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 viktorbezdek/skillstack --skill context-compressiongit clone --depth 1 https://github.com/viktorbezdek/skillstackWrote 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/viktorbezdek/skillstack/context-compression)<a href="https://agentmods.dev/skills/viktorbezdek/skillstack/context-compression"><img src="https://agentmods.dev/badge/skills/viktorbezdek/skillstack/context-compression/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/viktorbezdek/skillstack/context-compression"><img src="https://agentmods.dev/badge/skills/viktorbezdek/skillstack/context-compression.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 | $0.00131 | $0.01907 |
| Opus 5.5 | $0.00052 | $0.00763 |
| Sonnet 5.5 | $0.00026 | $0.00381 |
| Haiku 4.5 | $0.00013 | $0.00191 |
Grade A, and why
context-compression 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 — 176 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Context Compression Strategies
When agent sessions generate millions of tokens of conversation history, compression becomes mandatory. The naive approach is aggressive compression to minimize tokens per request. The correct optimization target is tokens per task: total tokens consumed to complete a task, including re-fetching costs when compression loses critical information.
When to Use / Not Use
Use when:
- Agent sessions exceed context window limits
- Codebases exceed context windows (5M+ token systems)
- Designing conversation summarization strategies
- Debugging cases where agents "forget" what files they modified
- Building evaluation frameworks for compression quality
Do NOT use when:
- Diagnosing why context is degrading -> use
context-degradation - KV-cache optimization or context partitioning -> use
context-optimization - Learning foundational context theory -> use
context-fundamentals - File-based offloading or scratch pads -> use
filesystem-context
Decision Tree
Why do you need compression?
├── Agent sessions hitting context limits
│ ├── What matters most?
│ │ ├── File tracking + decision history (long sessions) -> Anchored Iterative Summarization
│ │ ├── Maximum token savings (short sessions, low re-fetch cost) -> Opaque Compression
│ │ └── Readability + phase boundaries -> Regenerative Full Summary
│ └── Not sure? -> Start with Anchored Iterative (best quality trade-off)
├── Need to measure if compression is working
│ └── Probe-based evaluation (§Probe-Based Evaluation)
├── When to trigger compression?
│ └── See §Compression Trigger Strategies
└── Not about reducing size? -> See related skills
Core Compression Approaches
| Method | Ratio | Quality | Best For |
|---|---|---|---|
| Anchored Iterative | 98.6% | 3.70 | Long sessions where file tracking matters (coding, debugging) |
| Opaque | 99.3% | 3.35 | Short sessions, maximum token savings, low re-fetch costs |
| Regenerative Full | 98.7% | 3.44 | Sessions with clear phase boundaries, readability critical |
What ships with it
3 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.
- 1mo ago First seen · 176 lines · 131 tokens per session scan A 07206f3582cf
context-compression is a skill published in the GitHub repository viktorbezdek/skillstack (14 stars, last pushed 4mo ago), licensed MIT. It adds 131 tokens to every session and 1,907 once invoked, about $0.0005 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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