Tools

PAI Tools - CLI Utilities Reference

Last synced: Apr 22, 2026

PAI Tools - CLI Utilities Reference

This file documents single-purpose CLI utilities that have been consolidated from individual skills. These are pure command-line tools that wrap APIs or external commands.

Philosophy: Simple utilities don’t need separate skills. Document them here, execute them directly.

Model: Following the Tools/fabric/ pattern - 242+ Fabric patterns documented as utilities rather than individual skills.

Inference.ts - Unified AI Inference Tool

Location: ~/.claude/PAI/TOOLS/Inference.ts

Single inference tool with three run levels for different speed/capability trade-offs.

Usage:

# Fast (Haiku) - quick tasks, simple generation
bun ~/.claude/PAI/TOOLS/Inference.ts --level fast "System prompt" "User prompt"

# Standard (Sonnet) - balanced reasoning, typical analysis
bun ~/.claude/PAI/TOOLS/Inference.ts --level standard "System prompt" "User prompt"

# Smart (Opus) - deep reasoning, strategic decisions
bun ~/.claude/PAI/TOOLS/Inference.ts --level smart "System prompt" "User prompt"

# With JSON output
bun ~/.claude/PAI/TOOLS/Inference.ts --json --level fast "Return JSON" "Input"

# Custom timeout
bun ~/.claude/PAI/TOOLS/Inference.ts --level standard --timeout 60000 "Prompt" "Input"

Run Levels:

Level	Model	Default Timeout	Use Case
fast	Haiku	15s	Quick tasks, simple generation, basic classification
standard	Sonnet	30s	Balanced reasoning, typical analysis, decisions
smart	Opus	90s	Deep reasoning, strategic decisions, complex analysis

Programmatic Usage:

// From hooks (at ~/.claude/hooks/):
import { inference } from '../../.claude/PAI/TOOLS/Inference';

const result = await inference({
  systemPrompt: 'Analyze this',
  userPrompt: 'Content to analyze',
  level: 'standard',  // 'fast' | 'standard' | 'smart'
  expectJson: true,   // optional: parse JSON response
  timeout: 30000,     // optional: custom timeout
});

if (result.success) {
  console.log(result.output);
  console.log(result.parsed);  // if expectJson: true
}

When to Use:

“quick inference” → fast
“analyze this” → standard
“deep analysis” → smart
Hooks use this for sentiment analysis, tab titles, work classification

Technical Details:

Uses Claude CLI with subscription (not API key)
Disables tools and hooks to prevent recursion
Returns latency metrics for monitoring

RemoveBg.ts - Remove Image Backgrounds

Location: ~/.claude/PAI/TOOLS/RemoveBg.ts

Remove backgrounds from images using the poof.bg API.

Usage:

# Remove background from single image (overwrites original)
bun ~/.claude/PAI/TOOLS/RemoveBg.ts /path/to/image.png

# Remove background and save to different path
bun ~/.claude/PAI/TOOLS/RemoveBg.ts /path/to/input.png /path/to/output.png

# Process multiple images
bun ~/.claude/PAI/TOOLS/RemoveBg.ts image1.png image2.png image3.png

Environment Variables:

POOFBG_API_KEY - Required for background removal (from ~/.claude/PAI/.env)

When to Use:

“remove background from this image”
“remove the background”
“make this image transparent”

AddBg.ts - Add Background Color

Location: ~/.claude/PAI/TOOLS/AddBg.ts

Add solid background color to transparent images.

Usage:

# Add specific background color
bun ~/.claude/PAI/TOOLS/AddBg.ts /path/to/transparent.png "#EAE9DF" /path/to/output.png

# Add your brand background color (uses the color from PAI config)
bun ~/.claude/PAI/TOOLS/AddBg.ts /path/to/transparent.png --brand /path/to/output.png

When to Use:

“add background to this image”
“create thumbnail with UL background”
“add the brand color background”

Example brand color: #EAE9DF (warm paper/sepia tone — configure your own via PAI config)

GetTranscript.ts - Extract YouTube Transcripts

Location: ~/.claude/PAI/TOOLS/GetTranscript.ts

Extract transcripts from YouTube videos using yt-dlp (via fabric).

