assess-context
关于
The `assess-context` skill evaluates whether your current approach to a complex task is structurally sound or needs to change. It analyzes problem malleability, structural rigidity, and adaptation capacity to help you decide if you should push through or pivot. Use it as a meta-cognitive check when you're stuck, before major approach changes, or when accumulated workarounds hint at a flawed foundation.
快速安装
Claude Code
推荐npx skills add pjt222/agent-almanac -a claude-code/plugin add https://github.com/pjt222/agent-almanacgit clone https://github.com/pjt222/agent-almanac.git ~/.claude/skills/assess-context在 Claude Code 中复制并粘贴此命令以安装该技能
技能文档
Assess Context
Eval current reasoning ctx for malleability — ID rigid (no change), flexible (cheap change), where transformation pressure building, current approach has capacity to adapt.
Use When
- Complex task feels stuck, unclear push through or pivot
- Before significant approach change → assess current reasoning structure support
- Accumulated workarounds suggest underlying approach wrong
- After
healorawareness→ drift ID'd but appropriate response (continue, adjust, rebuild) unclear - Context grown long → unclear how much preserve vs rebuild
- Periodic structural health check during extended multi-step
In
- Required: Current task ctx + reasoning state (implicit)
- Optional: Specific concern triggering ("I keep adding workarounds")
- Optional: Proposed pivot direction (what approach need to become?)
- Optional: Previous results for trend
Do
Step 1: Inventory Reasoning Form
Catalog structural components no judgment.
Structural Inventory Table:
┌────────────────────┬──────────────┬──────────────────────────────────┐
│ Component │ Type │ Description │
├────────────────────┼──────────────┼──────────────────────────────────┤
│ Main task │ Skeleton │ The user's core request — cannot │
│ │ │ change without user direction │
├────────────────────┼──────────────┼──────────────────────────────────┤
│ Sub-task breakdown │ Flesh │ How the task is decomposed — │
│ │ │ can be restructured │
├────────────────────┼──────────────┼──────────────────────────────────┤
│ Tool strategy │ Flesh │ Which tools are being used and │
│ │ │ in what order — can be changed │
├────────────────────┼──────────────┼──────────────────────────────────┤
│ Output plan │ Flesh/Skel │ The expected deliverable format │
│ │ │ — may be constrained by user │
│ │ │ expectations │
├────────────────────┼──────────────┼──────────────────────────────────┤
│ Key assumptions │ Skeleton │ Facts treated as given — may be │
│ │ │ wrong but are load-bearing │
├────────────────────┼──────────────┼──────────────────────────────────┤
│ Constraints │ Skeleton │ Hard limits (user-imposed, tool │
│ │ │ limitations, time) │
├────────────────────┼──────────────┼──────────────────────────────────┤
│ Workarounds │ Scar tissue │ Patches for things that didn't │
│ │ │ work as expected — signals of │
│ │ │ structural stress │
└────────────────────┴──────────────┴──────────────────────────────────┘
Classify each:
- Skeleton: hard to change; changing cascades downstream
- Flesh: easy to change; swappable no affecting others
- Scar tissue: workarounds indicating structural problems; often flesh pretending skeleton
Map deps: which components depend on which? Skeleton w/ many dependents = load-bearing. Flesh w/ no dependents = disposable.
→ Complete inventory: what built from, what rigid, what flexible, where stress visible (workarounds). Reveals structure not obvious pre-catalog.
If err: Inventory hard to construct (too tangled) → that itself = finding. High opacity = high rigidity. Start w/ visible + note opacity zones.
Step 2: Map Transformation Pressure
Forces pushing toward change + forces resisting.
