysis](#step-2:-content-analysis)
- [Step 3: Destination Routing](#step-3:-destination-routing)
- [Step 4: Generate Plan](#step-4:-generate-plan)
- [Source: API_REVIEW_REPORT.md](#source:-api_review_reportmd)
- [Post-Consolidation](#post-consolidation)
- [Step 5: Execute Merges](#step-5:-execute-merges)
- [Fast Model Delegation](#fast-model-delegation)
- [Content Categories](#content-categories)
- [Merge Strategies](#merge-strategies)
- [Intelligent Weave](#intelligent-weave)
- [Replace Section](#replace-section)
- [Append with Context](#append-with-context)
- [Create New File](#create-new-file)
- [Integration](#integration)
- [Example Session](#example-session)
- [Troubleshooting](#troubleshooting)
- [No candidates found](#no-candidates-found)
- [Low-quality extractions](#low-quality-extractions)
- [Merge conflicts](#merge-conflicts)
- [Related Skills](#related-skills)


# Doc Consolidation

Extracts valuable knowledge from ephemeral LLM outputs and merges it into permanent documentation.

## When To Use

Use this skill when:
- You have untracked `*_REPORT.md` or `*_ANALYSIS.md` files from Claude sessions
- Git status shows markdown files that shouldn't be committed but contain useful content
- You want to preserve insights from code reviews, refactoring reports, or API audits
- Preparing a PR and need to clean up working artifacts

Do NOT use when:
- Files are already in proper documentation locations
  (`docs/`, `skills/`)
- Files are intentionally temporary scratch notes
- User explicitly wants to preserve the original report format
- Source files have no extractable value (pure log output)

## Formatting

When merging content into permanent documentation, follow
`Skill(leyline:markdown-formatting)` conventions: wrap prose
at 80 chars (prefer sentence/clause boundaries), blank lines
around headings, ATX headings only, blank line before lists,
and reference-

_meta.json

{
  "ownerId": "kn7d107jg9jv602h9ytsegydq184a42s",
  "slug": "nm-sanctum-doc-consolidation",
  "version": "1.9.19",
  "publishedAt": 1787750409156
}

modules/candidate-detection.md

# Candidate Detection Module

Identifies markdown files that are candidates for consolidation.

## Detection Signals

Apply signals in priority order. A file is a candidate if it matches **any** signal.

### Signal 1: Git-Untracked Location (Highest Priority)

```bash
# Find untracked .md files
git status --porcelain | grep '^??' | grep '\.md$'
```

**Exclude standard locations:**
- `docs/` - Already permanent documentation
- `skills/` - Skill definitions
- `modules/` - Skill modules
- `commands/` - Slash commands
- `agents/` - Agent definitions
- `.github/` - GitHub templates

**Exclude standard names:**
- `README.md`, `README`
- `LICENSE.md`, `LICENSE`
- `CONTRIBUTING.md`
- `CHANGELOG.md`, `HISTORY.md`
- `SECURITY.md`
- `CODE_OF_CONDUCT.md`

### Signal 2: ALL_CAPS Naming Pattern

Files with ALL_CAPS names that aren't standard conventions:

```
MATCHES (candidates):
- API_REVIEW_REPORT.md
- REFACTORING_REPORT.md
- MIGRATION_ANALYSIS.md
- AUDIT_FINDINGS.md
- *_REPORT.md
- *_ANALYSIS.md
- *_REVIEW.md
- *_FINDINGS.md

EXCLUDES (not candidates):
- README.md
- LICENSE.md
- CONTRIBUTING.md
- CHANGELOG.md
- SECURITY.md
- CODE_OF_CONDUCT.md
```

### Signal 3: Content Markers

Scan first 100 lines for LLM output markers:

**Strong markers (any one = candidate):**
- `**Date**:` or `Date:` at start of line
- `## Executive Summary`
- `## Summary` (at document start)
- `## Findings`
- `## Action Items`
- `## Recommendations`
- `## Conclusion`

**Supporting markers (need 2+ to qualify):**
- Markdown tables with `|` columns
- `### High Priority` / `### Medium Priority` / `### Low Priority`
- `- [ ]` checkbox lists
- `## 1.` numbered top-level sections
- `**Scope**:` or `**Status**:`
- Lines starting with status markers