Usage:

# Extract transcript to stdout
bun ~/.claude/PAI/TOOLS/GetTranscript.ts "https://www.youtube.com/watch?v=VIDEO_ID"

# Save transcript to file
bun ~/.claude/PAI/TOOLS/GetTranscript.ts "https://www.youtube.com/watch?v=VIDEO_ID" --save /path/to/transcript.txt

Supported URL Formats:

https://www.youtube.com/watch?v=VIDEO_ID
https://youtu.be/VIDEO_ID
https://www.youtube.com/watch?v=VIDEO_ID&t=123 (with timestamp)
https://youtube.com/shorts/VIDEO_ID (YouTube Shorts)

When to Use:

“get the transcript from this YouTube video”
“extract transcript from this video”
“fabric -y ” (user explicitly mentions fabric)

Technical Details:

Uses fabric -y under the hood
Prioritizes manual captions when available
Falls back to auto-generated captions
Multi-language support (detects automatically)

MemoryRetriever.ts - Compressed Knowledge Retrieval

Location: ~/.claude/PAI/TOOLS/MemoryRetriever.ts

BM25-lite search across the Knowledge Archive with optional LLM compression. Finds relevant notes by keyword matching, tag co-occurrence, and content frequency, then compresses results into a dense context-efficient summary.

Usage:

# Search and return compressed results (default: top 3)
bun ~/.claude/PAI/TOOLS/MemoryRetriever.ts "memory architecture"

# Return top 5 results
bun ~/.claude/PAI/TOOLS/MemoryRetriever.ts "security policy" --top 5

# Skip LLM compression, return raw excerpts
bun ~/.claude/PAI/TOOLS/MemoryRetriever.ts "karpathy" --raw

# Custom token budget for output
bun ~/.claude/PAI/TOOLS/MemoryRetriever.ts "threat model" --budget 800

Scoring:

Signal	Weight	Source
Title match	+10	Frontmatter title
Tag match	+5	Frontmatter tags
Related slug match	+3	Frontmatter related field
Content frequency	BM25	Full body text

When to Use:

“what do we know about X” → search knowledge
“find related notes about Y” → search + follow links
Context loading during Algorithm THINK phase knowledge checks
Retrieving compressed context for constrained token budgets

Location: ~/.claude/PAI/TOOLS/KnowledgeGraph.ts

Builds an in-memory graph from Knowledge Archive frontmatter (tags, wikilinks, related fields) and enables BFS traversal for associative recall. No persistent storage — computed from existing markdown files at query time.

Usage:

# BFS traversal from a note (default: 2 hops)
bun ~/.claude/PAI/TOOLS/KnowledgeGraph.ts traverse karpathy

# Traverse with custom depth
bun ~/.claude/PAI/TOOLS/KnowledgeGraph.ts traverse mempalace --hops 3

# Show directly connected notes
bun ~/.claude/PAI/TOOLS/KnowledgeGraph.ts related mempalace

# Graph summary: nodes, edges, clusters
bun ~/.claude/PAI/TOOLS/KnowledgeGraph.ts stats

# Top 10 most-connected notes
bun ~/.claude/PAI/TOOLS/KnowledgeGraph.ts hubs

# Find all notes with a specific tag
bun ~/.claude/PAI/TOOLS/KnowledgeGraph.ts find architecture

Edge Types:

Type	Weight	Source
Tag co-occurrence	1	Shared tags in frontmatter
Wikilink	3	`[[slug]]` in body text
Related field	5	Typed `related:` in frontmatter

When to Use:

“what’s connected to this topic” → traverse
“show me the knowledge graph” → stats + hubs
“find related notes” → related
Exploring how knowledge connects across domains

Voice Server API - Generate Voice Narration

Location: Voice server at http://localhost:31337/notify

Send text to the voice server running on localhost for TTS using a configured voice clone.

