headroom

name

headroom

description

Token-reduction skill for coding agents. Compresses JSON tool output, build logs, grep/ripgrep search results, source code, and conversation history the same way the Headroom proxy does — but in-context, with no binary install. Use this skill whenever an agent is about to paste a large tool result (a 1000-row JSON array, a 5000-line pytest log, a 200-result grep, a 1000-line source file) into its next message, or when the agent's conversation is approaching the context limit, or when the user complains about token cost or latency. Also use it to shape the agent's own output: skip ceremony, don't restate code already on screen, drop reasoning on routine turns. Triggers: "compress this output", "this is too long", "trim", "summarize the log", "reduce tokens", "context is full", "running out of context", "too many tokens", "make it shorter", "be terse".

Headroom — portable token-reduction skill

This skill teaches any coding agent to apply the Headroom compression techniques in-context. It does not require the Headroom binary.

When to apply which transform

Pick the transform by content type. If a tool result is shorter than ~200 tokens, skip compression — the overhead isn't worth it.

Content the agent is about to use / paste	Apply	Helper script
JSON array of dicts / objects (tool results, API responses)	SmartCrusher	scripts/smart_crusher.py
Build / test logs (pytest, npm, cargo, make)	LogCompressor	scripts/log_compressor.py
grep / ripgrep / ag output (file:line:content)	SearchCompressor	scripts/search_compressor.py
Source code (Python, JS, TS, Go, Rust, Java, C/C++)	CodeCompressor	scripts/code_compressor.py
Conversation history over the context budget	RollingWindow	scripts/context_manager.py
Anything the model is about to write back	Output Shaper (L2)	prompts/verbosity_steering.md

The seven rules

These are the core rules the agent should internalise. Full detail in AGENTS.md; this is the short version.

1. Compress before paste. Before pasting any tool result over ~200 tokens into the next message, run the matching helper script (or apply the matching heuristic by hand if no script is available). Cite the script in your narration: "Compressed 1,432 → 187 tokens with SmartCrusher (kept first 3, last 2, all errors, top 5 by BM25 to query)."
2. Never drop errors. Errors, stack traces, is_error: true results, and FAILED lines are sacred. Always preserve them 100%, even when compressing hard.
3. Keep first N and last N. For arrays, logs, and search results, always keep the first 3 and last 2 items. They give pagination context and recency.
4. Stabilise the prefix. When building prompts, keep the system prompt and the first user turn byte-identical across requests. Move dynamic content (timestamps, "today is", session IDs) to the tail of the prompt so the provider's KV cache stays warm.
5. Drop oldest tool outputs first. When the conversation exceeds the token budget, drop complete tool_call + tool_result pairs from the oldest end. Never split a pair. Never drop the system prompt. Never drop the last 2 user turns.
6. Shape your own output. Default verbosity is L2: skip preambles, don't restate code already on screen, reference by path:line. Drop to L3 (conclusions only) when the user asks for brevity. Never narrate tool results the user can already see.
7. Be reversible. When you compress a tool result, write the original to .headroom-cache/<sha256>.txt and include the SHA in your narration: "CCR key: a3f9…". If a later turn needs the original, retrieve it from there.

What "compression" means here

Compression is selection, not zipping. We don't run gzip. We pick which items / lines / nodes to keep, and emit a shorter representation that preserves the information the LLM needs to answer the user's question. The original is always retrievable from the CCR cache.

The selection heuristics, in priority order:

1. Errors — always kept.
2. First N — pagination / setup context.
3. Last N — recency.
4. Anomalies — values >2σ from the mean (for numeric arrays) or items whose fields differ sharply from their neighbours.
5. Relevant — top-K by BM25 similarity to the user's current question.
6. Change points — items where a field value transitions (status flips, counters reset).
7. Sample — a statistically representative sample of the remainder, so the LLM still has a sense of the distribution.

Output-token shaping (the "L2 default")

When generating your own reply, follow prompts/verbosity_steering.md at level 2 unless the user asks for more or less. The five levels:

• L0 — off
• L1 — skip ceremony ("Great, let me…")
• L2 — L1 + don't restate code/output already on screen; reference by path:line (default)
• L3 — L2 + conclusions only, skip reasoning unless asked
• L4 — fragments, minimum tokens

The "effort routing" companion rule: if the previous turn was a tool result with is_error: false and no new user ask, this is a mechanical continuation. Reply at L3 and skip deep reasoning. If the previous turn was an error, or a new user question, keep full effort. See prompts/effort_routing.md.

Files in this skill

• AGENTS.md — universal agent rules (works with any agent framework). Read this first.
• CLAUDE.md — Claude Code / Cursor specific copy of the same rules.
• docs/techniques.md — deep dive into each transform, with worked examples.
• docs/integrations.md — per-agent setup (Hermes, Claw, Codex, Cursor, Aider, Goose, Continue, OpenHands, vibe, Copilot CLI).
• docs/reversible-ccr.md — the on-disk CCR cache convention.
• scripts/ — pure-Python stdlib helpers. No pip install required.
• prompts/ — system-prompt fragments for output shaping.
• examples/ — before/after comparisons for each transform.

Important safety rules (do not violate)

These mirror the safety rules in upstream Headroom. Each prevents a concrete failure mode.

1. Never drop user or assistant text. Tool results can be compressed; human-authored messages cannot.
2. Never split a tool_call / tool_result pair. Dropping one half orphans the other and the model will hallucinate a result.
3. Parse failures are no-ops. If the input doesn't parse as the expected type, pass it through unchanged. A bad compression is worse than no compression.
4. Always preserve recency. The last N turns (default 2 user turns) are never dropped, even if they have low importance scores.
5. Always preserve the system prompt. Never.
6. 100% error preservation. No exceptions.
7. Idempotent. Running the compressor on already-compressed output should be a no-op (or close to it).

Credits

This skill is a portable re-implementation of the techniques in chopratejas/headroom by Tejas Chopra and contributors, licensed Apache 2.0. See NOTICE and the "Credits" section of README.md for full attribution. The upstream project is faster, more accurate, and ML-backed; install it if you can.