MCP Server Builder
anthropics/skills
Guides the design and implementation of Model Context Protocol servers in TypeScript or Python, from tool naming and error messages to evaluation.
Validation agent that validates plan tech choices against current best practices
Install Validate Agent
$ npx skills add parcadei/Continuous-Claude-v3 --skill validate-agent -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install parcadei/Continuous-Claude-v3 validate-agent --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/parcadei/Continuous-Claude-v3.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.claude/skills/validate-agent .claude/skills/validate-agent && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "validate-agent" agent skill from https://github.com/parcadei/Continuous-Claude-v3/tree/main/.claude/skills/validate-agent into .claude/skills/validate-agent/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "validate-agent", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/parcadei/Continuous-Claude-v3/tree/main/.claude/skills/validate-agentType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add parcadei/Continuous-Claude-v3 --skill validate-agent -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install parcadei/Continuous-Claude-v3 validate-agent --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/parcadei/Continuous-Claude-v3.git skills-src && mkdir -p .agents/skills && cp -r skills-src/.claude/skills/validate-agent .agents/skills/validate-agent && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "validate-agent" agent skill from https://github.com/parcadei/Continuous-Claude-v3/tree/main/.claude/skills/validate-agent into .agents/skills/validate-agent/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "validate-agent", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add parcadei/Continuous-Claude-v3 --skill validate-agent -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install parcadei/Continuous-Claude-v3 validate-agent --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/parcadei/Continuous-Claude-v3.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/.claude/skills/validate-agent .cursor/skills/validate-agent && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "validate-agent" agent skill from https://github.com/parcadei/Continuous-Claude-v3/tree/main/.claude/skills/validate-agent into .cursor/skills/validate-agent/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "validate-agent", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/parcadei/Continuous-Claude-v3.git --path .claude/skills/validate-agent--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add parcadei/Continuous-Claude-v3 --skill validate-agent -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install parcadei/Continuous-Claude-v3 validate-agent --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/parcadei/Continuous-Claude-v3.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/.claude/skills/validate-agent .gemini/skills/validate-agent && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "validate-agent" agent skill from https://github.com/parcadei/Continuous-Claude-v3/tree/main/.claude/skills/validate-agent into .gemini/skills/validate-agent/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "validate-agent", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install parcadei/Continuous-Claude-v3 validate-agentInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add parcadei/Continuous-Claude-v3 --skill validate-agent -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/parcadei/Continuous-Claude-v3.git skills-src && mkdir -p .github/skills && cp -r skills-src/.claude/skills/validate-agent .github/skills/validate-agent && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "validate-agent" agent skill from https://github.com/parcadei/Continuous-Claude-v3/tree/main/.claude/skills/validate-agent into .github/skills/validate-agent/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "validate-agent", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add parcadei/Continuous-Claude-v3 --skill validate-agent -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install parcadei/Continuous-Claude-v3 validate-agent --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/parcadei/Continuous-Claude-v3.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/.claude/skills/validate-agent .opencode/skills/validate-agent && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "validate-agent" agent skill from https://github.com/parcadei/Continuous-Claude-v3/tree/main/.claude/skills/validate-agent into .opencode/skills/validate-agent/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "validate-agent", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
validate-agent- GitHub stars
- 3.9k
- Used in
- 2 other repos
- Token cost
- ~1.5k tokens
- SKILL.md length
- 415 words
- Files
- 1
- Skills in repo
- 141
- Repo updated
- First seen
- Licence
- MIT
At a glance
Validation agent that validates plan tech choices against current best practices
- Works in 5 steps: Extract Tech Choices → Check Past Precedent (RAG-Judge) → Research Each Choice (WebSearch) → …
- Agent Workflows work in your project
- SKILL.md covers What You Receive, Your Process, Returning to Orchestrator and Important Guidelines, plus 2 more sections
- Calls uv
What it does
Validate Agent is an agent skill from parcadei/Continuous-Claude-v3. Validation agent that validates plan tech choices against current best practices
Its SKILL.md is about 1.5k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.
It sits in Agent Workflows. The repository describes itself as: Context management for Claude Code. Hooks maintain state via ledgers and handoffs. MCP execution without context pollution. Agent orchestration with isolated context windows. The licence is MIT.
When your agent uses it
- Agent Workflows work in your project
Example prompts
- “/validate-agent”
Requirements
- Python 3
Workflow steps
5 steps, taken from the step headings in SKILL.md.
What it can do on your machine
Read from SKILL.md and the folder at commit d07ff4b. It shows what the files ask for, not the result of running them.
Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
Shell commands in SKILL.md call:
uv
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md. Its commands use uv, which can reach the network depending on how they are called.
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Context cost
Validate Agent loads about 1.5k tokens when it runs. Until then it costs about 24 tokens; SKILL.md has 415 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
Safety
Auto-check passed
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.
SKILL.md
The full file from parcadei/Continuous-Claude-v3 at commit d07ff4b, republished under its MIT licence (© parcadei). 415 words, ~1,493 tokens.
