doc-condenser
**DEPRECATED** — Modern Claude models condense and summarize technical documentation natively with comparable quality. The output…
Pre-implementation plan audit stress-testing scope, assumptions, risks, and failure modes before code is written. Triggers on: "review this plan", "is this plan solid", "what am I missing", "challenge my assumptions", "stress-test this", "/plan-review".
Claude Code
$ npx -y skills add Mathews-Tom/armory --skill plan-review --agent claude-code
Codex
$ npx skills add Mathews-Tom/armory --skill plan-review -a codex -y
Cursor
$ npx skills add Mathews-Tom/armory --skill plan-review -a cursor -y
Gemini CLI
$ npx skills add Mathews-Tom/armory --skill plan-review -a gemini-cli -y
GitHub Copilot
$ npx skills add Mathews-Tom/armory --skill plan-review -a github-copilot -y
OpenCode
$ npx skills add Mathews-Tom/armory --skill plan-review -a opencode -y
Windsurf
$ npx skills add Mathews-Tom/armory --skill plan-review -a windsurf -y
name: plan-review description: 'Pre-implementation plan audit stress-testing scope, assumptions, risks, and failure modes before code is written. Triggers on: "review this plan", "is this plan solid", "what am I missing", "challenge my assumptions", "stress-test this", "/plan-review".' metadata: version: 1.1.0 category: review tags: [plan-audit, scope, risk-assessment, assumptions] difficulty: intermediate
Execute a structured pre-implementation audit of a technical plan, proposal, or design document. The goal is to surface risks, bad assumptions, missing pieces, and scope problems _before_ any code is written — when course corrections are cheapest.
This skill is read-only. It never modifies code. It produces a severity-tagged review document with a final ship/rethink/reject verdict.
Use the requested lens: Product, Engineering, or Combined. When none is specified, use Combined without a configuration question. Resolve facts from the plan and repository; ask only for a material decision the evidence cannot settle.
Also assess scope size from the plan:
4-section review — Scope, Risks, Missing, Verdict. Skip the full multi-section template.
Read the plan and its authoritative project goal. Summarize the intended user workflow, the next useful outcome, non-goals and constraints in 2–3 sentences. Identify a mismatch between project value and technically coherent milestones before reviewing implementation detail. Do not pause for confirmation unless an unresolved contradiction changes the outcome or safety.
Extract material assumptions that could change the next outcome or its safety. For each one:
| Assumption | If wrong? | Supporting evidence | What falsifies it? | | ---------- | --------- | ------------------- | ------------------ |
Common assumption categories to probe:
For components on the next delivery path, identify demonstrated failure risks:
| Component | Failure Mode | Blast Radius | Recovery Strategy | | --------- | ------------ | ------------ | ----------------- |
Additionally identify **data flow shadow paths** — side effects, async callbacks, event propagation, or cache invalidation chains that are not on the happy path but will execute in production.
Use ASCII diagrams to illustrate non-obvious data flow or failure propagation where the plan involves three or more interacting components.
For each major component, assess:
enforced? What happens when they break?
optimistic vs. pessimistic strategies, idempotency guarantees.
This section is language- and framework-agnostic. Reference `references/project-detection.md` for framework-aware examples when the user's stack is known.
Skip this section when running product-lens-only mode.
Skip this section when running engineering-lens-only mode.
How components connect to each other and to the outside world:
between modules.
synchronized? What happens during partial failure?
first? Are there intermediate states where the system is inconsistent?
independently? What data is irreversible?
Report a missing element only when a source requirement or concrete failing scenario makes it necessary for the current outcome. Consider security, data integrity, recovery, workload and operational needs at that boundary; do not turn this list into mandatory dashboards, runbooks, retries, benchmarks or extra qualification. Put optional improvements under non-blocking follow-ups, or omit them.
Curated, production-grade skills, agents, hooks, rules, commands, utilities, and presets for AI coding agents. No magic, no demos — battle-tested workflows built for developers who use AI seriously.
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