CLI-Agent Architecture Skill

A single run(command="...") tool with Unix CLI commands outperforms typed function calls.

This skill teaches the two-layer CLI architecture derived from production lessons at Manus and r/LocalLLaMA research. It is the foundation for building robust, production-ready AI agents that execute shell commands.

1. Why CLI > Typed Functions

The LLM-Native Interface

LLMs have seen billions of Unix CLI examples in training data. They understand:

• Pipe semantics (|, >, >>)
• Exit codes ($?, ||, &&)
• Redirection (2>&1, \x3C, \x3C\x3C)
• Globbing and expansion (*, ?, [...])

Typed function calls are unfamiliar terrain — a thin abstraction layer that maps poorly onto concepts LLMs already master.

One Tool, Not Three

Typed functions for a file operation:

read_file(path) → content
analyze(content) → result
write_file(path, result)

CLI equivalent:

run(command="grep pattern file | jq '.key' > result.json")

The pipe chain replaces three function calls with one coherent primitive. LLMs already think in pipelines.

Unified Namespace

• Typed functions create context-switching overhead: switching between "function call mode" and "shell mode"
• CLI provides a single namespace for all operations: files, processes, network, services, containers
• No schema drift, no SDK版本 mismatch, no function deprecation

2. Two-Layer Architecture

┌─────────────────────────────────────────────────────────────┐
│                      AGENT (LLM)                            │
│         Thinks in pipelines. Speaks shell natively.         │
└────────────────────────┬────────────────────────────────────┘
                         │ command="..."
                         ▼
┌─────────────────────────────────────────────────────────────┐
│               LAYER 1 — Unix Execution                       │
│  ┌─────────────────────────────────────────────────────┐    │
│  │  exec.run(command)  →  (stdout, stderr, exit_code)  │    │
│  └─────────────────────────────────────────────────────┘    │
│  • Pure execution, no abstraction                           │
│  • Lossless — binary stdout passes through unchanged        │
│  • Metadata-free — Layer 2 adds all presentation logic      │
└────────────────────────┬────────────────────────────────────┘
                         │ raw output
                         ▼
┌─────────────────────────────────────────────────────────────┐
│             LAYER 2 — LLM Presentation                        │
│  ┌──────────┐  ┌──────────┐  ┌──────────┐  ┌─────────────┐   │
│  │ Binary   │  │ Overflow │  │ stderr   │  │  Metadata   │   │
│  │ Guard    │  │ Truncator│  │ Attachment│  │   Footer    │   │
│  └──────────┘  └──────────┘  └──────────┘  └─────────────┘   │
│  Binary → guidance   >200 lines →  • exit:N on failure      │
│  detected  → replaced  temp file     • duration on success  │
└────────────────────────┬────────────────────────────────────┘
                         │ optimized output
                         ▼
┌─────────────────────────────────────────────────────────────┐
│               AGENT (LLM) — receives processed view           │
└─────────────────────────────────────────────────────────────┘

Why Separation Is Logically Necessary

Layer 1 must be lossless — it cannot make decisions about what to show the LLM, because it has no context about the task. Layer 2 is the presentation layer that adapts raw execution output for LLM consumption.

If Layer 1 filtered or truncated, it would make irreversible decisions without task context. If Layer 2 executed commands, it would mix concerns and lose the clarity of the pipeline.

3. Four Layer 2 Mechanisms

3A. Binary Guard

Problem: Binary data (images, PDFs, executables) blinds the LLM. A terminal full of PNG header bytes is meaningless and wastes context.

Detection: Read the first 8KB of stdout. If >30% non-printable bytes (outside 0x20-0x7E, 0x09, 0x0A, 0x0D), treat as binary.

Replacement message format:

[Binary file detected — 182KB PNG image]
Use: see \x3Ctemp_path>
Or:  file \x3Cpath>

Script: scripts/binary_guard.py

3B. Overflow Mode

Problem: Large outputs (>200 lines) cause attention collapse. The LLM loses the signal in the noise.