Download SKILL.mdSave it as
.claude/skills/validate-agent/SKILL.md (or your agent's skills folder).- name
- validate-agent
- description
- Validation agent that validates plan tech choices against current best practices
Note: The current year is 2025. When validating tech choices, check against 2024-2025 best practices.
Validate Agent
You are a validation agent spawned to validate a technical plan's choices against current best practices. You research external sources to verify the plan's technology decisions are sound, then write a validation handoff.
What You Receive
When spawned, you will receive:
- Plan content - The implementation plan to validate
- Plan path - Location of the plan file
- Handoff directory - Where to save your validation handoff
Your Process
Step 1: Extract Tech Choices
Read the plan and identify all technical decisions:
- Libraries/frameworks chosen
- Patterns/architectures proposed
- APIs or external services used
- Implementation approaches
Create a list like:
Tech Choices to Validate:
1. [Library X] for [purpose]
2. [Pattern Y] for [purpose]
3. [API Z] for [purpose]Step 2: Check Past Precedent (RAG-Judge)
Before web research, check if we've done similar work before:
# Query Artifact Index for relevant past work
uv run python scripts/braintrust_analyze.py --rag-judge --plan-file <plan-path>This returns:
- Succeeded handoffs - Past work that worked (patterns to follow)
- Failed handoffs - Past work that failed (patterns to avoid)
- Gaps identified - Issues the plan may be missing
If RAG-judge finds critical gaps (verdict: FAIL), note these for the final report.
Step 3: Research Each Choice (WebSearch)
For each tech choice, use WebSearch to validate:
WebSearch(query="[library/pattern] best practices 2024 2025")
WebSearch(query="[library] vs alternatives [year]")
WebSearch(query="[pattern] deprecated OR recommended [year]")Check for:
- Is this still the recommended approach?
- Are there better alternatives now?
- Any known deprecations or issues?
- Security concerns?
Step 4: Assess Findings
For each tech choice, determine:
- VALID - Current best practice, no issues
- OUTDATED - Better alternatives exist
- DEPRECATED - Should not use
- RISKY - Security or stability concerns
- UNKNOWN - Couldn't find enough info (note as assumption)
Step 5: Create Validation Handoff
Write your validation to the handoff directory.
Handoff filename: validation-<plan-name>.md
---
date: [ISO timestamp]
type: validation
status: [VALIDATED | NEEDS REVIEW]
plan_file: [path to plan]
---
# Plan Validation: [Plan Name]
## Overall Status: [VALIDATED | NEEDS REVIEW]
## Precedent Check (RAG-Judge)
**Verdict:** [PASS | FAIL]
### Relevant Past Work:
- [Session/handoff that succeeded with similar approach]
- [Session/handoff that failed - pattern to avoid]
### Gaps Identified:
- [Gap 1 from RAG-judge, if any]
- [Gap 2 from RAG-judge, if any]
(If no relevant precedent: "No similar past work found in Artifact Index")
## Tech Choices Validated
### 1. [Tech Choice]
**Purpose:** [What it's used for in the plan]
**Status:** [VALID | OUTDATED | DEPRECATED | RISKY | UNKNOWN]
**Findings:**
- [Finding 1]
- [Finding 2]
**Recommendation:** [Keep as-is | Consider alternative | Must change]
**Sources:** [URLs]
### 2. [Tech Choice]
[Same structure...]
## Summary
### Validated (Safe to Proceed):
- [Choice 1] ✓
- [Choice 2] ✓
### Needs Review:
- [Choice 3] - [Brief reason]
- [Choice 4] - [Brief reason]
### Must Change:
- [Choice 5] - [Brief reason and suggested alternative]
## Recommendations
[If NEEDS REVIEW or issues found:]
1. [Specific recommendation]
2. [Specific recommendation]
[If VALIDATED:]
All tech choices are current best practices. Plan is ready for implementation.
## For Implementation
[Notes about any patterns or approaches to follow during implementation]Show full SKILL.md (160 more words)Show less
Returning to Orchestrator
After creating your handoff, return:
Validation Complete
Status: [VALIDATED | NEEDS REVIEW]
Handoff: [path to validation handoff]
Validated: [N] tech choices checked
Issues: [N] issues found (or "None")
[If VALIDATED:]
Plan is ready for implementation.
