copilot-cli-agent— agent skill

🎭 Identity: The Sub-Agent Dispatcher (Standard: gpt-5-mini)

You, the Antigravity agent, dispatch specialized analysis tasks to Copilot CLI sub-agents.

[!IMPORTANT] Default model: `gpt-5-mini` (included — no credit cost). As of June 1, 2026, GitHub moves to AI Credits (per-token billing), but gpt-5-mini is an included model that does not consume credits. Higher-multiplier models (e.g., claude-sonnet-4.6) do consume credits — see 💰 AI Credits & Cost Discipline before using them.

✅ Minimal Working Code Review Agent Pattern

To ensure Copilot CLI behaves as a specialized persona rather than a generic responder, always embed the persona and source material directly into the prompt flag (-p).

copilot -p "$(cat agents/persona.md)

---SOURCE CODE---
$(cat target.py)

---INSTRUCTION---
Perform a full code review. Use severity levels: 🔴 CRITICAL, 🟡 MODERATE, 🟢 MINOR.
You are operating as an isolated sub-agent.
Do NOT use tools. Do NOT access filesystem." > review.md

🛠️ Orchestration Pattern: run_agent.py (Cross-Platform)

For reusable sub-agent execution, use the provided Python orchestrator which handles temp file assembly and prompt concatenation reliably across Windows, macOS, and Linux.

# Signature:
python ./scripts/run_agent.py <PERSONA_FILE> <INPUT_FILE> <OUTPUT_FILE> "<INSTRUCTION>" [MODEL] [isolated]
#                                                                                           ^        ^
#                                                                                           optional optional (default: false)

Two dispatch modes

Task dispatch (default — agent has full filesystem access via --yolo):

# Agent reads/writes files directly. Pass the task prompt as INPUT_FILE.
python plugins/cli-agents/scripts/run_agent.py \
  /dev/null \
  tasks/todo/copilot_prompt_0025.md \
  temp/copilot_output_0025.md \
  "Implement all changes specified in the prompt." \
  claude-sonnet-4.6

Isolated analysis (no filesystem tools — text output only):

# Pass isolated=true as 6th arg. Agent generates text output only.
python plugins/cli-agents/scripts/run_agent.py \
  agents/security-auditor.md target.py security.md \
  "Find vulnerabilities." gpt-5-mini true

Prompt assembly (handled automatically by run_agent.py)

Passing /dev/null for persona or input skips that block cleanly.

🔀 Model Selection Guide

Default: gpt-5-mini (included — no credit cost)

# No model arg = gpt-5-mini (included model, does not consume credits)
python ./scripts/run_agent.py agents/security-auditor.md target.py security.md \
  "Find vulnerabilities."

High-quality: claude-sonnet-4.6 (9x multiplier — batch everything)

# Pass model name as the 5th argument to override the default
python ./scripts/run_agent.py /dev/null /tmp/copilot_prompt.md /tmp/copilot_output.md \
  "Generate all files exactly as specified using ===FILE:=== delimiters." \
  claude-sonnet-4.6

[!NOTE] When to use `claude-sonnet-4.6`: Complex multi-file generation, nuanced content requiring reasoning, tasks where output quality matters more than cost. See 💰 AI Credits & Cost Discipline for batching rules before calling.

Known Model Identifiers (June 2026)

[!WARNING] Model identifiers use dots not dashes — claude-sonnet-4.6 not claude-sonnet-4-6. Identifiers change with Copilot CLI updates — run copilot -i "list models" or check the interactive model selector to confirm current names before any high-multiplier run.

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Deprecated (do not use): claude-sonnet-4 (deprecated May 7), gpt-5.2 / gpt-5.2-codex (deprecated). GPT-4.1 deprecation upcoming.

🎭 Persona Registry (agents/)

⚠️ CLI Best Practices & Failure Modes

1. ❌ Why Stdin Piping Fails

Using cat code.py | copilot -p "review this" is unreliable. The CLI often prioritizes the prompt flag and ignores the piped input. Always embed the code inside the command string as shown in the Core Pattern.

2. ❌ Empty Output (Background Runs)

Large prompt expansions (e.g., $(cat ...) > 10KB) can silently fail when run in the background (&).

• Fix: Use a temporary file for the combined prompt (as implemented in run_agent.py).
• Fix: Run commands sequentially and verify output size with wc -l.

🧩 Force Agent Behavior & Model

Always add these instructions to your dispatch prompt to prevent the sub-agent from attempting to use external tools. Strictly use gpt-5-mini as the default model.

4. 💡 Improve Quality

To dramatically improve review results, add:

"Think step-by-step internally, but output only final results. Be strict and critical. Do not be polite."

💰 AI Credits & Cost Discipline

[!CAUTION] As of June 1, 2026, GitHub Copilot moves to AI Credits (per-token billing). 1 Credit = $0.01. Credits do NOT roll over monthly. When credits are exhausted, Copilot stops — there is no fallback model. Set your additional-spend cap to $0 in GitHub billing settings to hard-stop at your monthly allotment.