Pressure Map:
┌─────────────────────────┬──────────────────────────────────────────┐
│ External Pressure │ Forces from outside the reasoning │
│ (pushing toward change) │ │
├─────────────────────────┼──────────────────────────────────────────┤
│ New information │ Tool results or user input that │
│ │ contradicts current approach │
├─────────────────────────┼──────────────────────────────────────────┤
│ Tool contradictions │ Tools returning unexpected results that │
│ │ the current approach cannot explain │
├─────────────────────────┼──────────────────────────────────────────┤
│ Time pressure │ The current approach is too slow for the │
│ │ complexity of the task │
├─────────────────────────┼──────────────────────────────────────────┤
│ Internal Pressure │ Forces from within the reasoning │
│ (pushing toward change) │ │
├─────────────────────────┼──────────────────────────────────────────┤
│ Diminishing returns │ Each step yields less progress than the │
│ │ previous one │
├─────────────────────────┼──────────────────────────────────────────┤
│ Workaround accumulation │ The number of patches is growing — │
│ │ complexity is outpacing the structure │
├─────────────────────────┼──────────────────────────────────────────┤
│ Coherence loss │ Sub-tasks are not fitting together │
│ │ cleanly anymore │
├─────────────────────────┼──────────────────────────────────────────┤
│ Resistance │ Forces opposing change │
│ (pushing against change)│ │
├─────────────────────────┼──────────────────────────────────────────┤
│ Sunk cost │ Significant work already done on current │
│ │ approach — pivoting "wastes" that effort │
├─────────────────────────┼──────────────────────────────────────────┤
│ "Good enough" │ The current approach is producing │
│ │ acceptable (if not optimal) results │
├─────────────────────────┼──────────────────────────────────────────┤
│ Pivot cost │ Switching approaches means rebuilding │
│ │ context, losing momentum, potential │
│ │ confusion │
└─────────────────────────┴──────────────────────────────────────────┘
Estimate balance: pressure growing, stable, declining?
→ Clear picture of forces on approach. Pressure significantly > resistance → pivot overdue. Resistance significantly > pressure → approach continues.
If err: Pressure map ambiguous (neither strong) → project forward: pressures intensify? Workarounds compound? "Good enough now but degrading" under more pressure than appears.
Step 3: Assess Rigidity
How flexible is approach — can adapt or break?
Rigidity Score:
┌──────────────────────────┬─────┬──────────┬──────┬──────────────┐
│ Dimension │ Low │ Moderate │ High │ Assessment │
│ │ (1) │ (2) │ (3) │ │
├──────────────────────────┼─────┼──────────┼──────┼──────────────┤
│ Component swappability │ Can swap parts │ Changing one │ │
│ │ freely │ breaks others│ │
├──────────────────────────┼─────┼──────────┼──────┼──────────────┤
│ "God module" dependency │ No single point │ Everything │ │
│ │ of failure │ depends on │ │
│ │ │ one conclusion│ │
├──────────────────────────┼─────┼──────────┼──────┼──────────────┤
│ Tool entanglement │ Tools serve │ Approach is │ │
│ │ reasoning │ shaped by │ │
│ │ │ tool limits │ │
├──────────────────────────┼─────┼──────────┼──────┼──────────────┤
│ Assumption transparency │ Assumptions are │ Assumptions │ │
│ │ stated, testable │ are implicit, │ │
│ │ │ untested │ │
├──────────────────────────┼─────┼──────────┼──────┼──────────────┤
│ Workaround count │ None or few │ Multiple │ │
│ │ │ accumulating │ │
├──────────────────────────┼─────┼──────────┼──────┼──────────────┤
│ Total (max 15) │ 5-7: flexible │ │ │
│ │ 8-10: moderate │ │ │
│ │ 11-15: rigid │ │ │
└──────────────────────────┴─────┴──────────┴──────┴──────────────┘
→ Rigidity score + specific evidence per dim. Score reveals whether approach absorbs change or needs rebuild.
If err: All dims low (claiming high flexibility) → probe "god module" dim carefully: 1 key conclusion/assumption everything depends? If yes → flexibility illusory — 1 wrong assumption collapses whole.
Step 4: Estimate Change Capacity
Practical ability to pivot/adapt.
- Ctx window remaining: room for new reasoning? Extensive = high capacity. Approaching limits = low capacity
- Info preservation on pivot: approach changes → what carries forward? High-quality sub-task outputs survive pivots; reasoning chains tied to old approach do not
- Recovery tools: MEMORY.md capture key findings pre-pivoting? User provide additional ctx? Relevant files accessible?
- User patience: urgency indicated? Multi corrections → declining patience. Explicit "take your time" → high patience
Change capacity includes practical constraints of current session.
→ Honest assessment of ability to change course, both technical + relational.
If err: Low capacity (limited ctx, critical info at risk) → first priority pre-pivot = preservation: summarize key findings, note critical facts, update MEMORY.md. Pivoting no preservation worse than not pivoting.
Step 5: Classify Readiness
Combine into readiness classification.
