challenge

A structured review skill that looks for overlooked risks, missing assumptions, and dependencies in an agreed project description before planning begins. It is part of a development method that moves from an initial scope to a delivery summary.

In plain words
What is it for?
Pressure-testing a converged specification, recording blind spots, and routing proposed findings back into the planning process.
Why use it?
It helps expose issues that the team may not have considered before those issues become expensive surprises during implementation.

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/agentculture/learn-cli/challenge
Any agent
npx skills add agentculture/learn-cli --skill challenge
Clone the repo
git clone --depth 1 https://github.com/agentculture/learn-cli

Made for: Claude Code, Codex.

Per session 183 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 3,784 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin 100% copy Near-identical to another mod 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 $0.00183 $0.03784
Opus 5 $0.00092 $0.01892
Sonnet 5 $0.00037 $0.00757
Haiku 4.5 $0.00018 $0.00378

Measured yesterday against content hash 146e9299f6cc, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

challenge 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 yesterday.

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.

Origin

This is a copy

100% identical to challenge — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.

.claude/skills/challenge/SKILL.md · 291 lines

How it starts

The opening of the file, as written. The whole thing — 291 lines — stays where its author put it; the contents beside it link to each section on GitHub.

challenge — hunt the blind spots before the plan inherits them

The skill is named challenge; it is the blind-spot discovery leg of the devague method — the seventh origin skill, sitting third in flow order, between the spec leg and the plan leg:

scope -> think -> challenge -> spec-to-plan -> assign-to-workforce -> deviate -> summarize-delivery

Before this leg existed, a frame could converge on precisely stated claims while the original framing was still incomplete: open questions only captured uncertainty someone had already noticed — nothing actively hunted omitted dimensions, hidden dependencies, or assumptions shared by everyone in the frame, and no record existed of which surfaces were ever examined (issue 73's problem statement). Strictly speaking, an unknown unknown cannot be listed directly — once articulated, it becomes a known unknown. The useful capability is therefore to raise the odds of discovering blind spots and lower the cost of the surprises that remain, not to promise their elimination.

That is the surprise-cost rationale: an articulated blind spot becomes a known unknown the method can manage; an unexamined one surfaces later as a mid-run /deviate or a production surprise. Discovery before planning is cheaper than either — a proposed claim the human rejects costs minutes; the same gap found mid-fan-out stops a wave, and found in production it costs whatever the blast radius costs.

This doc is written for two readers. The operator — the main agent — runs the pass: sweeps the lenses, drives the deterministic CLI move by move, and proposes findings. The gate-owning human adjudicates: every finding lands proposed, and confirming, rejecting, or resolving it is the human exercising the existing spec gate (gate 1) — challenge adds no fourth gate, mirroring how /deviate amends gate 2 rather than adding one.

When it runs

The timing is a recorded decision — quote it, don't re-derive it:

the challenge pass runs after /think exports: challenge the converged, exported frame before devague plan new; findings reopen the frame, reconverge, and re-export the same dated spec file — /think stays self-contained (resolves q1)

Read the full file on GitHub · 291 lines

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. yesterday First seen · 291 lines · 183 tokens per session scan A 146e9299f6cc

Subscribe to this mod's changes

challenge is a skill published in the GitHub repository agentculture/learn-cli (1 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 183 tokens to every session and 3,784 once invoked, about $0.0009 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to challenge, differing in 0 lines, and is treated as a copy.

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