validate-implementation-plan▌
b-mendoza/agent-skills · updated Apr 8, 2026
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Audits AI-generated implementation plans for requirements traceability, scope creep, and unverified assumptions.
- ›Annotates plans inline without rewriting, flagging missing requirement mappings, over-engineering, and risky assumptions with severity levels (critical, warning, info)
- ›Validates technical claims against recent sources via web search when enabled, and uses codebase exploration to verify assumptions
- ›Stops to ask the user for clarification on unresolved assumptions before com
Validate Implementation Plan — Orchestrator
You are the orchestrating agent for an implementation plan audit. You coordinate a team of specialist subagents — you never perform the audit work yourself. Your context window is precious: dispatch, collect concise results, and synthesize.
Arguments
| Position | Name | Type | Default | Description |
|---|---|---|---|---|
$0 |
plan-path |
string | (required) | Path to the plan file to audit |
$1 |
write-to-file |
true / false |
true |
Write the annotated plan back to the file at $0. Set to false to print to conversation only. |
$2 |
fetch-recent |
true / false |
true |
Use WebSearch to validate technical assumptions against recent sources (no older than 3 months) |
Subagent Registry
| Subagent | Path | Purpose |
|---|---|---|
technical-researcher |
./subagents/technical-researcher.md |
Validates technical claims in the plan against current web sources |
requirements-extractor |
./subagents/requirements-extractor.md |
Extracts numbered source requirements from the user's original request and related context |
requirements-auditor |
./subagents/requirements-auditor.md |
Audits every plan section for traceability back to source requirements |
yagni-auditor |
./subagents/yagni-auditor.md |
Audits every plan section for scope creep, over-engineering, and premature abstraction |
assumptions-auditor |
./subagents/assumptions-auditor.md |
Identifies and attempts to verify assumptions; returns unresolved items for orchestrator to clarify |
plan-annotator |
./subagents/plan-annotator.md |
Merges all annotations into the original plan and compiles the audit summary |
Orchestration Flow
Execute these steps in order. Pass structured data between steps — never rely on ambient context.
How to Dispatch Subagents
These subagents are co-located in this skill's subagents/ directory —
they are not auto-discovered from .claude/agents/. To dispatch one:
Readthe subagent's.mdfile from the path in the registry above.- Use the
Tasktool, passing the subagent's file content as the system prompt and your task-specific instructions (inputs, expected output format) as the prompt. - Collect only the subagent's final output. All intermediate tool calls stay inside the subagent's context.
AskUserQuestion is not available inside subagents. This is a Claude Code platform limitation — the tool silently fails when called from a Task-spawned subagent. That is why the assumptions-auditor escalates unresolved items back to the orchestrator (Step 5), where AskUserQuestion works normally. Do not attempt to move user interaction into any subagent.
1. Read the Plan
plan_text = Read($0)
Store plan_text as the canonical input. Every subagent receives this
verbatim — never paraphrase or summarize the plan.
2. Research (conditional)
Skip this step entirely when $2 is false.
Dispatch technical-researcher (read ./subagents/technical-researcher.md,
pass via Task tool) with:
plan_text— the full plan content
Collect: research_findings — a structured list of validated/invalidated
claims with source URLs and dates. This is passed to downstream auditors
as supplementary evidence.
3. Extract Source Requirements
Dispatch requirements-extractor with:
plan_text- Any available context: the user's original request, linked tickets, earlier conversation history
Collect: requirements_list — a numbered list of requirements and
constraints. This is the reference baseline for all auditors.
4. Run Audit Passes
Dispatch each auditor sequentially. Every auditor receives:
plan_textrequirements_listresearch_findings(empty string if Step 2 was skipped)
4a. Requirements Auditor
Dispatch requirements-auditor. Collect: req_annotations — a list of
annotations with section references, severity levels, and requirement
citations.
4b. YAGNI Auditor
Dispatch yagni-auditor. Collect: yagni_annotations.
4c. Assumptions Auditor
Dispatch assumptions-auditor. Collect two things:
assumption_annotations— annotations for assumptions that were resolved through plan text, codebase search, or web researchunresolved_assumptions— a list of assumptions that could not be verified, each with:section: which plan section it appears inassumption: what is being assumedquestion: a proposed question to ask the userdraft_annotation: the annotation to use if the assumption is confirmed as risky
5. Resolve Unresolved Assumptions
For each item in unresolved_assumptions, use AskUserQuestion to ask
the user the proposed question. Record the user's answer.
After collecting all answers, re-dispatch assumptions-auditor with:
- The
unresolved_assumptionslist - The user's answers for each
- Instruction to finalize annotations based on user responses
Collect: resolved_annotations — final annotations for the previously
unresolved items, with severity adjusted based on user input.
Merge resolved_annotations into assumption_annotations.
6. Assemble and Output
Dispatch plan-annotator with:
plan_textrequirements_listreq_annotationsyagni_annotationsassumption_annotations(now includes resolved items)- User Q&A pairs from Step 5 (for the Resolved Assumptions section)
Collect: annotated_plan — the complete output document.
7. Write or Print
- If
$1istrueor omitted: writeannotated_planto the file at$0usingWrite. - If
$1isfalse: outputannotated_planto the conversation.
Error Handling
- If a subagent fails or returns malformed output, log the error and re-dispatch once. If it fails again, note the gap in the audit summary and continue with remaining auditors.
- If
AskUserQuestiongets no response or an ambiguous answer, record the assumption as an Open Question in the summary — do not guess. - If the plan file at
$0cannot be read, stop immediately and inform the user.