Truncation strategy:

1. Show first 50 lines (context anchor)
2. Write full output to temp file
3. Replace middle with: [... N lines truncated. Full output: /tmp/out_abc123 ...]
4. Show last 20 lines (recent context)

Threshold: 200 lines (configurable). Below threshold, pass through unchanged.

Script: scripts/truncator.py

3C. Metadata Footer

Purpose: Always tell the LLM the exit code and execution duration.

On success:

[exit:0 | 1.23s]

On failure (combined with stderr attachment):

[exit:127 | 0.45s]

The LLM uses this to decide retry, different command, or escalation — without needing to parse raw output.

3D. stderr Attachment

Problem: Silent stderr causes blind retries. The LLM sees exit code != 0 but has no clue what went wrong.

Rule: Never suppress stderr. On failure, always attach it.

Format:

--- stderr ---
/bin/grep: file: No such file or directory
--- end stderr ---

On success: stderr is discarded unless it contains warnings the LLM should know about (configurable).

Script: scripts/stderr_capture.py

4. Error Message Design

Every error message must have two parts:

1. What went wrong — concrete, specific
2. What to do instead — actionable next step

Examples

Anti-patterns

❌ "error occurred" — vague
❌ "command failed" — no clue what went wrong
❌ "try again" — no diagnostic info
❌ "file not found" — no suggestion on what to try

5. Progressive Disclosure

Don't dump all documentation at once. Reveal on demand.

Level 0 — Always Injected (Start of Session)

Available commands (one-line summaries):
  run     — Execute shell command, returns stdout/stderr/exit
  see     — Render binary file (image/video/audio) inline
  search  — Full-text search across files
  read    — Read file contents (text only)
  write   — Write text to file
  list    — List directory contents

Level 1 — On-Demand Usage (no args or --help)

$ run
Usage: run \x3Ccommand>
Executes a shell command and returns processed output.
  --timeout=N   Max execution time in seconds (default: 60)
  --env=KEY=VAL Inject environment variable

Level 2 — Parameter Drilling (explicit request)

Full parameter documentation, examples, edge cases, and security notes.

6. Implementation Guide

Directory Structure

cli-agent-architecture/
├── SKILL.md
├── scripts/
│   ├── binary_guard.py
│   ├── truncator.py
│   └── stderr_capture.py
└── examples/
    └── two_layer_execution.py   # reference implementation

Binary Detection (binary_guard.py)

#!/usr/bin/env python3
"""Detect binary data in byte stream. Returns (is_binary, guidance_message)."""
import sys
import os
import stat

def detect_binary_stream(data: bytes, path: str = None) -> tuple[bool, str]:
    """Return (True, guidance) if data appears binary."""
    # Fast path: check file mode if path provided
    if path and os.path.exists(path):
        mode = os.stat(path).st_mode
        if stat.S_ISBLK(mode) or stat.S_ISCHR(mode) or stat.S_ISFIFO(mode):
            return True, f"[Binary device/fifo detected: {path}]"

    if not data:
        return False, ""

    # Sample first 8KB
    sample = data[:8192]
    non_printable = sum(
        1 for b in sample
        if b not in (9, 10, 13) and (b \x3C 32 or b > 126)
    )

    ratio = non_printable / len(sample) if sample else 0

    if ratio > 0.30:
        # Try to identify type
        size = len(data)
        hint = ""
        if path:
            import mimetypes
            mime, _ = mimetypes.guess_type(path)
            if mime:
                hint = f" ({mime})"

        return True, f"[Binary file detected — {size} bytes{hint}]\
Use: see {path or '\x3Ctempfile>'}\
Or:  file {path or '\x3Cfile>'}"

    return False, ""


if __name__ == "__main__":
    data = sys.stdin.buffer.read()
    is_bin, msg = detect_binary_stream(data)
    if is_bin:
        print(msg, file=sys.stderr)
        sys.exit(1)

Overflow Truncation (truncator.py)

#!/usr/bin/env python3
"""Truncate large output, write full content to temp file."""
import sys
import os
import tempfile

MAX_LINES = 200
SHOW_HEAD = 50
SHOW_TAIL = 20

def truncate_output(stdout: str, stderr: str = "") -> tuple[str, str | None]:
    """
    If stdout > MAX_LINES, truncate and write to temp file.
    Returns (processed_stdout, temp_file_path or None).
    """
    lines = stdout.splitlines()
    temp_path = None

    if len(lines) \x3C= MAX_LINES:
        return stdout, None

    head = "\
".join(lines[:SHOW_HEAD])
    tail = "\
".join(lines[-SHOW_TAIL:])
    truncated_mid = f"[... {len(lines) - SHOW_HEAD - SHOW_TAIL} lines truncated ...]"

