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-degradationgit 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-degradation)<a href="https://agentmods.dev/skills/viktorbezdek/skillstack/context-degradation"><img src="https://agentmods.dev/badge/skills/viktorbezdek/skillstack/context-degradation/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-degradation"><img src="https://agentmods.dev/badge/skills/viktorbezdek/skillstack/context-degradation.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 | $0.02042 |
| Opus 5.5 | $0.00052 | $0.00817 |
| Sonnet 5.5 | $0.00026 | $0.00408 |
| Haiku 4.5 | $0.00013 | $0.00204 |
Grade A, and why
context-degradation 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 — 174 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Context Degradation Patterns
Language models exhibit predictable degradation patterns as context length increases. These patterns are not random failures — they follow measurable thresholds and can be systematically diagnosed and mitigated.
When to Use / Not Use
Use when:
- Agent performance degrades unexpectedly during long conversations
- Debugging cases where agents produce incorrect or irrelevant outputs
- Designing systems that must handle large contexts reliably
- Investigating "lost in middle" phenomena in agent outputs
- Evaluating model selection based on degradation thresholds
Do NOT use when:
- Learning context basics or theory -> use
context-fundamentals - Compressing or summarizing context -> use
context-compression - KV-cache optimization or context partitioning -> use
context-optimization - Building isolated multi-agent architectures -> use
multi-agent-patterns
Decision Tree
What degradation symptom are you seeing?
├── Agent ignores information from middle of context
│ └── Lost-in-Middle -> Place critical info at edges, use explicit headers
├── Agent keeps referencing wrong/incorrect facts
│ ├── Wrong facts came from tool output error -> Context Poisoning (truncate to before poison point)
│ ├── Wrong facts came from retrieved docs -> Context Poisoning (validate docs before loading)
│ └── Wrong facts came from model hallucination -> Context Poisoning (mark and re-evaluate)
├── Agent focuses on irrelevant information
│ └── Context Distraction -> Relevance filter before loading, namespacing, JIT context
├── Agent mixes requirements from different tasks
│ └── Context Confusion -> Task segmentation, clear transitions, state isolation
├── Agent receives contradictory information
│ ├── From different sources -> Context Clash (priority rules, conflict marking)
│ ├── From version conflicts -> Context Clash (version filtering)
│ └── From different perspectives -> Context Clash (explicit conflict marking)
└── Performance degrades as context grows
└── Measure local degradation curve -> Choose mitigation from Four-Bucket Approach
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 · 174 lines · 131 tokens per session scan A 9c9af747726c
context-degradation 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 2,042 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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