Usage:

# Single narration segment
curl -X POST http://localhost:31337/notify \
  -H "Content-Type: application/json" \
  -d '{
    "message": "Your text here",
    "voice_id": "$ELEVENLABS_VOICE_ID",
    "title": "Voice Narrative"
  }'

# Pause between segments
sleep 2

Voice Configuration:

Voice ID: Set via ELEVENLABS_VOICE_ID environment variable
Stability: 0.55 (natural variation in storytelling)
Similarity Boost: 0.85 (maintains authentic sound)
Server: http://localhost:31337/notify
Max Segment: 450 characters
Pause Between: 2 seconds

When to Use:

“read this to me”
“voice narrative”
“speak this”
“narrate this”
“perform this”

Technical Details:

Pulse must be running (voice handler lives at ~/.claude/PAI/PULSE/VoiceServer/voice.ts, port 31337)
Segments longer than 450 chars should be split
Natural 2-second pauses between segments for storytelling flow
Uses ElevenLabs API under the hood

extract-transcript.py - Transcribe Audio/Video Files

Location: ~/.claude/PAI/TOOLS/extract-transcript.py

Local transcription using faster-whisper (4x faster than OpenAI Whisper, 50% less memory). Self-contained UV script for offline transcription.

Usage:

# Transcribe single file (base.en model - recommended)
cd ~/.claude/PAI/TOOLS/
uv run extract-transcript.py /path/to/audio.m4a

# Use different model
uv run extract-transcript.py audio.m4a --model small.en

# Generate subtitles
uv run extract-transcript.py video.mp4 --format srt

# Batch transcribe folder
uv run extract-transcript.py /path/to/folder/ --batch --model base.en

Supported Formats:

Audio: m4a, mp3, wav, flac, ogg, aac, wma
Video: mp4, mov, avi, mkv, webm, flv

Output Formats:

txt - Plain text transcript (default)
json - Structured JSON with timestamps
srt - SubRip subtitle format
vtt - WebVTT subtitle format

Model Options:

Model	Size	Speed	Accuracy	Use Case
tiny.en	75MB	Fastest	Basic	Quick drafts, testing
base.en	150MB	Fast	Good	General use (recommended)
small.en	500MB	Medium	Very Good	Important recordings
medium	1.5GB	Slow	Excellent	Production quality
large-v3	3GB	Slowest	Best	Critical accuracy needs

When to Use:

“transcribe this audio”
“transcribe recording”
“extract transcript from audio”
“convert audio to text”
“generate subtitles”

Technical Details:

100% local processing (no API calls, completely offline)
First run auto-installs dependencies via UV (~30 seconds)
Models auto-download from HuggingFace on first use
Apple Silicon (M1/M2/M3) optimized
Processing speed: ~3-5 minutes for 36MB audio file (base.en model)

Prerequisites:

UV package manager: curl -LsSf https://astral.sh/uv/install.sh | sh
No manual model download required (auto-downloads on first use)

YouTubeApi.ts - YouTube Channel & Video Stats

Location: ~/.claude/PAI/TOOLS/YouTubeApi.ts

Wrapper around YouTube Data API v3 for channel statistics and video metrics.

Usage:

# Get channel statistics
bun ~/.claude/PAI/TOOLS/YouTubeApi.ts --channel-stats

# Get video statistics
bun ~/.claude/PAI/TOOLS/YouTubeApi.ts --video-stats VIDEO_ID

# Get latest uploads
bun ~/.claude/PAI/TOOLS/YouTubeApi.ts --latest-videos

Environment Variables:

YOUTUBE_API_KEY - Required for API access (from ~/.claude/PAI/.env)
YOUTUBE_CHANNEL_ID - Default channel ID

When to Use:

“get YouTube stats”
“YouTube channel statistics”
“video performance metrics”
“subscriber count”

Data Retrieved:

Total subscribers
Total views
Total videos
Recent upload performance
View counts, likes, comments per video

Technical Details:

Uses YouTube Data API v3 REST endpoints
Quota: 10,000 units per day (free tier)
Each API call costs ~3-5 quota units

TruffleHog - Scan for Exposed Secrets

Location: System-installed CLI tool (brew install trufflehog)

Scan directories for 700+ types of credentials and secrets.