Transformation Readiness Matrix:
┌─────────────────┬────────────────────────┬────────────────────────┐
│ │ Low Rigidity │ High Rigidity │
├─────────────────┼────────────────────────┼────────────────────────┤
│ High Pressure │ READY — pivot now. │ PREPARE — simplify │
│ + High Capacity │ The approach can adapt │ first. Remove │
│ │ and should. Preserve │ workarounds, clarify │
│ │ valuable sub-outputs, │ assumptions, then │
│ │ rebuild the structure │ pivot │
├─────────────────┼────────────────────────┼────────────────────────┤
│ High Pressure │ INVEST — preserve │ CRITICAL — ask the │
│ + Low Capacity │ findings first. Update │ user. Explain the │
│ │ MEMORY.md, summarize │ situation: approach is │
│ │ progress, then pivot │ struggling, pivoting │
│ │ with preserved context │ is costly, what do │
│ │ │ they want to prioritize?│
├─────────────────┼────────────────────────┼────────────────────────┤
│ Low Pressure │ DEFER — the approach │ DEFER — no urgency, │
│ + Any Capacity │ is working. Continue. │ continue. Monitor for │
│ │ Reassess if pressure │ pressure changes │
│ │ increases │ │
└─────────────────┴────────────────────────┴────────────────────────┘
Doc classification:
- Label (READY / PREPARE / INVEST / CRITICAL / DEFER)
- Key findings per dim
- Recommended next action
- Signal changing classification
→ Clear justified classification + specific recommended action. Feels like conclusion not guess.
If err: Ambiguous → default PREPARE — reducing rigidity (clarifying assumptions, removing workarounds) valuable regardless of full pivot happens. Prep improves approach whether continues or changes.
Check
- Structural inventory done w/ skeleton/flesh/scar-tissue classification
- Transformation pressures mapped (external, internal, resistance)
- Rigidity scored across multi dims + specific evidence
- Change capacity assessed including practical session constraints
- Readiness classification determined w/ justified reasoning
- Concrete next action ID'd per classification
- Reassessment trigger defined
Traps
- Assess only technical approach: Ctx readiness includes user relationship factors. Technically flexible but user-frustrated approach more rigid than appears
- Sunk cost as rigidity: Prior effort not structural rigidity. Work done may be valuable regardless. Distinguish "I can't change" (rigidity) from "I don't want to" (sunk cost)
- Assessment as avoidance: Invoking assess-context to avoid difficult decision → inconclusive by design. Pressure clear → act
- Ignore workarounds as signals: Workarounds = scar tissue — evidence structure stressed + patched not properly adapted. High count → next stress more likely to break through
- Confuse rigidity w/ commitment: Committed approach (deliberately chosen, evidence-based) diff from rigid (locked by deps + assumptions). Commitment changeable by decision; rigidity only by restructuring
→
assess-form— multi-system assessment model this adapts to AI reasoning ctxadapt-architecture— classified READY → use architectural adaptation principles for pivotheal— deeper subsystem scan when drift beyond structural issuescenter— establishes balanced baseline for honest assessmentcoordinate-reasoning— manages info freshness assessment depends on
GitHub 仓库
相关推荐技能
llamaguard
其他LlamaGuard是Meta推出的7-8B参数内容审核模型,专门用于过滤LLM的输入和输出内容。它能检测六大安全风险类别(暴力/仇恨、性内容、武器、违禁品、自残、犯罪计划),准确率达94-95%。开发者可通过HuggingFace、vLLM或Sagemaker快速部署,并能与NeMo Guardrails集成实现自动化安全防护。
cost-optimization
其他这个Claude Skill帮助开发者优化云成本,通过资源调整、标记策略和预留实例来降低AWS、Azure和GCP的开支。它适用于减少云支出、分析基础设施成本或实施成本治理策略的场景。关键功能包括提供成本可视化、资源规模调整指导和定价模型优化建议。
quantizing-models-bitsandbytes
其他这个Skill使用bitsandbytes库量化大语言模型,能在GPU内存有限时通过8位或4位量化减少50-75%内存占用,同时保持精度损失最小。它支持INT8、NF4、FP4等多种量化格式,可与HuggingFace Transformers无缝集成,适用于需要部署更大模型或加速推理的场景。还提供QLoRA训练和8位优化器支持,让开发者能轻松实现高效模型压缩。
dispatching-parallel-agents
其他该Skill用于并行处理3个以上无依赖关系的独立故障,可为每个问题域分派专属Claude代理同时执行调查修复。它通过并发处理多个独立问题显著提升故障排查效率,特别适用于测试文件、子系统等无共享状态的场景。