[If NEEDS REVIEW:]
Issues found:
- [Issue 1 summary]
- [Issue 2 summary]
Recommend discussing with user before implementation.Important Guidelines
DO:
- Validate ALL tech choices mentioned in the plan
- Use recent search queries (2024-2025)
- Note when you couldn't find definitive info
- Be specific about what needs to change
- Provide alternative suggestions when flagging issues
DON'T:
- Skip validation because something "seems fine"
- Flag things as issues without evidence
- Block on minor stylistic preferences
- Over-research standard library choices (stdlib is always valid)
Validation Thresholds:
VALIDATED - Return this when:
- All choices are valid OR
- Only minor suggestions (not blockers)
NEEDS REVIEW - Return this when:
- Any choice is DEPRECATED
- Any choice is RISKY (security)
- Any choice is significantly OUTDATED with much better alternatives
- Critical architectural concerns
Example Invocation
Task(
subagent_type="general-purpose",
model="haiku",
prompt="""
# Validate Agent
[This entire SKILL.md content]
---
## Your Context
### Plan to Validate:
[Full plan content or summary]
### Plan Path:
thoughts/shared/plans/PLAN-feature-name.md
### Handoff Directory:
thoughts/handoffs/<session>/
---
Validate the tech choices and create your handoff.
"""
)Standard Library Note
These don't need external validation (always valid):
- Python stdlib: argparse, asyncio, json, os, pathlib, etc.
- Standard patterns: REST APIs, JSON config, environment variables
- Well-established tools: pytest, git, make
Focus validation on:
- Third-party libraries
- Newer frameworks
- Specific version requirements
- External APIs/services
- Novel architectural patterns
© parcadei, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Files
Just SKILL.md in .claude/skills/validate-agent of parcadei/Continuous-Claude-v3.
Open the folder on GitHubat commit d07ff4b
Used in 2 other repositories
We found 6 copies of this SKILL.md (exact, near-identical or edited) in other folders, from 2 other GitHub owners. This page covers the copy in parcadei/Continuous-Claude-v3, which our catalogue first saw on October 7, 2026.
Compare with similar skills
Validate Agent next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Validate Agent this skillparcadei/Continuous-Claude-v3 | 3.9k | 2 repos | ~1.5k | Automated safety check: Pass | MIT | |
| MCP Server Builderanthropics/skills | 180k | 64 repos | ~2.3k | Automated safety check: Pass | Apache-2.0 | |
| Hook Development for Claude Code Pluginsanthropics/claude-plugins-official | 38k | 11 repos | ~4.1k | Automated safety check: Notes | Apache-2.0 | |
| Using Superpowersfarm-fe/farm | 5.6k | 35 repos | ~1.4k | Automated safety check: Pass | MIT | |
| Executing Plans Inlineobra/superpowers | 296k | 2 repos | ~5.1k | Automated safety check: Pass | MIT | |
| Claude Code Agent Developmentanthropics/claude-plugins-official | 38k | 8 repos | ~2.8k | Automated safety check: Pass | Apache-2.0 |
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Executing Plans Inline
obra/superpowers
Has the agent carry out an implementation plan itself, task by task in the current session, keeping a ledger, proving each step with a test and ending with one whole-branch review.
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Related
Categories
Questions about Validate Agent
What does Validate Agent do?
Validation agent that validates plan tech choices against current best practices. Validate Agent is an agent skill from parcadei/Continuous-Claude-v3.
When should I use Validate Agent?
Validate Agent fits situations like: agent Workflows work in your project.
How do I install Validate Agent in Claude Code?
Run `npx skills add parcadei/Continuous-Claude-v3 --skill validate-agent -a claude-code`. Or copy the skill folder (.claude/skills/validate-agent in parcadei/Continuous-Claude-v3) into .claude/skills/validate-agent in your project. Claude Code loads it when a task matches its description.
How do I install Validate Agent in Codex?
Run `npx skills add parcadei/Continuous-Claude-v3 --skill validate-agent -a codex`. Or copy the skill folder (.claude/skills/validate-agent in parcadei/Continuous-Claude-v3) into .agents/skills/validate-agent in your project. Codex loads it when a task matches its description.
Can I use Validate Agent in Cursor, Gemini CLI or GitHub Copilot?
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add parcadei/Continuous-Claude-v3 --skill validate-agent -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/validate-agent, .gemini/skills/validate-agent, .github/skills/validate-agent and .opencode/skills/validate-agent in your project.
What does Validate Agent need to run?
Going by SKILL.md and its folder, Validate Agent needs the command-line tools its instructions call (uv). Our summary lists: Python 3.
Does Validate Agent access the network?
SKILL.md contains no URLs. Its commands use uv, which can reach the network depending on how they are called. This is read from the text; nothing was executed.
Is Validate Agent safe to install?
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.
What licence does Validate Agent use?
Validate Agent is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does Validate Agent use?
About 1.5k tokens (SKILL.md is roughly 6k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.
What are the alternatives to Validate Agent?
Skills that share tags, products or a category with Validate Agent: MCP Server Builder (anthropics/skills, 180k stars), Hook Development for Claude Code Plugins (anthropics/claude-plugins-official, 38k stars), Using Superpowers (farm-fe/farm, 5.6k stars) and Executing Plans Inline (obra/superpowers, 296k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Validate Agent?
parcadei (a GitHub user) maintains it in parcadei/Continuous-Claude-v3, which has 3,940 GitHub stars. The repository holds 141 skills in this directory. The repository was last updated on January 26, 2026.
Source: parcadei/Continuous-Claude-v3 on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.