Usage:

# Scan directory
trufflehog filesystem /path/to/directory

# Scan git repository
trufflehog git file:///path/to/repo

# Scan with verified findings only
trufflehog filesystem /path/to/directory --only-verified

Installation:

brew install trufflehog

When to Use:

“check for secrets”
“scan for sensitive data”
“find API keys”
“detect credentials”
“security audit before commit”

What It Detects:

API keys (OpenAI, AWS, GitHub, Stripe, 700+ services)
OAuth tokens
Private keys (SSH, PGP, SSL/TLS)
Database connection strings
Passwords in code
Cloud provider credentials

Technical Details:

Scans files, git history, and commits
Uses entropy detection + regex patterns
Verifies findings when possible (calls APIs to check if keys are valid)
No API key required (standalone CLI tool)

RTK — Context Reduction Proxy

Location: ~/.local/bin/rtk (installed via curl -fsSL https://raw.githubusercontent.com/rtk-ai/rtk/refs/heads/master/install.sh | sh)

Repository: github.com/rtk-ai/rtk (MIT license)

Transparent CLI proxy that compresses Bash command output by 60-90%, reducing token consumption in Claude Code sessions. Integrated via PreToolUse hook — commands are automatically rewritten to rtk equivalents before execution.

How It Works:

PreToolUse hook (ContextReduction.hook.sh) intercepts Bash commands
Rewrites git status → rtk git status, bun test → rtk bun test, etc.
RTK binary executes the command, applies command-specific compression
Returns compact output via updatedInput — AI receives fewer tokens

Compression Coverage (50+ commands):

Domain	Commands	Typical Savings
Git	status, log, diff, commit, push, pull	85-99%
Build	cargo, tsc, Next.js builds	75-87%
Test	pytest, vitest, jest, cargo test	94-99%
Lint	eslint, ruff, pylint, mypy, prettier	80-90%
Package	npm, pnpm, pip, bun	70-95%
File ops	ls, tree, find, grep, diff	50-80%
Infrastructure	docker, kubectl, aws, psql	varies
GitHub CLI	gh pr, gh issue, gh run	30%+

Key Commands:

rtk gain              # Token savings dashboard
rtk gain --history    # Historical savings data
rtk discover          # Find unoptimized commands
rtk verify            # Check installation integrity

When to Use:

Automatic — the PreToolUse hook handles everything transparently
Direct invocation for analytics: rtk gain, rtk discover

Technical Details:

Rust binary, zero runtime dependencies
3-tier parsing: JSON → regex → passthrough (graceful degradation)
SQLite tracking database at ~/.config/rtk/history.db
Config at ~/.config/rtk/config.toml
Hook requires jq (for JSON stdin parsing)

Monitor Tool — Event-Driven Background Watching

The Monitor tool starts a background script whose stdout lines become chat notifications. Instead of polling in a loop (burning tokens), the script runs independently and wakes you when something happens. Zero token cost between events.

Key rules:

Each stdout line = one notification. Stderr goes to output file only.
Always use grep --line-buffered in pipes (otherwise pipe buffering delays events).
Poll intervals: 30s+ for remote APIs, 0.5-1s for local checks.
Handle transient failures: curl ... || true in poll loops.
Set persistent: true for session-length watches. Cancel with TaskStop.

Recipe: Agent Watchdog (auto-triggered by hook)

The Pulse agent-guard hook automatically injects a watchdog reminder when background agents are spawned. The watchdog monitors tool-activity.jsonl for silence while agents are active:

Monitor({
  description: "Agent watchdog",
  persistent: true,
  timeout_ms: 3600000,
  command: "bun $HOME/.claude/PAI/TOOLS/AgentWatchdog.ts"
})

Alerts when no tool calls detected for 90 seconds with active agents. Rate-limited to one alert per 60 seconds. Runs for the session lifetime — covers all background agents.