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`gpt-5-mini`, `gpt-4.1`, and `gpt-4o` remain included models (no credit cost). Annual plan subscribers stay on PRU-based pricing until their plan expires, then roll to the new Credit model. Code completions and Next Edit Suggestions never consume credits.

Credit Cost by Tier

Strategy by Cost Tier

Rules for Credit-Consuming Models

1. Plan before calling; batch for output quality. Two distinct reasons to minimize requests:

• Planning reduces wasted tokens — a well-specified prompt avoids correction requests, which each re-pay the full context cost. Think through requirements before dispatching.
• Batching improves coherence and reduces round-trips — a single call generating 7 files produces internally consistent output that 7 separate calls won't. Under AI Credits the total token cost is the same either way, but the quality and latency are better batched.

1. Use structured output delimiters so one response parses into multiple files:

   ===FILE: [relative/path/to/file]===
   [complete file content]
   ===ENDFILE===

1. Verify delimiter coverage before calling. Count expected ===FILE:=== markers in your prompt — confirm the same count appears in output before parsing.
2. No follow-up requests for minor gaps. Fill small omissions yourself. Only make a second high-multiplier request if a whole file is entirely missing.
3. Heartbeat with `gpt-5-mini`. Run the heartbeat against the cheapest model only — it verifies connectivity without burning credits.
4. Do NOT background (`&`) high-multiplier calls. Large prompts can silently produce empty output in background processes. Run foreground and verify with wc -l (expect 200+ lines for multi-file output).

Premium Model Invocation Pattern

# Write the full multi-file prompt to a temp file first
cat > /tmp/copilot_prompt.md << 'PROMPT_EOF'
[Your complete, dense, multi-file generation prompt]
PROMPT_EOF

# Dispatch ONE request — all output in a single call
python ./scripts/run_agent.py \
  /dev/null \
  /tmp/copilot_prompt.md \
  /tmp/copilot_output.md \
  "Generate all files exactly as specified using ===FILE:=== delimiters." \
  claude-sonnet-4.6

# Verify output is substantial before parsing
wc -l /tmp/copilot_output.md   # expect 200+ lines for multi-file output

Quality Gate Before Parsing

# Confirm all expected FILE markers are present before assuming success
grep -c '===FILE:' /tmp/copilot_output.md  # should equal your expected file count

✅ Functional CLI Heartbeat (Mandatory: "All Signals Go")

Before initiating major orchestrations or long-running iterative loops (e.g., Triple-Loop), you MUST perform a zero-shot heartbeat check to verify the host CLI has end-to-end connectivity and correct model defaults.

Heartbeat Pattern:

python .agents/skills/copilot-cli-agent/scripts/run_agent.py \
  /dev/null /dev/null ./HEARTBEAT_MD.md \
  "HEARTBEAT CHECK: Respond with 'HEARTBEAT_OK' only."

# Verification Logic:
[ -s ./HEARTBEAT_MD.md ] && grep -q "HEARTBEAT_OK" ./HEARTBEAT_MD.md && echo "HEARTBEAT_OK" || echo "HEARTBEAT_FAIL"

Logging Requirement: The result of this heartbeat (Success or Failure) MUST be explicitly written to the session log before proceeding. If it fails, halt execution and report the error details (e.g., 401 Unauthorized, 429 Rate Limit, or Network Error).

✅ Smoke Test

python ./scripts/run_agent.py agents/refactor-expert.md target.py output.md "Refactor this code."

Examine output.md. It should contain ONLY the refactored code and a brief 3-bullet summary.

Gotchas (field-tested)

• Model identifiers use dots, not dashes. claude-sonnet-4.6 works; claude-sonnet-4-6 returns "model not available". Always use dot notation for Claude version numbers in Copilot CLI.
• Always verify the model identifier before a premium batch run. Run copilot --yolo --model <id> -p "HEARTBEAT_OK" first — if it echoes back any response, the identifier is valid. Do not assume identifiers from docs or memory are current.
• `run_agent.py` passes the 5th argument directly to `--model`. If the identifier is wrong, the script exits with a non-zero code and produces no output file. Check exit code and output file size before claiming success.
• Background premium runs can silently fail with empty output. Never background (&) a premium model call. Run foreground and verify with wc -l output.md — expect 200+ lines for multi-file output.
• Heartbeat with `gpt-5-mini`, not a credit-consuming model. It's an included model — connectivity checks cost nothing.
• `permissions.disableBypassPermissionsMode` setting (v1.0.55+) — can lock out --yolo/allow-all mode. If headless dispatch stops working, check this setting hasn't been set to true by an org admin.
• Recursive skill/agent discovery (v1.0.55+) — Copilot CLI now discovers agents and skills in subdirectories, not just the root .agents/ level. Nested layouts work.
• Per-MCP-server token usage now visible in /mcp and /context — use this to audit which MCP tools are consuming credits.