## Detection Algorithm

```python
def detect_candidates(repo_path: str) -> list[CandidateFile]:
    candidates = []

    # Get untracked markdown files
    untracked = git_untracked_md_files(repo_path)

    for file_path in untracked:
        # Skip standard locations
        if is_standard_location(file_path):
            continue

        # Skip standard names
        if is_standard_name(file_path):
            continue

        score = 0
        reasons = []

        # Check naming pattern
        if is_allcaps_nonstandard(file_path):
            score += 3
            reasons.append("ALL_CAPS non-standard name")

        # Check content markers
        content = read_first_n_lines(file_path, 100)
        strong, supporting = count_content_markers(content)

        if strong > 0:
            score += 3
            reasons.append(f"Strong markers: {strong}")

        if supporting >= 2:
            score += 2
            reasons.append(f"Supporting markers: {supporting}")

        # Threshold: score >= 2
        if score >= 2:
            candidates.append(CandidateFile(
                path=file_path,
                score=score,
                reasons=reasons
            ))

    return sorted(candidates, key=lambda c: c.score, reverse=True

modules/content-analysis.md

# Content Analysis Module

Extracts and categorizes valuable content from candidate files.

## Content Categories

### Category Definitions

| Category | Description | Indicators |
|----------|-------------|------------|
| **Actionable Items** | Tasks, TODOs, next steps that require action | `Action Items`, `Next Steps`, `TODO`, `- [ ]` checkboxes |
| **Decisions Made** | Architecture choices, tradeoffs, rationale | `Decision`, `Chose`, `Tradeoff`, `Rationale`, `Why we` |
| **Findings/Insights** | Audit results, analysis conclusions, observations | `Findings`, `Observations`, `Analysis`, `Discovered`, `Noted` |
| **Metrics/Baselines** | Quantitative data, before/after, benchmarks | Tables with numbers, `Before`, `After`, percentages, `Improvement` |
| **Migration Guides** | Step-by-step procedures, how-to instructions | `Steps`, `How to`, `Migration`, numbered lists with commands |
| **API Changes** | Interface modifications, breaking changes, deprecations | `API`, `Breaking`, `Deprecated`, `New endpoint`, `Removed` |

### Extraction Process

For each candidate file:

1. **Parse structure** - Identify sections by headers (`##`, `###`)
2. **Extract chunks** - Each section becomes a content chunk
3. **Categorize** - Match chunk to best-fit category
4. **Score value** - Assess high/medium/low

## Value Scoring

### High Value
Content that is:
- **Specific**: Contains concrete names, paths, numbers
- **Actionable**: Reader can act on it directly
- **Unique**: Not already documented elsewhere

Examples:
- Specific action items with owners
- Concrete metrics (before: 287 lines, after: 255 lines)
- Explicit decisions with rationale
- Step-by-step procedures that worked

### Medium Value
Content that is:
- **Somewhat specific**: General guidance with some detail
- **Reference-worthy**: Useful for future lookups
- **Partially covered**: Extends existing documentation

Examples:
- General recommendations without specifics
- Findings that align with existing docs
- Metrics without clear baseline comparison

### Low Value
Content that is:
- **Generic**: Could apply to any project
- **Redundant**: Already well-documented elsewhere
- **Ephemeral**: Only relevant to the moment

Examples:
- Executive summaries (usually boilerplate)
- Generic best practice reminders
- Status statements ("The review is complete")

## Chunk Extraction Algorithm

```python
def extract_chunks(content: str) -> list[ContentChunk]:
    chunks = []
    current_section = None
    current_content = []

    for line in content.split('\n'):
        # New section header
        if line.startswith('## '):
            if current_section:
                chunks.append(make_chunk(current_section, current_content))
            current_section = line[3:].strip()
            current_content = []
        elif line.startswith('### '):
            # Subsection - append to current or create new
            if current_section:
                current_content.append(line)
            else:

modules/destination-routing.md

# Destination Routing Module

Maps extracted content chunks to appropriate destinations in the documentation.

## Routing Strategy

### Priority Order

1. **Semantic match** - Find existing doc that covers the topic
2. **Default mapping**
 - Use category-based default destinations
3. **Create new** - Only when no suitable destination exists

### Preference: Existing Over New

Always prefer merging into existing documentation:
- Keeps documentation consolidated
- Avoids duplicate coverage
- Maintains established structure

Create new files only when:
- Content is substantial (>500 chars of high-value)
- No existing doc covers the topic
- Content warrants standalone treatment

## Semantic Matching

### Algorithm

```python
def find_semantic_match(chunk: ContentChunk, existing_docs: list[str]) -> str | None:
    """Find best-matching existing document for a content chunk."""

    best_match = None
    best_score = 0

    for doc_path in existing_docs:
        doc_content = read_file(doc_path)
        score = compute_relevance(chunk, doc_content)

        if score > best_score and score >= MATCH_THRESHOLD:
            best_match = doc_path
            best_score = score

    return best_match

def compute_relevance(chunk: ContentChunk, doc_content: str) -> float:
    """Score relevance of chunk to document."""
    score = 0.0

    # Header matching (highest weight)
    doc_headers = extract_headers(doc_content)
    if any(similar(chunk.header, h) for h in doc_headers):
        score += 0.4

    # Keyword overlap
    chunk_keywords = extract_keywords(chunk.content)
    doc_keywords = extract_keywords(doc_content)
    overlap = len(chunk_keywords & doc_keywords) / len(chunk_keywords)
    score += overlap * 0.3

    # Category alignment
    if doc_likely_category(doc_content) == chunk.category:
        score += 0.2

    # Reference mentions
    if chunk mentions doc_path or doc mentions chunk source:
        score += 0.1

    return score

MATCH_THRESHOLD = 0.5  # Minimum score to consider a match
```

### Existing Doc Discovery

Scan these locations for potential destinations:

```python
DOC_LOCATIONS = [
    'docs/',
    'docs/plans/',
    'docs/adr/',
    'README.md',
    'CHANGELOG.md',
]

# Plugin-specific locations
PLUGIN_DOC_LOCATIONS = [
    '{plugin}/docs/',
    '{plugin}/README.md',
]
```

## Default Mappings

When semantic matching finds no suitable destination:

| Category | Default Destination | Notes |
|----------|---------------------|-------|
| Actionable Items | `docs/plans/YYYY-MM-DD-{topic}.md` | New plan file |
| Decisions Made | `docs/adr/NNNN-YYYY-MM-DD-{topic}.md` | New ADR |
| Findings/Insights | `docs/{topic}.md` | New doc or best-effort match |
| Metrics/Baselines | `docs/benchmarks.md` or inline | Append if exists |
| Migration Guides | `docs/migration-guide.md` | Append section |
| API Changes | `CHANGELOG.md` or `docs/api.md` | Prefer CHANGELOG |

### Topic Extraction

Derive topic slug from content:

```python
def extract_t

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Machine-readable data

The same record, as JSON, for agents and crawlers.

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Record generated Oct 10, 2026.

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