Recipe: Deploy Monitoring

Watch a Cloudflare Pages or Workers deploy for completion or errors:

# Monitor wrangler deploy output (run deploy with Bash(run_in_background), then tail its output)
Monitor({
  description: "Cloudflare deploy status",
  persistent: false,
  timeout_ms: 300000,
  command: "tail -f /tmp/deploy.log | grep --line-buffered -E '(Published|Error|Failed|SUCCESS)'"
})

Recipe: Pulse Log Tailing

Watch Pulse daemon logs for errors during a debugging session:

Monitor({
  description: "Pulse error watcher",
  persistent: true,
  timeout_ms: 300000,
  command: "tail -f ~/.claude/Pulse/logs/pulse-stdout.log | grep --line-buffered -i -E '(error|fatal|crash|unhandled)'"
})

Recipe: Build/Test Watching

Monitor a long test suite and get notified on failures:

Monitor({
  description: "Test failure watcher",
  persistent: false,
  timeout_ms: 600000,
  command: "tail -f /tmp/test-output.log | grep --line-buffered -E '(FAIL|ERROR|✗|AssertionError)'"
})

Recipe: PR/CI Status Monitoring

Poll GitHub for CI status changes on a PR:

Monitor({
  description: "CI status for PR #42",
  persistent: true,
  timeout_ms: 3600000,
  command: "last_status=''; while true; do status=$(gh pr checks 42 --json state --jq '.[].state' 2>/dev/null | sort -u | tr '\\n' ','); if [ \"$status\" != \"$last_status\" ]; then echo \"CI: $status\"; last_status=\"$status\"; fi; sleep 30; done"
})

Recipe: Security Scan Watching

Tail security scan results for critical findings:

Monitor({
  description: "Security scan critical findings",
  persistent: false,
  timeout_ms: 600000,
  command: "tail -f /tmp/security-scan.log | grep --line-buffered -i 'CRITICAL'"
})

Integration with Other Skills

Art Skill

Background removal: RemoveBg.ts
Add backgrounds: AddBg.ts

Blogging Skill

Image optimization: RemoveBg.ts, AddBg.ts
Social preview thumbnails

Research Skill

YouTube transcripts: GetTranscript.ts
Audio/video transcription: extract-transcript.py
Voice narration: Voice server API

Metrics Skill

YouTube analytics: YouTubeApi.ts

Security Workflows

Secret scanning: trufflehog (system tool)

Adding New Tools

When adding a new utility tool to this system:

Add tool file: Place .ts or .py file directly in ~/.claude/PAI/TOOLS/
- Use Title Case for filenames (e.g., GetTranscript.ts, not get-transcript.ts)
- Keep the directory flat - NO subdirectories
Document here: Add section to this file with:
- Tool location (e.g., ~/.claude/)
- Usage examples
- When to use triggers
- Environment variables (if any)
Update CLAUDE.md routing table: Ensure TOOLS.md is referenced in the documentation index
Test: Verify tool works from new location

Don’t create a separate skill if the entire functionality is just a CLI command with parameters.

Deprecated Skills

The following skills have been consolidated into this Tools system:

Images → Tools/RemoveBg.ts, Tools/AddBg.ts (2024-12-22)
VideoTranscript → Tools/GetTranscript.ts (2024-12-22)
VoiceNarration → Voice server API (2024-12-22)
ExtractTranscript → Tools/extract-transcript.py, Tools/ExtractTranscript.ts (2024-12-22)
YouTube → Tools/YouTubeApi.ts (2024-12-22)
Sensitive → trufflehog system tool (2024-12-22)

Archived skill files have been removed.

algorithm.ts - The Algorithm CLI

Location: ~/.claude/PAI/TOOLS/algorithm.ts

Run the PAI Algorithm in Loop, Interactive, Ideate, or Optimize mode against a PRD.

