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.
npx skills add DVNghiem/FlowDeck --skill context-budgetgit clone --depth 1 https://github.com/DVNghiem/FlowDeckWrote 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/dvnghiem/flowdeck/context-budget)<a href="https://agentmods.dev/skills/dvnghiem/flowdeck/context-budget"><img src="https://agentmods.dev/badge/skills/dvnghiem/flowdeck/context-budget/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/dvnghiem/flowdeck/context-budget"><img src="https://agentmods.dev/badge/skills/dvnghiem/flowdeck/context-budget.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 2 findings, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high Memory Poisoning · line 147 Skill manipulates agent memory, state, or stored context. Memory corruption can alter personality, override safety rules, or cause unpredictable behavior.Fix: Protect agent memory and state from modification by untrusted content. Use read-only memory for critical instructions and validate all state changes.
- high Memory Poisoning · line 261 Skill manipulates agent memory, state, or stored context. Memory corruption can alter personality, override safety rules, or cause unpredictable behavior.Fix: Protect agent memory and state from modification by untrusted content. Use read-only memory for critical instructions and validate all state changes.
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 | $0.00022 | $0.02251 |
| Opus 5.5 | $0.00009 | $0.00900 |
| Sonnet 5.5 | $0.00004 | $0.00450 |
| Haiku 4.5 | $0.00002 | $0.00225 |
Grade A, and why
context-budget 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 1mo 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 — 267 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Context Budget Skill
Treat context window as a finite resource. Every token loaded — files, rules, tool outputs, conversation history — consumes budget. Optimizing context improves speed, cuts costs, and prevents mid-session truncation.
When to Activate
Activate when:
- A session exceeds 50K tokens or feels sluggish
- You are about to load large files, MCP tools, or heavy rulesets
- You want to audit and slim down your FlowDeck setup
- You are designing new skills, agents, or workflows
Core Principles
- Load less, get more — context quality beats context quantity
- Measure before optimizing — know your current burn rate
- Batch over chat — accumulate work, run checks once
- Right-size the model — light tasks do not need the strongest model
Why Context Budget Matters
| Factor | Impact |
|---|---|
| Context window limit | Hard cap — exceed it and early conversation is lost |
| Cost per token | More context = more input tokens = higher bill |
| Response latency | Large context increases time-to-first-token |
| Attention degradation | Models perform worse on content near the middle of long context |
Hard Limits (Examples)
| Model | Context Window |
|---|---|
| Claude 3.5 Haiku | 200K tokens |
| Claude 3.5 Sonnet | 200K tokens |
| GPT-4o | 128K tokens |
| GPT-4o mini | 128K tokens |
Treat 80% of the window as your practical maximum. Beyond that, truncation risk rises sharply.
Skill Size Audit
Oversized skills waste context on every activation. Audit yours regularly.
Thresholds
| Metric | Warning | Critical |
|---|---|---|
| Lines per SKILL.md | > 300 | > 400 |
| Words in description | > 25 | > 30 |
| Files loaded per task | > 5 | > 10 |
| Rules active at once | > 8 | > 12 |
How to Audit
# Count lines in all skills
find src/skills -name "SKILL.md" -exec wc -l {} + | sort -n
# Flag skills over 300 lines
find src/skills -name "SKILL.md" -exec sh -c 'lines=$(wc -l < "$1"); [ "$lines" -gt 300 ] && echo "$lines $1"' _ {} \;
# Check description word counts
grep -r "^description:" src/skills/ | awk '{print NF, $0}' | sort -n
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.
- 1mo ago First seen · 267 lines · 22 tokens per session scan A c03d54dfd95e
context-budget is a skill published in the GitHub repository DVNghiem/FlowDeck (39 stars, last pushed 2mo ago), licensed MIT. It adds 22 tokens to every session and 2,251 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 2026-08-30.
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