especialista-em-discovery-driven-planning

especialista-em-discovery-driven-planning is a skill for Claude Code from euwebertdefreitas/ai-skills-for-claude-code. It costs 0 tokens per session (487 once invoked), scanned A, original, MIT.

A planning guide for projects where important assumptions are uncertain. Discovery-driven planning tests what must be true before committing more time or money.

In plain words
What is it for?
Ranking assumptions by risk, designing inexpensive tests, setting learning checkpoints, and planning backward from desired results.
Why use it?
It reduces the risk of building a detailed plan around guesses that have not been checked.

Skill for Claude Code

Written for Claude Code: when-to-use in frontmatter.

Good fit Ranking assumptions by risk, designing inexpensive tests, setting learning checkpoints, and planning backward from desired results.

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Install with agentmods
npx agentmods add skills/euwebertdefreitas/ai-skills-for-claude-code/especialista-em-discovery-driven-planning
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.

Any agent
npx skills add euwebertdefreitas/ai-skills-for-claude-code --skill especialista-em-discovery-driven-planning
Clone the repo
git clone --depth 1 https://github.com/euwebertdefreitas/ai-skills-for-claude-code

Made for: Claude Code.

Wrote 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.

agentmods badge for especialista-em-discovery-driven-planning

README.md
[![agentmods](https://agentmods.dev/badge/skills/euwebertdefreitas/ai-skills-for-claude-code/especialista-em-discovery-driven-planning/github.svg)](https://agentmods.dev/skills/euwebertdefreitas/ai-skills-for-claude-code/especialista-em-discovery-driven-planning)
Your own site
<a href="https://agentmods.dev/skills/euwebertdefreitas/ai-skills-for-claude-code/especialista-em-discovery-driven-planning"><img src="https://agentmods.dev/badge/skills/euwebertdefreitas/ai-skills-for-claude-code/especialista-em-discovery-driven-planning/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.

agentmods 80×15 button for especialista-em-discovery-driven-planning

Your own site · 80×15
<a href="https://agentmods.dev/skills/euwebertdefreitas/ai-skills-for-claude-code/especialista-em-discovery-driven-planning"><img src="https://agentmods.dev/badge/skills/euwebertdefreitas/ai-skills-for-claude-code/especialista-em-discovery-driven-planning.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 0 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 487 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
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 5.1 $0.00000 $0.00487
Opus 5 $0.00000 $0.00244
Sonnet 5 $0.00000 $0.00097
Haiku 4.5 $0.00000 $0.00049

Measured 12d ago against content hash c3d14316d891, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

especialista-em-discovery-driven-planning 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.

skills/especialista-em-discovery-driven-planning/SKILL.md · 44 lines

What it actually says

Expert in Discovery-Driven Planning

Identity / Role

You are a senior Discovery-Driven Planning specialist. Give opinionated, production-grade guidance and explain trade-offs, not just options. Be concrete and decisive; recommend, don't just enumerate.

When to use

  • Plan ventures under high uncertainty
  • Surface and test key assumptions cheaply
  • Use reverse income statements and checkpoints

Out of scope: User-centered innovation (design-thinking) and product discovery (gestao-de-produto).

Core principles

  1. Plan to learn — assumptions, not forecasts, drive the plan.
  2. Make assumptions explicit and rank by risk.
  3. Define checkpoints to test before spending more.
  4. Convert assumptions to knowledge incrementally.

Workflow / Process

  1. Clarify — confirm the goal, constraints, and current state before acting.
  2. Assess — inspect what exists; find the real problem, not the symptom.
  3. Design — propose an approach with explicit trade-offs and a clear recommendation.
  4. Execute — implement in small, verifiable steps using Discovery-Driven Planning conventions.
  5. Verify — validate against assumption tests pass at checkpoints before further commitment.

Best practices

  • Build an assumption checklist tied to the plan.
  • Use reverse income statements (start from required outcome).
  • Set milestone checkpoints to validate/kill.
  • Re-plan as assumptions become facts.

Anti-patterns

  • Treating projections as facts in new ventures.
  • Spending the full budget before testing key risks.
  • Ignoring disconfirming evidence at checkpoints.

Reference

For depth — key concepts, tooling/stack, checklists, and pitfalls — read reference.md in this skill folder. Load it only when the task needs that depth.

Files

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.

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. 12d ago First seen · 44 lines · 0 tokens per session scan A c3d14316d891

Subscribe to this mod's changes

especialista-em-discovery-driven-planning is a skill published in the GitHub repository euwebertdefreitas/ai-skills-for-claude-code (8 stars, last pushed 3mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 487 tokens. 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.

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