prd-implementation-precheck

Overview of prd-implementation-precheck skill

What prd-implementation-precheck does

prd-implementation-precheck is a guardrail skill for implementing a PRD or feature spec without jumping straight into code. It forces a short preflight review first: summarize intent, check for scope and consistency problems, raise missing details and risks, then wait for user confirmation before implementation.

Who should install this skill

This skill is best for teams and solo builders who regularly hand an AI a requirements doc and want fewer avoidable rewrites. It is especially useful when PRDs are incomplete, reference multiple files, or could trigger wide architectural changes if interpreted too literally.

The real job-to-be-done

The main value is not “implement faster.” It is “reduce bad implementation starts.” If your common failure mode is an agent coding the obvious interpretation of an underspecified PRD, prd-implementation-precheck for Requirements Planning is a better fit than a generic implementation prompt.

Why this skill is different from a normal prompt

A normal prompt often blends analysis and coding into one step. prd-implementation-precheck skill separates them:

1. find the PRD and related context
2. run a focused precheck
3. surface blockers and questions first
4. implement only after confirmation
5. validate or state what was not validated

That decision gate is the differentiator.

What it checks before coding

The repository centers the precheck on practical implementation risks:

• scope creep
• misalignment with existing patterns or architecture
• missing dependencies or unclear ownership
• underspecified behavior and edge cases
• regression, migration, or performance risks
• vague testing expectations

When this skill is a strong fit

Use prd-implementation-precheck when:

• the user says “implement this PRD/spec”
• the spec references existing systems or patterns
• you need the agent to ask clarifying questions before editing files
• minimizing change size matters
• you want explicit acknowledgment of what was and was not validated

When this skill is not the best fit

Skip it when:

• the task is a tiny one-file change with no ambiguity
• you already have an approved engineering plan
• you want brainstorming, not implementation
• the “PRD” is really just a rough idea with no actionable requirements yet

How to Use prd-implementation-precheck skill

Install context and repository path

The skill lives at skills/prd-implementation-precheck in zhaono1/agent-playbook:
https://github.com/zhaono1/agent-playbook/tree/main/skills/prd-implementation-precheck

The repository README shows a symlink-style install into a Claude skills directory. If you use a skills manager, adapt to your environment; if you install manually, point your skill entry to this skill's SKILL.md.

Files to read before trusting it in production

Read these first, in order:

1. skills/prd-implementation-precheck/SKILL.md
2. skills/prd-implementation-precheck/README.md

SKILL.md contains the real operating behavior: trigger intent, required workflow, allowed tools, and the precheck checklist. README.md is helpful for quick orientation and example usage.

How prd-implementation-precheck is triggered in practice

The trigger is straightforward: ask the agent to implement a PRD, feature spec, or requirements doc. Typical requests look like:

• Implement the PRD at docs/feature-prd.md
• Implement this spec, but review it first for gaps
• Use prd-implementation-precheck on docs/billing-upgrade.md

The key is giving a concrete document path or pasting the spec text.

What inputs the skill needs

For good output, provide:

• the PRD path or full text
• referenced files or relevant code areas
• constraints such as tech stack, deadlines, migration limits, or “minimal changes only”
• validation expectations such as tests to run or manual QA scope

Without those inputs, the skill can still precheck, but the questions will stay broad.

Turn a rough request into a strong prompt

Weak:

• Implement this PRD

Stronger:

• Use prd-implementation-precheck on docs/search-v2.md. Review scope, dependency gaps, edge cases, and testability first. Keep implementation minimal and consistent with existing patterns in app/search and shared/api. Ask for confirmation before editing files.

Why this works: it tells the skill what to inspect, what “good” means, and what parts of the codebase matter.

Recommended workflow for prd-implementation-precheck usage

A good usage pattern is:

1. point the agent to the PRD
2. ask for a 1-2 sentence intent summary
3. require blockers first, then non-blocking risks
4. answer the clarifying questions or narrow the scope
5. confirm implementation
6. ask for validation results and any unrun checks

This mirrors the repository workflow and keeps the skill useful rather than ceremonial.

What the precheck output should look like

A useful first response should include:

• a short summary of the PRD's intent
• a list of findings by category
• explicit questions where the PRD is incomplete
• a recommendation to proceed, proceed with assumptions, or wait

If the agent skips directly to coding, it is not really using prd-implementation-precheck as designed.

