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 agentmods add skills/codebygarv/ai-skills/context-window-optimizernpx skills add codebygarv/Ai-skills --skill context-window-optimizergit clone --depth 1 https://github.com/codebygarv/Ai-skillsWhat 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 | $0.00029 | $0.00342 |
| Opus 5 | $0.00015 | $0.00171 |
| Sonnet 5 | $0.00006 | $0.00068 |
| Haiku 4.5 | $0.00003 | $0.00034 |
Grade A, and why
context-window-optimizer 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 2d 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.
What it actually says
Purpose
Compress, filter, and structure codebase context and instructions provided to AI coding agents and LLMs, maximizing token efficiency, eliminating noise, and preventing context window dilution.
When to Use
- Feeding large codebases, trace logs, or documentation into AI assistants.
- Hitting token context limits in AI agent sessions.
- Improving LLM reasoning accuracy by stripping unneeded boilerplate and node_modules cruft.
What to Analyze
- Signal-to-Noise Ratio: Strip minified bundles, lockfiles, compiled artifacts, and repetitive mock fixtures.
- Skeleton & Interface Extraction: Extract TypeScript interfaces, class signatures, and exported function headers rather than full 1,000-line function bodies.
- Log & Trace Compression: Deduplicate repeated stack traces, error loops, and verbose debug strings into frequency summaries.
- Structured Chunking: Organize context into semantic markdown blocks with unambiguous file paths.
Output Format
- Optimized Context Package: Clean, condensed markdown representation of the essential code/architecture.
- Token Reduction Score: Estimated tokens saved (e.g. 75,000 tokens $ ightarrow$ 6,200 tokens, 91% reduction).
- Extracted Type Interface Summary: High-signal API surface overview.
Avoid
- Dumping 50,000-line
package-lock.jsonfiles into LLM context. - Stripping crucial type signatures or error definitions needed for reasoning.
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 2d ago First seen · 33 lines · 29 tokens per session scan A 65e2af18220a
context-window-optimizer is a skill published in the GitHub repository codebygarv/Ai-skills (24 stars, last pushed 13d ago), licensed MIT. It adds 29 tokens to every session and 342 once invoked, about $0.0001 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 2026-08-30.
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