---

name: consult-light description: This skill should be used for fast AI consultation via cursor-agent. Triggers include context synthesis, progress tracking, pre-flight checks, or when the loop needs quick reasoning before escalating to consult-deep.

consult-light

fast consultation tier using cursor-agent. context synthesis, progress tracking, pre-flight checks. escalates to consult-deep when confidence < 7.

philosophy

"cheap, fast, frequent - the first line of autonomous reasoning"

principle	application
token efficiency	structured packets, not raw history
confidence scoring	0-10 scale, threshold-based escalation
single model	gemini-3-pro for everything (fast, cheap)
fail fast	confidence < 7 → escalate to consult-deep
stdin for long prompts	pipe via heredoc, not inline args

when to use

use	skip
context synthesis	simple file reads
progress tracking	one-off commands
pre-flight validation	human-in-the-loop moments
quick sanity checks	architectural decisions (→ consult-deep)
hook-level reasoning	final review (→ consult-deep)

model

gemini-3-pro via cursor-agent. fast, cheap, good reasoning.

# short prompts: inline
cursor-agent -p --model gemini-3-pro "prompt here"

# long prompts: stdin (required for multi-line)
cat <<'EOF' | cursor-agent -p --model gemini-3-pro
long prompt
with multiple lines
and structured content
EOF

workflow

1. build packet: structure context into state packet
2. invoke: pipe prompt to cursor-agent via stdin
3. parse response: extract action, confidence, reason
4. route: confidence >= 7 → proceed, < 7 → escalate to consult-deep

state packet schema

{
  "session": {
    "id": "uuid",
    "started": "ISO timestamp",
    "issue": "ARB-123"
  },
  "progress": {
    "phase": "execute",
    "step": 3,
    "total": 7
  },
  "recent_actions": [
    "wrote tests for auth module",
    "ran verify --format=summary",
    "2 test failures in login.test.ts"
  ],
  "current_state": "fixing test failures",
  "question": "should I refactor the auth helper or patch inline?"
}

response schema

{
  "action": "patch inline - minimal change for this fix",
  "confidence": 8,
  "reason": "isolated bug, refactor would expand scope",
  "escalate": false
}

invocation patterns

context synthesis

cat <<'EOF' | cursor-agent -p --model gemini-3-pro
Given this session state:
{packet}

Summarize progress and recommend next action.
Output JSON: {action, confidence, reason, escalate}
EOF

progress tracking

cat <<'EOF' | cursor-agent -p --model gemini-3-pro
Session: {packet}

Assess: what % complete? any blockers?
Output JSON: {percent_complete, blockers[], next_step, confidence}
EOF

pre-flight check

cat <<'EOF' | cursor-agent -p --model gemini-3-pro
About to: {proposed_action}
Context: {packet}

Validate: safe to proceed? preconditions met?
Output JSON: {proceed: bool, issues[], confidence}
EOF

domain skill depth review

cat <<'EOF' | cursor-agent -p --model gemini-3-pro
Domain skill depth review.

Task: {task_description}
Sources consulted: {sources_list}
Artifacts created: {artifact_list}

Content includes:
{content_summary}

Validate:
1. Primary sources used? (not just summaries)
2. Decision trees present? (not just philosophy)
3. Concrete values? (numbers, timings)
4. Integration with user's tools?

Output JSON: {depth_score: 1-10, pass: bool, issues: [], confidence: 1-10}
EOF

code work review

cat <<'EOF' | cursor-agent -p --model gemini-3-pro
Code work review.

Task: {task_description}
Files changed: {file_list}
Tests: {test_summary}

Validate:
1. Tests cover new behavior?
2. Follows codebase patterns?
3. Edge cases handled?

Output JSON: {pass: bool, issues: [], confidence: 1-10}
EOF

escalation

when confidence < 7 or explicit escalate flag:

# escalate to consult-deep (Codex)
codex exec --model gpt-5.2-max --approval-mode xhigh \
  "Review needed: {context}
   consult-light confidence: {score}
   Reason for escalation: {reason}
   Provide thorough analysis."

references

• references/prompt-templates.md - reusable prompts
• references/model-selection.md - when to use which model
• references/packet-building.md - context packet patterns

CLI cookbook

# check status
cursor-agent status

# short prompt (inline)
cursor-agent -p --model gemini-3-pro "quick question"

# long prompt (stdin) - REQUIRED for multi-line
cat <<'EOF' | cursor-agent -p --model gemini-3-pro
multi-line prompt
with context
EOF

# with workspace context
cat prompt.txt | cursor-agent -p --model gemini-3-pro --workspace /path/to/project

# JSON output format
cursor-agent -p --model gemini-3-pro --output-format json "prompt"

# list available models (error will show them)
cursor-agent -p --model help "" 2>&1 | grep "Available models"

anti-patterns

• inline long prompts: use stdin heredoc for multi-line (will fail otherwise)
• context bloat: send structured packets, not raw history
• spinning: low confidence → escalate, don't retry same prompt
• missing confidence: always request confidence score for routing
• wrong tier: architectural decisions belong in consult-deep