Practical prompt template

You can use this structure:

• Use prd-implementation-precheck for Requirements Planning on [PRD path].
• Summarize the intended change in 1-2 sentences.
• Check scope, alignment with current architecture, missing dependencies, undefined behavior, risks, and test expectations.
• List blockers first.
• Do not implement until I confirm.
• After confirmation, make minimal consistent changes and report validation performed.

Constraints that materially affect output quality

This skill performs better when you state:

• whether backward compatibility is required
• whether schema or migration changes are allowed
• whether the agent should favor existing patterns over ideal redesigns
• what “minimal change” means in your repo
• whether incomplete tests are acceptable

These constraints help the precheck identify the right risks instead of generic ones.

What to expect after confirmation

After approval, the skill is designed to implement with minimal, consistent changes and then validate via tests or manual steps. If validation cannot be run, the output should say so clearly rather than implying completion confidence it does not have.

prd-implementation-precheck skill FAQ

Is prd-implementation-precheck good for beginners?

Yes, if you already have a written PRD. The structure helps beginners avoid vague “build this” prompts. But it will not write a full product spec for you; it works better once requirements exist in some usable form.

How is this better than an ordinary implementation prompt?

The benefit is the forced pause before coding. Ordinary prompts often bury uncertainty until after code is written. prd-implementation-precheck usage surfaces ambiguity earlier, which is usually cheaper than rework.

Does this skill replace technical design review?

No. It is a lightweight implementation precheck, not a full architecture review process. It can catch obvious alignment and dependency problems, but it should not be treated as formal signoff for high-risk systems.

Can I use it for small tasks?

You can, but the overhead may not be worth it for trivial changes. It is best when a spec could be interpreted in multiple ways or touch several parts of the codebase.

What if my PRD is incomplete?

That is one of the best use cases. The skill should expose missing behavior, unclear dependencies, and test gaps before coding. If your team often writes “good enough” specs, this is exactly where the skill helps.

Does prd-implementation-precheck install include extra scripts or rules?

Based on the repository structure, this skill is document-driven. There are no extra rules/, resources/, or helper scripts in this skill folder, so most of the value comes from the workflow and checklist in SKILL.md.

When should I not use prd-implementation-precheck?

Do not use it when you need open-ended product ideation, when the work is already fully decomposed into precise engineering tasks, or when the cost of a precheck is higher than the cost of just making the change.

How to Improve prd-implementation-precheck skill

Give the skill a narrower implementation target

The biggest quality boost is scoping. Instead of “implement the PRD,” say:

• which app area is in scope
• what is explicitly out of scope
• whether data model or API changes are allowed

This reduces bloated precheck findings and keeps implementation closer to intent.

Provide repo-specific patterns to compare against

The skill checks alignment, but it needs something to align to. Point it to similar files, modules, or conventions:

• Follow the existing pattern in app/billing/subscriptions
• Do not introduce a new state manager
• Reuse current API error handling style

That produces sharper questions and fewer speculative warnings.

Ask for assumptions to be labeled clearly

A common failure mode is silent assumption-making. Improve prd-implementation-precheck skill output by asking the agent to separate:

• confirmed requirements
• inferred assumptions
• unresolved blockers

This makes approval easier and prevents accidental commitment to unstated behavior.

Strengthen the testing part of the prompt

The repository checklist includes testing, so use it. Tell the agent:

• what counts as done
• what tests should pass
• what manual checks matter
• whether no-test changes are acceptable

If you do not define success criteria, the implementation phase can look complete while remaining weakly validated.

Watch for over-generic risk lists

If the first precheck report reads like boilerplate, the usual cause is thin input. Fix that by adding:

• affected user flow
• expected behavior changes
• non-goals
• rollout or migration constraints

Better context leads to risk analysis that is specific enough to trust.

Iterate after the first precheck, not after the first code diff

The best way to improve results is to treat the precheck as a revision point. Answer the questions, tighten the PRD, then rerun or continue. Doing this before code starts preserves the main advantage of prd-implementation-precheck.

Pair it with explicit approval language

Because the skill is built around a confirmation gate, use direct commands:

• Proceed with assumptions A and B
• Do not change database schema
• Implement only phase 1
• Wait for another review after the plan

Clear approval language keeps the second phase controlled rather than open-ended.