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 skills add cnfeat/top-pm-skills --skill planning-under-uncertaintygit clone --depth 1 https://github.com/cnfeat/top-pm-skillsWrote 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/cnfeat/top-pm-skills/planning-under-uncertainty)<a href="https://agentmods.dev/skills/cnfeat/top-pm-skills/planning-under-uncertainty"><img src="https://agentmods.dev/badge/skills/cnfeat/top-pm-skills/planning-under-uncertainty/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/cnfeat/top-pm-skills/planning-under-uncertainty"><img src="https://agentmods.dev/badge/skills/cnfeat/top-pm-skills/planning-under-uncertainty.svg" alt="Reviewed on agentmods" width="80" 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 5.1 | $0.00053 | $0.00941 |
| Opus 5 | $0.00026 | $0.00470 |
| Sonnet 5 | $0.00011 | $0.00188 |
| Haiku 4.5 | $0.00005 | $0.00094 |
Grade A, and why
planning-under-uncertainty 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 12d 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 — 72 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Planning Under Uncertainty
Help the user navigate product planning when the future is unclear using adaptive planning frameworks from 44 product leaders.
How to Help
When the user asks for help with planning under uncertainty:
- Understand the uncertainty type - Ask what's driving the ambiguity: technical unknowns, market volatility, AI/ML unpredictability, or organizational change
- Assess planning horizon - Determine if they need short-term execution tactics or long-term strategic flexibility
- Match framework to context - Recommend appropriate planning approaches based on their uncertainty profile
- Build in adaptation mechanisms - Help them create checkpoints and decision criteria for pivoting
Core Principles
Embrace optionality over prediction
Amjad Masad: "Being agile, not being stuck with roadmaps, being able to just say, oh, we're just going to switch priorities right away, is going to be super important." In rapidly changing environments like AI, maintain flexibility to pivot when new capabilities emerge rather than committing to rigid long-term plans.
Build buffers for chaos
Upasna Gautam: "Any time we're planning we build in buffers for all of that chaos that's happening on a daily basis." In chaotic environments, planning must include explicit time buffers and contingency plans ranging from days to months depending on scope.
Use data as compass, not GPS
Shaun Clowes: "Data is more like a compass than a GPS. If you look at data as a way of giving you the answer, you're always wrong." Use data to validate or invalidate intuition rather than waiting for it to tell you exactly what to do.
Value learning over winning
Ramesh Johari: "Experimentation was never historically in science about winners and losers... Experimentation is always very hypothesis driven. It's about, what are you learning?" A healthy experimentation culture values learning from "failed" risky bets more than safe, incremental "wins."
Develop reproducible testing processes
Nikita Bier: "Develop a reproducible testing process, and that will actually influence the probability of your success more than anything." Success in uncertain markets is driven by the quality and speed of the testing process rather than the initial idea.
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.
- 12d ago First seen · 72 lines · 53 tokens per session scan A d5eb298edc77
planning-under-uncertainty is a skill published in the GitHub repository cnfeat/top-pm-skills (48 stars, last pushed 3mo ago), licensed MIT. It adds 53 tokens to every session and 941 once invoked, about $0.0003 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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