Usage:

# Run modes
algorithm -m loop -p <PRD> [-n 128]           # Autonomous loop execution
algorithm -m interactive -p <PRD>              # Interactive claude session
algorithm -m optimize -p <PRD>                 # Autonomous metric optimization
algorithm -m ideate -p <PRD>                   # Evolutionary ideation session

# Presets (ideate)
algorithm -m ideate -p <PRD> --preset dream        # Pure free-form dreaming
algorithm -m ideate -p <PRD> --preset explore       # Broad exploration
algorithm -m ideate -p <PRD> --preset balanced      # Default balanced
algorithm -m ideate -p <PRD> --preset directed      # Problem-focused
algorithm -m ideate -p <PRD> --preset surgical      # Maximum analytical focus
algorithm -m ideate -p <PRD> --preset wild-but-picky  # Dream wildly, select ruthlessly

# Presets (optimize)
algorithm -m optimize -p <PRD> --preset cautious    # Small steps, no regression
algorithm -m optimize -p <PRD> --preset aggressive   # Large steps, accepts regression

# Focus dial (ideate only, 0.0=dream, 1.0=laser)
algorithm -m ideate -p <PRD> --focus 0.2

# Individual parameter overrides (repeatable)
algorithm -m ideate -p <PRD> --focus 0.2 --param selectionPressure=0.9
algorithm -m optimize -p <PRD> --param stepSize=0.8 --param regressionTolerance=0.3

# Management
algorithm new -t <title> [-e <effort>]         # Create a new PRD
algorithm status [-p <PRD>]                    # Show PRD status
algorithm pause -p <PRD>                       # Pause a running loop
algorithm resume -p <PRD>                      # Resume a paused loop
algorithm stop -p <PRD>                        # Stop a loop

Parameter schema: ~/.claude/PAI/ALGORITHM/parameter-schema.md

AlgorithmPhaseReport.ts - Algorithm State Reporter

Location: ~/.claude/PAI/TOOLS/AlgorithmPhaseReport.ts

Writes algorithm execution state to algorithm-phase.json for dashboard consumption.

Usage:

# Phase transitions
bun AlgorithmPhaseReport.ts phase --phase OBSERVE --task "Auth rebuild" --sla Standard

# Criteria tracking
bun AlgorithmPhaseReport.ts criterion --id 1 --desc "JWT rejects expired tokens" --status pending
bun AlgorithmPhaseReport.ts criterion --id 1 --status completed --evidence "Tests pass"

# Agent tracking
bun AlgorithmPhaseReport.ts agent --name engineer-1 --type Engineer --status active

# Capabilities
bun AlgorithmPhaseReport.ts capabilities --list "Task Tool,Engineer Agents"

# Parameter configuration (v3.16.0+)
bun AlgorithmPhaseReport.ts config --preset dream --focus 0.25 --mode ideate

# Meta-learner adjustment tracking (v3.16.0+)
bun AlgorithmPhaseReport.ts meta-adjust --param selectionPressure --from 0.3 --to 0.45 --rationale "text"

KnowledgeHarvester.ts - Knowledge Archive Harvester

Location: ~/.claude/PAI/TOOLS/KnowledgeHarvester.ts

Validate and maintain the KNOWLEDGE/ archive (3 entity types: People, Companies, Ideas). Validates against schemas in _schema.md, handles MOC regeneration and maintenance. Note: Algorithm LEARN phase writes knowledge directly; harvester reflections are disabled. The harvester’s primary role is now validation, maintenance, and index regeneration.

Usage:

# Harvest from all sources
bun ~/.claude/PAI/TOOLS/KnowledgeHarvester.ts harvest

# Harvest from specific source
bun ~/.claude/PAI/TOOLS/KnowledgeHarvester.ts harvest --source work

# Preview without writing
bun ~/.claude/PAI/TOOLS/KnowledgeHarvester.ts harvest --dry-run

# Archive health dashboard
bun ~/.claude/PAI/TOOLS/KnowledgeHarvester.ts status

# Regenerate all MOC dashboards
bun ~/.claude/PAI/TOOLS/KnowledgeHarvester.ts index

Sources:

