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/docxology/template/context-degradationnpx skills add docxology/template --skill context-degradationgit clone --depth 1 https://github.com/docxology/templateWrote 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/docxology/template/context-degradation)<a href="https://agentmods.dev/skills/docxology/template/context-degradation"><img src="https://agentmods.dev/badge/skills/docxology/template/context-degradation.svg" alt="Measured on agentmods" 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 | $0.00045 | $0.03479 |
| Opus 5 | $0.00023 | $0.01740 |
| Sonnet 5 | $0.00009 | $0.00696 |
| Haiku 4.5 | $0.00005 | $0.00348 |
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 5d 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.
This is a copy
100% identical to context-degradation — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 237 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Context Degradation Patterns
Diagnose and fix context failures before they cascade. Context degradation is not binary — it is a continuum that manifests through five distinct, predictable patterns: lost-in-middle, poisoning, distraction, confusion, and clash. Each pattern has specific detection signals and mitigation strategies. Treat degradation as an engineering problem with measurable thresholds, not an unpredictable failure mode.
When to Activate
Activate this skill 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
- Evaluating context engineering choices for production systems
- Investigating "lost in middle" phenomena in agent outputs
- Analyzing context-related failures in agent behavior
Do not activate this skill for adjacent work owned by other skills:
- Explaining foundational context mechanics without an active failure:
context-fundamentals. - Applying token-efficiency tactics after the failure pattern is known:
context-optimization. - Designing a compression or handoff summary strategy:
context-compression. - Persisting large outputs, logs, or scratch state outside the prompt:
filesystem-context.
Core Concepts
Structure context placement around the attention U-curve: beginning and end positions receive reliable attention, while middle positions suffer materially reduced recall accuracy in long-context experiments (claim-context-degradation-lost-middle-ruler). This is not a model bug but a consequence of attention mechanics — the first token (often BOS) acts as an "attention sink" that absorbs disproportionate attention budget, leaving middle tokens under-attended as context grows.
Treat context poisoning as a circuit breaker problem. Once a hallucination, tool error, or incorrect retrieved fact enters context, it compounds through repeated self-reference. A poisoned goals section causes every downstream decision to reinforce incorrect assumptions. Detection requires tracking claim provenance; recovery requires truncating to before the poisoning point or restarting with verified-only context.
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.
- 5d ago First seen · 237 lines · 45 tokens per session scan A 4e1896f641dd
context-degradation is a skill published in the GitHub repository docxology/template (19 stars, last pushed yesterday), licensed Apache-2.0. It adds 45 tokens to every session and 3,479 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to context-degradation, differing in 0 lines, and is treated as a copy.
Other skills, from other repositories
context-engineering
Ensure graphify corpus is current and complete before each agent session. Define minimum AI-accessible documentation per repo. Use at session startup to verify internal context is available to AI tools. Implements DORA AI Capability 3.
learn
Review the current conversation and update project knowledge artifacts - common-gotchas.md (bug patterns), AGENTS.md (conventions), agent memory (cross-session). Use when asked to '/learn', 'what did we learn', 'capture lessons', 'update common-gotchas'.
save-learning
Use when you learned something durable — a decision, a hard-won lesson, a useful reference. Captures it into the memory wiki so it compounds.
save-memory
Persist newly-learned facts from the current session into the memory pyramid. Scans the conversation for durable information, finds the right file, deduplicates, and writes back. Use when the user says "remember this", "save it", "for next time", or when a non-obvious fact surfaced that a future session would ask…
wrap-up
Update memory/ACTIVE.md with the state of the current session — what was worked on, what's done, what's next, what's blocked — so the next session can pick up without re-learning context. Run at session end or when switching to an unrelated task.
beads
Use when working in a repository that uses bd or Beads for durable project task tracking, issue dependencies, blocker management, multi-session handoff, or shared work memory. Trigger when the user asks to find ready work, claim or close tasks, create follow-up work, inspect blockers, recover project context, or…