token-budgeted-compaction-with-reinjection

token-budgeted-compaction-with-reinjection is a skill for Claude Code, Codex from ychampion/cskill-agents. It costs 35 tokens per session (471 once invoked), scanned A, original, MIT.

A way to shorten growing session history within per-skill and overall token limits, while retaining truncated skill or document content for possible reinjection later.

In plain words
What is it for?
It is for compacting agent conversations and controlling how much skill metadata and document context is carried between turns.
Why use it?
It prevents context from exceeding the API limit without permanently losing every important instruction or attachment.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one. Also seen: mentions Claude Code.

Good fit It is for compacting agent conversations and controlling how much skill metadata and document context is carried between turns.

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Install with agentmods
npx agentmods add skills/ychampion/cskill-agents/token-budgeted-compaction-with-reinjection
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add ychampion/cskill-agents --skill token-budgeted-compaction-with-reinjection
Clone the repo
git clone --depth 1 https://github.com/ychampion/cskill-agents

Made for: Claude Code, Codex.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for token-budgeted-compaction-with-reinjection

README.md
[![agentmods](https://agentmods.dev/badge/skills/ychampion/cskill-agents/token-budgeted-compaction-with-reinjection/github.svg)](https://agentmods.dev/skills/ychampion/cskill-agents/token-budgeted-compaction-with-reinjection)
Your own site
<a href="https://agentmods.dev/skills/ychampion/cskill-agents/token-budgeted-compaction-with-reinjection"><img src="https://agentmods.dev/badge/skills/ychampion/cskill-agents/token-budgeted-compaction-with-reinjection/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for token-budgeted-compaction-with-reinjection

Your own site · 80×15
<a href="https://agentmods.dev/skills/ychampion/cskill-agents/token-budgeted-compaction-with-reinjection"><img src="https://agentmods.dev/badge/skills/ychampion/cskill-agents/token-budgeted-compaction-with-reinjection.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 35 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 471 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00035 $0.00471
Opus 5 $0.00017 $0.00235
Sonnet 5 $0.00007 $0.00094
Haiku 4.5 $0.00003 $0.00047

Measured 8d ago against content hash 7de569da0397, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

token-budgeted-compaction-with-reinjection scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 8d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

agents/claude-code/skills/token-budgeted-compaction-with-reinjection/SKILL.md · 28 lines

How it starts

The opening of the file, as written. The whole thing — 28 lines — stays where its author put it; the contents beside it link to each section on GitHub.

SKILL: Token Budgeted Compaction With Reinjection

Domain: compaction Trigger: Use when session history is growing toward the API limit and you need a safe way to trim older context while still reinjecting critical artifacts like skill metadata. Source Pattern: Distilled from reviewed session memory, compaction, and context-budgeting implementations.

Core Method

Run compaction in capped batches tied to a per-skill token budget: truncate each skill/document to <= 5k tokens, enforce an overall skill budget (25k tokens), bubble up warnings when budgets are hit, and only reinject the trimmed content when downstream turns request it. Keep a reusable helper that resets the per-skill counters after a compact run so the next turn starts from a clean slate.

Key Rules

  • Truncate at most the allowed per-skill token amount and track how many tokens were retained; this becomes the compact summary that can be reinjected later.
  • Reserve a shared skills token budget and subtract each truncated skill from it; stop truncating once the budget is exhausted and emit a warning so the user knows some instructions were dropped.
  • Reinjection occurs during the next turn only after the compact boundary moves; do not ring the same trimmed skill multiple times in one turn.
  • Emit attachments for reinjected content so the downstream model can fetch specific sections rather than re-running the entire compact logic.

Example Application

When a Claude Code conversation runs long, apply this skill to compact turns by triming per-skill summaries down to 5k tokens, track the remaining budget, warn when the budget is exhausted, and include the reinjected skill attachments alongside the next query so the model has the context it needs.

Anti-Patterns (What NOT to do)

  • Don't truncate skills arbitrarily without tracking their token costs; you need to know which instructions survived to explain compaction decisions to the user.
  • Don't reinject the same trimmed data repeatedly in the same turn; the budget is about the current turn, so reinject only after a new compact boundary has been established.

Read the full file on GitHub · 28 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 8d ago First seen · 28 lines · 35 tokens per session scan A 7de569da0397

Subscribe to this mod's changes

token-budgeted-compaction-with-reinjection is a skill published in the GitHub repository ychampion/cskill-agents (36 stars, last pushed 5mo ago), licensed MIT. It adds 35 tokens to every session and 471 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.