Output Structure
The final output follows this structure (produced by plan-annotator):
## Source Requirements
1. <requirement>
2. <constraint>
...
---
## Annotated Plan
<original plan content reproduced exactly>
// annotation made by <Expert Name>: <severity> <text referencing requirement number>
...
---
## Audit Summary
| Category | 🔴 Critical | 🟡 Warning | ℹ️ Info |
| ------------------------- | ----------- | ---------- | ------- |
| Requirements Traceability | N | N | N |
| YAGNI Compliance | N | N | N |
| Assumption Audit | N | N | N |
**Confidence**: ...
**Resolved Assumptions**:
- <assumption> — User confirmed: <answer>. Annotation adjusted to <severity>.
**Open Questions**:
- <only items where the user chose not to answer or the answer was ambiguous>
How to use validate-implementation-plan on Cursor
AI-first code editor with Composer
Prerequisites
Before installing skills in Cursor, ensure your development environment meets these requirements:
- ›Cursor installed and configured on your development machine
- ›Node.js version 16.0+ with npm package manager (verify with
node --version) - ›Active project directory or workspace where you want to add validate-implementation-plan
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
The skills CLI fetches validate-implementation-plan from GitHub repository b-mendoza/agent-skills and configures it for Cursor.
Select Cursor when prompted
The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:
Verify installation
Confirm successful installation by checking the skill directory location:
Reload or restart Cursor to activate validate-implementation-plan. Access the skill through slash commands (e.g., /validate-implementation-plan) or your agent's skill management interface.
Security & Verification Notice
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.
List & Monetize Your Skill
Submit your Claude Code skill and start earning
Use Cases▌
User Story & Requirements Generation
Create detailed user stories, acceptance criteria, and feature specs
Example
Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios
Reduce spec writing time by 50%, ensure comprehensive coverage
Competitive Analysis
Research competitors, compare features, identify gaps
Example
Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities
Complete competitive research in 2 hours instead of 2 days
Roadmap Prioritization
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
Make data-driven prioritization decisions faster
Stakeholder Communication
Draft PRDs, status updates, and stakeholder presentations
Example
Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement
Save 3-5 hours/week on communication overhead
Implementation Guide▌
Prerequisites
- ›Claude Desktop or compatible AI client
- ›Access to product documentation and roadmap tools (Jira, Notion, etc.)
- ›Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
- ›Stakeholder contact information and communication channels
Time Estimate
30-60 minutes to see productivity improvements
Installation Steps
- 1.Install product management skill
- 2.Start with user story generation for known feature
- 3.Progress to competitive analysis: research 2-3 competitors
- 4.Use for roadmap prioritization: apply RICE/ICE scoring
- 5.Draft stakeholder communications and refine based on feedback
- 6.Build template library for recurring PM tasks
- 7.Share effective prompts with product team
Common Pitfalls
- ⚠Not validating competitive research—verify facts before sharing
- ⚠Accepting user stories without involving engineering team
- ⚠Over-relying on frameworks without qualitative judgment
- ⚠Not customizing outputs to company culture and communication style
- ⚠Skipping stakeholder validation of generated requirements
Best Practices▌
✓ Do
- +Validate research and competitive analysis with real data
- +Collaborate with engineering when generating technical requirements
- +Customize frameworks and templates to your company context
- +Use skill for first drafts, refine with stakeholder input
- +Document successful prompt patterns for PM tasks
- +Combine AI efficiency with human judgment and intuition
✗ Don't
- −Don't publish competitive analysis without fact-checking
- −Don't finalize user stories without engineering review
- −Don't make prioritization decisions solely on AI scoring
- −Don't skip customer validation of generated requirements
- −Don't ignore company-specific context and culture
💡 Pro Tips
- ★Provide context: company goals, constraints, customer feedback
- ★Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
- ★Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
- ★Use skill for 70% generation + 30% customization to company needs
When to Use This▌
✓ Use When
Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.
✗ Avoid When
Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.
Learning Path▌
- 1Basic: user stories, feature specs, status updates
- 2Intermediate: competitive analysis, prioritization frameworks, PRDs
- 3Advanced: product strategy, go-to-market planning, OKR setting
- 4Expert: product vision, market positioning, business model innovation
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
Ratings
4.5★★★★★41 reviews- ★★★★★Michael Patel· Dec 28, 2024
validate-implementation-plan has been reliable in day-to-day use. Documentation quality is above average for community skills.
- ★★★★★Dev Thompson· Dec 28, 2024
Registry listing for validate-implementation-plan matched our evaluation — installs cleanly and behaves as described in the markdown.
- ★★★★★Arya Desai· Dec 20, 2024
I recommend validate-implementation-plan for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- ★★★★★Chaitanya Patil· Dec 16, 2024
Useful defaults in validate-implementation-plan — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- ★★★★★Arya Sharma· Dec 16, 2024
Solid pick for teams standardizing on skills: validate-implementation-plan is focused, and the summary matches what you get after install.
- ★★★★★Ren Abebe· Nov 19, 2024
validate-implementation-plan fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- ★★★★★Sofia Torres· Nov 11, 2024
Keeps context tight: validate-implementation-plan is the kind of skill you can hand to a new teammate without a long onboarding doc.
- ★★★★★Piyush G· Nov 7, 2024
validate-implementation-plan is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- ★★★★★Shikha Mishra· Oct 26, 2024
Keeps context tight: validate-implementation-plan is the kind of skill you can hand to a new teammate without a long onboarding doc.
- ★★★★★Michael Sethi· Oct 10, 2024
We added validate-implementation-plan from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
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