Borrowing it
Nothing to install: this file belongs to nyktora/noctrace. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/nyktora/noctrace/main/.claude/skills/context-health/SKILL.mdgit clone --depth 1 https://github.com/nyktora/noctraceWrote 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.
[](https://agentmods.dev/skills/nyktora/noctrace/context-health)<a href="https://agentmods.dev/skills/nyktora/noctrace/context-health"><img src="https://agentmods.dev/badge/skills/nyktora/noctrace/context-health/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.
<a href="https://agentmods.dev/skills/nyktora/noctrace/context-health"><img src="https://agentmods.dev/badge/skills/nyktora/noctrace/context-health.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00037 | $0.02104 |
| Opus 5.5 | $0.00015 | $0.00842 |
| Sonnet 5.5 | $0.00007 | $0.00421 |
| Haiku 4.5 | $0.00004 | $0.00210 |
Grade A, and why
context-health 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 2mo 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.
How it starts
The opening of the file, as written. The whole thing — 223 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Context Health Scoring Specification
Purpose
Compute a real-time A-F grade representing the health of a Claude Code session's context window. This warns developers when quality is degrading before they notice it in the output.
Input Data
All data comes from parsed JSONL records. The health scorer receives an array of parsed records (not raw lines) and computes the grade incrementally — it can be called after each new record to update the score.
interface HealthInput {
assistantRecords: {
timestamp: number;
inputTokens: number;
outputTokens: number;
}[];
compactionEvents: {
timestamp: number;
preTokens: number;
trigger: "auto" | "manual";
}[];
toolCalls: {
timestamp: number;
toolName: string;
filePath?: string; // for Read calls
isError: boolean;
}[];
sessionStartTime: number;
sessionEndTime: number | null; // null = still running
}
Signal Computations
1. Context Fill (weight: 40%)
function computeFillScore(input: HealthInput): number {
const maxWindow = 200_000;
// Use the most recent assistant record's input_tokens
const latest = input.assistantRecords.at(-1);
if (!latest) return 100; // no data = healthy
const fillPct = latest.inputTokens / maxWindow;
if (fillPct < 0.50) return 100; // A
if (fillPct < 0.65) return 80; // B
if (fillPct < 0.80) return 60; // C
if (fillPct < 0.90) return 40; // D
return 20; // F
}
2. Compaction Count (weight: 25%)
function computeCompactionScore(input: HealthInput): number {
const count = input.compactionEvents.length;
if (count === 0) return 100; // A
if (count === 1) return 75; // B
if (count === 2) return 55; // C
if (count === 3) return 35; // D
return 15; // F
}
3. Re-read Ratio (weight: 15%)
Track file paths from Read tool calls. A "re-read" is any Read call targeting a file path that was already read earlier in the session.
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.
- 2mo ago First seen · 223 lines · 37 tokens per session scan A 20f241f970c9
context-health is a skill published in the GitHub repository nyktora/noctrace (5 stars, last pushed 6mo ago), licensed MIT. It adds 37 tokens to every session and 2,104 once invoked, about $0.0001 per session on Opus 5.5. 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 2026-08-31.
context-health compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| context-healthnyktora/noctrace | Skill | 5 | 6mo ago | MIT | A | 37 | Claude Code |
| observability-phoenixdavila7/claude-code-templates | Skill | 33k | today | MIT | A | 47 | Claude Code, Codex |
| agent-inspectrajudandigam/agent-inspect | Skill | 171 | 2d ago | MIT | A | 52 | Claude Code, Codex |
| vigil-alerttonone-ai/tonone | Skill | 77 | 23d ago | MIT | A | 55 | Claude Code |
| vigil-instrumenttonone-ai/tonone | Skill | 77 | 23d ago | MIT | A | 61 | Claude Code |
| sre-expertpersonamanagmentlayer/pcl | Skill | 44 | 12d ago | Apache-2.0 | A | 63 | Claude Code |
| vigil-incidenttonone-ai/tonone | Skill | 77 | 23d ago | MIT | A | 48 | Claude Code |