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/rohitgehe05/mindpowers/validating-problemsnpx skills add rohitgehe05/mindpowers --skill validating-problemsgit clone --depth 1 https://github.com/rohitgehe05/mindpowersWhat 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 | $0.00041 | $0.02555 |
| Opus 5 | $0.00020 | $0.01277 |
| Sonnet 5 | $0.00008 | $0.00511 |
| Haiku 4.5 | $0.00004 | $0.00255 |
Grade A, and why
validating-problems 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.
How it starts
The opening of the file, as written. The whole thing — 214 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Validating problems
Overview
Determine what can defensibly be said about a customer or business problem before a team pitches a direction, prioritises work, or writes a PRD. Produce a scoped, solution-free problem definition whose claims remain traceable to evidence.
Keep problem validation separate from prioritisation and solution validation. Remain useful when evidence is incomplete without lowering the standard for calling a claim supported.
Explain the conclusion plainly
Think precisely and respond in common words. Keep the detailed assessment in the claim and evidence ledgers; do not dump that machinery into the conversation or present it as a scorecard.
For a material user-facing conclusion, give a compact reasoning receipt:
- lead with the conclusion or recommendation;
- say what evidence you checked and the main reasons;
- state the important uncertainty; and
- give one next step, ending with one concrete question when a response is needed.
Use internal status and schema terms only when they help the user act. Explain an unavoidable technical term on first use. When a rule could be misunderstood, give one short example. If the user says the explanation is unclear, explain again from scratch rather than defining the same jargon with more jargon.
Bound the decision
Establish or infer these boundaries before evaluating the problem:
decision_to_inform: Name the decision this work will support.desired_outcome: Name the customer or business condition that should improve.scope: Bound the exact users, workflow, context, and time period covered.out_of_scope: Exclude prioritisation, solution selection, and any other adjacent decision not being tested.
Do not force the user to restate a boundary already present in the conversation or available evidence. Narrow the claim when the evidence covers less than the proposed scope.
Inspect before asking
Inspect relevant conversation context, linked workspace files, research, analytics summaries, support material, and previous problem briefs before asking a question. Cite each inspected source or path in the evidence ledger. Never ask the user to transcribe evidence that can be accessed safely.
What ships with it
1 file 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.
- 2d ago First seen · 214 lines · 41 tokens per session scan A 11531a44dea4
validating-problems is a skill published in the GitHub repository rohitgehe05/mindpowers (5 stars, last pushed 24d ago), licensed MIT. It adds 41 tokens to every session and 2,555 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
Other skills, from other repositories
finishing-a-development-branch
Use when implementation is complete, all tests pass, and you need to decide how to integrate the work.
story-deslop
网文去AI味。检测并清除文本中的AI写作痕迹,让文字回归自然、非模板化。触发方式:/story-deslop、/去AI味、「去AI味」「这篇太AI了」「网文去AI味」。.
story
网络小说工具箱主入口。根据用户需求自动路由到对应 skill,并可管理作者习惯、启动本地 Dashboard。触发方式:/story、$story、/story dashboard、/网文、「我想写小说」「记住我的写作习惯」「打开工作台」「检查更新」。.
brainstorming-research-ideas
Guides researchers through structured ideation frameworks to discover high-impact research directions. Use when exploring new problem spaces, pivoting between projects, or seeking novel angles on existing work.
aws-wechat-article-topics
公众号选题|爆款标题|热点追踪|系列策划 — 公众号 AI 选题与标题生成,覆盖热点调研、选题策划、起标题、写摘要、系列排期。面向自媒体编辑、内容运营。触发词(单独触发仅限对已有标题/摘要的修改):「改标题」「换个标题」「重起标题」「优化标题」「标题再想想」「换个标题试试」「改摘要」「重写摘要」「优化摘要」「摘要再优化下」。新做选题、起新标题、策划系列/内容日历、追热点都请走 aws-wechat-article-main;需要多环节串联(写+审+排+配图+发)也走 main。.
higgsfield
Use this skill whenever the user asks anything about Higgsfield AI — writing or refining video/image prompts, choosing a model (Kling, Sora 2, Veo, Wan, Seedance, Minimax Hailuo, DoP, Soul, Nano Banana, Seedream, Flux, GPT Image, etc.), camera controls, named motion presets, Soul ID character consistency, Cinema…