Source	Flag	What It Scans
memory	`--source memory`	Auto-memory files from Claude sessions
work	`--source work`	WORK/ directory PRDs and session artifacts
reflections	`--source reflections`	Learning/reflection documents
research	`--source research`	RESEARCH/ directory content

When to Use:

“harvest knowledge”
“update knowledge archive”
“knowledge status”
“regenerate knowledge index”

ArchitectureSummaryGenerator.ts - Architecture Summary Generator

Location: ~/.claude/PAI/TOOLS/ArchitectureSummaryGenerator.ts

Generate PAI_ARCHITECTURE_SUMMARY.md from PAISYSTEMARCHITECTURE.md and subsystem docs. Provides a compact architecture overview derived from the master architecture document.

Usage:

# Generate/regenerate the architecture summary
bun ~/.claude/PAI/TOOLS/ArchitectureSummaryGenerator.ts generate

# Check if summary is stale (exit 1 if stale, 0 if fresh)
bun ~/.claude/PAI/TOOLS/ArchitectureSummaryGenerator.ts check

When to Use:

“regenerate architecture summary”
“check if architecture summary is stale”
After modifying PAISYSTEMARCHITECTURE.md

MCP Servers

Config: ~/.claude/.mcp.json

PAI connects to external MCP servers for domain-specific tool access.

Server	Type	Endpoint	Controllable
reversinglabs	HTTP	`rl-protect-mcp.YOUR-SUBDOMAIN.workers.dev/mcp`	Yes (your CF Worker)
cloudflare	HTTP	`mcp.cloudflare.com/mcp`	No (external)

Result Size Override (Claude Code v2.1.91+)

MCP tool results are truncated by default. Servers can override this by adding _meta["anthropic/maxResultSizeChars"] to their tool result annotations, allowing results up to 500K characters without truncation.

This is a server-side annotation — the MCP server must add it to its responses. There is no client-side setting.

Implementation example (in MCP server response):

{
  "content": [{ "type": "text", "text": "...large result..." }],
  "_meta": {
    "anthropic/maxResultSizeChars": 500000
  }
}

PAI applicability:

reversinglabs — Can add this annotation (we control the Worker). Useful for rl_scan and rl_diff which return large analysis payloads.
cloudflare — External server; annotation must be added upstream by their maintainers.

Architecture: ~/.claude/PAI/DOCUMENTATION/PAISystemArchitecture.md (master architecture reference)
CLI Tools: ~/.claude/PAI/DOCUMENTATION/Tools/Cli.md (Algorithm CLI, Arbol CLI)

Last Updated: 2026-04-20

PAI Tools - CLI Utilities Reference

PAI Tools - CLI Utilities Reference

Inference.ts - Unified AI Inference Tool

RemoveBg.ts - Remove Image Backgrounds

AddBg.ts - Add Background Color

GetTranscript.ts - Extract YouTube Transcripts

MemoryRetriever.ts - Compressed Knowledge Retrieval

KnowledgeGraph.ts - Associative Knowledge Navigation

Voice Server API - Generate Voice Narration

extract-transcript.py - Transcribe Audio/Video Files

YouTubeApi.ts - YouTube Channel & Video Stats

TruffleHog - Scan for Exposed Secrets

RTK — Context Reduction Proxy

Monitor Tool — Event-Driven Background Watching

Recipe: Agent Watchdog (auto-triggered by hook)

Recipe: Deploy Monitoring

Recipe: Pulse Log Tailing

Recipe: Build/Test Watching

Recipe: PR/CI Status Monitoring

Recipe: Security Scan Watching

Integration with Other Skills

Art Skill

Blogging Skill

Research Skill

Metrics Skill

Security Workflows

Adding New Tools

Deprecated Skills

algorithm.ts - The Algorithm CLI

AlgorithmPhaseReport.ts - Algorithm State Reporter

KnowledgeHarvester.ts - Knowledge Archive Harvester

ArchitectureSummaryGenerator.ts - Architecture Summary Generator

MCP Servers

Result Size Override (Claude Code v2.1.91+)

Related Documentation