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hebertzhuhebertzhu/agent-foundry/skills/context-fundamentals/SKILL.md

context-fundamentals

Skill0 starsChanged 8 months agoMITView on GitHub

用于上下文工程基础问题：当用户要理解 context window、注意力行为、progressive disclosure、context budgeting 或高层 agent 上下文架构，或者会问“为什么 agent 一长对话就失忆”“为什么 agent 聊久了就不靠谱”“上下文到底怎么设计”“context budget 怎么分”“上下文预算怎么分”“为什么 prompt 很长反而更差”“为什么 prompt 越堆越差”，or English asks like “why does the agent get worse in long chats”, “how should I budget context”, “why does a longer prompt perform worse” 时使用。Prefer `context-compression` for 会话内压缩，Prefer `memory-systems` for 跨会话长期记忆；Do not use for 直接实现具体压缩流程或持久化记忆系统。

16 other projects use a version of this.

Adopted by

Most recently taken up by

Used with

Projects with it also use long-horizon-prompting, bdi-mental-states and 10 more.

Other files people use alongside this one.

Files found in the same public projects as a version of this one, more often than they turn up anywhere else. Each project counts once, large collections count for less, and this file's own repository is left out.
  • DVwebBiz11Skill

    long-horizon-prompting

    This skill should be used when writing, enhancing, or evaluating the launch prompt for a long-running autonomous agent or a parallel multi-agent orchestration attacking a hard problem: pseudo-formal task briefs that define terms and an exact success predicate linguistically, enumerate non-counting outcomes, set persistence rules with explicit stop and return conditions and effort floors, manage a diverse portfolio of parallel approaches with an approach registry and blocked-route bookkeeping, and gate the return on adversarial audit. Route agent topology and coordination protocols to multi-agent-patterns, runtime control surfaces and loop governance to harness-engineering, evaluator and quality-gate construction to evaluation, judge design to advanced-evaluation, and compaction or memory mechanics to context-compression and memory-systems.

    Also in 5 projects that use this

  • Downey6689Skill

    bdi-mental-states

    Transforms external RDF context into BDI mental states. Invoke when modeling beliefs, desires, and intentions.

    Also in 6 projects that use this

  • Downey6689Skill

    hosted-agents

    Guidance for hosted/sandboxed agents. Invoke when running background agents in isolated VMs or multi-client setups.

    Also in 6 projects that use this

  • DVwebBiz11Skill

    self-improvement-loops

    This skill should be used when the harness, scaffold, workflow, or optimizer itself is the optimization target: recursive self-improvement (RSI) loops, meta-harnesses, self-improving harnesses that mine their own failures and propose bounded edits, evolutionary or population-based search over agent scaffolds, acceptance gates for self-modifying systems, and agentic context evolution where the mechanism that produces context is versioned and evolved. Route governance of a single autonomous loop (locked surfaces, durable logs, rollback, novelty gates, approval boundaries) to harness-engineering, measurement and quality-gate design to evaluation, judge design to advanced-evaluation, and remote sandbox infrastructure to hosted-agents.

    Also in 4 projects that use this

  • AbdelrhmanAlquranSkill

    latent-briefing

    This skill should be used when the user asks to "share memory between agents", "KV cache compaction for multi-agent", "orchestrator worker context", "latent briefing", "reduce worker tokens", "cross-agent memory without summarization", or discusses Attention Matching compaction, recursive language models with workers, or token explosion in hierarchical agents.

    Also in 4 projects that use this

  • rohunvoraSkill

    advanced-evaluation

    Master LLM-as-a-Judge evaluation techniques including direct scoring, pairwise comparison, rubric generation, and bias mitigation. Use when building evaluation systems, comparing model outputs, or establishing quality standards for AI-generated content.

    Also in 6 projects that use this

  • +1 more from Downey6689

The file

We split this skill into the rules it gives an agent

3 rules. 0 are common, 3 are distinctive, and 0 are very specific to this project.

  • 0

    Common

    Most of the 14 edited versions we read keep these.

  • 3

    Distinctive

    This author's own choices. Worth a look.

  • 0

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Distinctive:

    Call out trade-offs, failure modes, and evaluation signals.

    line 16
  2. Distinctive:

    Keep examples generic, privacy-safe, and portable across hosts.

    line 21
  3. Distinctive:

    Do not stop at theory when the user really needs a concrete storage or retrieval design.

    line 24

“Common” is measured: kept by at least 6 in 10 of the 14 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

context-fundamentals

Intent

  • 用于上下文工程基础问题：当用户要理解 context window、注意力行为、progressive disclosure、context budgeting 或高层 agent 上下文架构，或者会问“为什么 agent 一长对话就失忆”“为什么 agent 聊久了就不靠谱”“上下文到底怎么设计”“context budget 怎么分”“上下文预算怎么分”“为什么 prompt 很长反而更差”“为什么 prompt 越堆越差”，or English asks like “why does the agent get worse in long chats”, “how should I budget context”, “why does a longer prompt perform worse” 时使用。Prefer context-compression for 会话内压缩，Prefer memory-systems for 跨会话长期记忆；Do not use for 直接实现具体压缩流程或持久化记忆系统。

Default operating pattern

  1. Separate short-term context from durable memory or documentation.
  2. Define the minimum information model that must survive the current session.
  3. Choose storage, compression, retrieval, and update boundaries deliberately.
  4. Call out trade-offs, failure modes, and evaluation signals.
  5. Recommend the smallest next architecture or workflow improvement.

Pack fit

  • Included in: context-systems
  • Keep examples generic, privacy-safe, and portable across hosts.

Boundary

  • Do not stop at theory when the user really needs a concrete storage or retrieval design.

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