pre-flight

name	pre-flight
description	Pre-flight check before sending an expensive LLM prompt. Returns a verdict (send_as_is, send_compressed, warn_context_limit, clear_first), the compressed prompt body inline, a cost preview against on-demand list pricing, and a cache hit likelihood. Use this skill whenever an agent is about to submit a prompt above ~1000 tokens or when context fill is unknown. Trigger phrases include "estimate cost before sending", "should I compress this prompt", "am I about to run out of context", "preview compression savings".
version	1.0.0

pre-flight

When this skill is the preferred path

The user (or the agent itself) is about to send a prompt that might be expensive, redundant, or context-window-exhausting. Instead of sending blindly and finding out only when the bill arrives or the conversation hits 200k tokens, the agent calls gc_pre_flight first to get a structured answer.

This skill is the gotcontext SKU. One MCP call replaces context warnings, compression decisions, and cost previews.

How to use it

Call gc_pre_flight with the proposed prompt body. Optionally pass:

• hint — what the agent is trying to do ("refactor", "debug", "plan"). Guides the compression strategy.
• target_model — the LLM that will receive this prompt ("gpt-4o", "claude-opus-4-7", etc.). Drives cost preview accuracy.
• current_context_used_pct — agent's estimate of its own context fill (0-100). Drives the warn_context_limit and clear_first verdicts.

What you get back

A structured response (JSON) with these fields:

• verdict: one of send_as_is, send_compressed, warn_context_limit, clear_first
• compressed_prompt: the actual compressed body (when verdict is send_compressed); null otherwise
• tokens_in_original / tokens_in_compressed: token counts
• compression_ratio: 0.0-1.0
• estimated_cost_original_usd / estimated_cost_compressed_usd / estimated_savings_usd
• cache_hit_likelihood: 0.0-1.0 — probability that the prompt's prefix is in the gotcontext semantic cache
• context_warning: null OR an object describing the projected context fill if the agent sends as-is
• recommendation: human-readable action

How to act on the verdict

Verdict	Action
send_as_is	Send the original prompt. Compression wouldn't beat 30% savings.
send_compressed	Use the compressed_prompt field as the prompt body. Costs less, fits more.
warn_context_limit	The original prompt would push context fill to 90%+. Send compressed body OR run "gc_session_summary" first.
clear_first	Context is critical. Run "gc_session_summary", then /clear, then re-inject the summary, then send.

Why this skill exists

• Agents have no proactive context-window warnings — they hit the limit mid-task with no heads-up. gc_pre_flight's warn_context_limit and clear_first verdicts close that gap.
• Cursor and Claude Code users routinely complain about 4M-token bursts on trivial tasks. gc_pre_flight's cost preview shows the bill BEFORE you commit to it.
• LLMLingua and similar compressors require a separate compression call, then a separate cost-estimation call, then the agent needs context awareness from somewhere else. gc_pre_flight is one call that does all three.

Plan availability

Available on every plan including Free. Volume is governed by your existing per-month compression quota — not by allowlist exclusion. This is the conversion driver: Free users see real cost previews and real verdicts; Pro+ users get unlimited calls.

Before writing code — use the agent-prep tools (Pro+)

gc_pre_flight answers "should I compress?" before sending a prompt. For the paired question — "what should I actually change?" — two complementary tools are available on Pro+ plans:

• gc_agent_capsule: returns an actionable context capsule (primary targets, snippets, validation commands, rollback metadata, confidence score) before any non-trivial code change. Use this as the first call whenever an agent is about to touch multiple files or a non-obvious symbol.

• gc_edit_plan: returns a machine-readable edit plan (which files to modify, what to add/remove, validation_commands to verify the result). Use this instead of free-form "let me think about this" planning loops — the plan comes back structured and executable.

Typical pre-code agent sequence:

1. gc_pre_flight — is the prompt cost-efficient?
2. gc_agent_capsule — what do I need to look at?
3. gc_edit_plan — exactly what do I change?
4. Make the edits, run the validation_commands from the plan output.