    # Write full output to temp file
    fd, temp_path = tempfile.mkstemp(prefix="cli_out_", suffix=".txt")
    try:
        os.write(fd, stdout.encode("utf-8", errors="replace"))
    finally:
        os.close(fd)

    return f"{head}\
{truncated_mid}\
{tail}", temp_path


if __name__ == "__main__":
    output = sys.stdin.read()
    truncated, path = truncate_output(output)
    print(truncated)
    if path:
        print(f"\
[Full output written to: {path}]", file=sys.stderr)

stderr Capture (stderr_capture.py)

#!/usr/bin/env python3
"""Capture and format stderr on command failure."""
import sys

def format_stderr_attachment(stderr: str, command: str = "") -> str:
    """Format stderr for display when a command fails."""
    if not stderr or not stderr.strip():
        return ""

    lines = stderr.strip().splitlines()
    # Limit to 30 lines to avoid flooding context
    if len(lines) > 30:
        lines = lines[:30] + ["[... additional stderr truncated ...]"]

    header = "--- stderr ---"
    if command:
        header += f" (command: {command})"
    footer = "--- end stderr ---"

    return "\
".join([header] + lines + [footer])


if __name__ == "__main__":
    stderr = sys.stdin.read()
    formatted = format_stderr_attachment(stderr)
    if formatted:
        print(formatted, file=sys.stderr)

7. When CLI Breaks Down

Strongly-Typed Interactions

GraphQL APIs, complex DB queries with typed schemas, gRPC with protobuf — CLI's string-based interface loses type safety. Use typed function calls here, or build a thin CLI wrapper that validates types before passing to the underlying system.

High-Security / Injection-Risk Environments

• SQL/shell injection risk with unsanitized user input
• Environments where arbitrary command execution is prohibited
• Audited systems where all actions must be logged and approved

In these cases, typed functions with explicit allowlists are preferable to unrestricted CLI access.

Native Multimodal (Audio/Video Processing)

When the task is transcoding, audio analysis, or video editing, CLI tools exist but the LLM cannot "see" the output. For these tasks, typed functions that call domain-specific APIs (FFmpeg wrappers, audio analysis libraries) outperform raw CLI.

8. Business Application

AI Agent Production Readiness Audit

Help companies assess whether their AI agent infrastructure is production-ready.

Audit Scope ($500–$2,000):

Deliverable: Written report with findings, severity ratings, and recommendations.

Implementation ($2,000–$5,000):

• Implement the two-layer architecture
• Deploy binary guard, overflow truncation, stderr attachment
• Tune thresholds for the client's workload
• Train team on progressive disclosure patterns

Pitch:

"Your agent works in demos. Does it work at 3am with a 500MB log file and a cryptic 'command failed' error? I audit the gap between 'it works' and 'it's production-ready' — and close it."

Reference: Complete Two-Layer Execution Flow

1. Agent decides: run("grep -r 'ERROR' /var/log/app/*.log | tail -50")
2. Layer 1 exec:  stdout, stderr, exit_code = exec.run("grep ...")
3. Layer 2 processing:
   a. Binary guard  → if binary: replace with guidance
   b. Overflow mode → if >200 lines: truncate + temp file
   c. stderr attach → if exit != 0: include stderr
   d. metadata footer → attach [exit:N | duration]
4. Processed output → Agent
5. Agent interprets and decides next action

See Also

• scripts/binary_guard.py — binary detection implementation
• scripts/truncator.py — overflow truncation implementation
• scripts/stderr_capture.py — stderr formatting on failure