problem-framing

A requirements-analysis skill that turns unclear product, bug, design, architecture, data, or permission questions into a specific decision and an acceptable outcome.

In plain words
What is it for?
Use it to diagnose inconsistent workflows, missing or uneditable capabilities, unclear ownership, API or state questions, and choices that affect product behaviour.
Why use it?
It prevents implementation from starting before the problem, scope, owner, constraints, and success criteria are understood.

Skill for Claude CodeCodex

Install

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.

agentmods
npx agentmods add skills/taichuy/1flowbase/problem-framing
Any agent
npx skills add taichuy/1flowbase --skill problem-framing
Clone the repo
git clone --depth 1 https://github.com/taichuy/1flowbase

Made for: Claude Code, Codex.

Per session 171 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,999 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5 $0.00171 $0.01999
Opus 5 $0.00086 $0.01000
Sonnet 5 $0.00034 $0.00400
Haiku 4.5 $0.00017 $0.00200

Measured 2d ago against content hash 18ef67b3fc03, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

problem-framing 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 2d 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.

.agents/skills/problem-framing/SKILL.md · 131 lines

How it starts

The opening of the file, as written. The whole thing — 131 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Problem Framing

Outcome

把请求收敛成可决策、可执行、可验收的结果,不替实现者规定完整路径。完成时,现状有证据,结果可观察,范围、owner 与授权闭合,验证足以结算风险,并有唯一建议和停止条件。

本 Skill 只形成决策,不修改产品代码、测试、migration、schema 或运行时行为。

Trigger Boundary

  • 把“为什么新增和编辑不一致”“为什么某能力看不见 / 不能改”“接口或状态应由谁拥有”“这是缺陷还是设计边界”等请求视为需求对齐,即使用户使用“看看原因”“诊断一下”等查询措辞。
  • 只有答案不会改变产品行为、交互、contract、owner、成功标准或后续改动方向时,才把请求视为可跳过的事实查询。
  • 在完成本 Skill 的决策输出前,不进入 implementation / QA Skill,也不让专项代码探索替代需求分析;需要证据时只获取会改变方向的最小证据。

Reasoning Catalysts

先推理后结论:先定义理想结果,再用第一性原理拆出事实、隐藏因果、硬约束与失败模式;用奥卡姆剃刀选择足以解释证据的最小机制;先升温发散真实方向,再降温收敛唯一建议,最终通俗易懂但不牺牲准确性。

这里的“先推理”指先输出可核验的现状与需求分析摘要,再给方向和最终建议;不以“结论 / 建议”开头,也不展示内部思维链。

优先用高信息关系、反例或最小案例催化,不堆模型已知常识和同义说明。催化词只改变搜索方向,不替代证据、领域精度或硬边界,也不作为口号复述。

Architecture Catalysts

  • Deep Modules / Information Hiding:公共接口只暴露调用方决策所需的最小充分信息;状态判断、协议细节与兼容分支留在内部。
  • Conservation of Complexity + Requisite VarietyTesler's Law / Ashby's Law):必要复杂度不能消失;由拥有足够状态与动作空间的语义 owner 吸收。
  • Observability × Controllability ⇒ Ownership:看不见相关状态或不能控制其转移的模块,不拥有该复杂度。
  • Proven Mechanisms over Ad-hoc Rules:优先成熟数学关系、算法、数据结构、状态机、约束与调度机制,不用临时规则堆叠代替。

用以下 complexity placement heuristic 选择 owner;这是本 Skill 的架构判定式,不是经典控制论原公式:

owner*(x) =
  argmin_m [C_leak(m) + C_coordination(m) + C_failure(m)]

subject to:
  SourceOfTruth_m(x)
  ∧ Observable_m(x)
  ∧ Controllable_m(x)
  ∧ Variety_m ≥ Variety_x

C_leak 是泄漏给调用方的兼容、分支与隐式约定;C_coordination 是跨 owner 协调成本;C_failure 是复杂度错置造成的失败成本。

Decision Field

把请求看作受约束决策;用关系筛选内容,不机械复述检查字段,但最终答复必须遵守 Response Contract:

证据 -> 现状 -> 与目标的差距 -> 可观察成功标准
source of truth / owner -> 必要复杂度
授权 / contract -> 可行方向
失败风险 -> 验证强度
潜在决策变化 × 影响 > 获取成本 -> 新证据

维持以下守恒关系:

  • 用户描述提供线索,结论强度不超过证据;安全、数据、权限与已确认 contract 是硬边界,工作偏好只改变方向权重。
  • 方案范围不超过授权与非目标;新增范围同时产生成功标准、owner、证据责任和资源边界。
  • 只处理会改变可行域或推荐的未知;其他缺口使用有界假设。下一步不能减少决策残差时停止。
  • 后端是 contract 与状态唯一数据来源;前端不承担输出兼容,接口字段保持后端 DTO / 领域语义原名。

Control Loop

[证据与结果差距] -> [三个真实方向] -> [唯一建议] -> [用户决策]
       ^                                      |
       +---- 边界、语义或授权发生变化 --------+

Read the full file on GitHub · 131 lines

Files

What ships with it

7 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.

Changes

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.

  1. 2d ago First seen · 131 lines · 171 tokens per session scan A 18ef67b3fc03

Subscribe to this mod's changes

problem-framing is a skill published in the GitHub repository taichuy/1flowbase (259 stars, last pushed 3d ago), licensed Apache-2.0. It adds 171 tokens to every session and 1,999 once invoked, about $0.0009 per session on Opus 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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