skill-integration-tester
- Repo stars 1,568
- Author updated Live
- Author repo claude-trading-skills
- Domain
- Data
- Compatible agents
-
- Claude Code
- Cursor
- Cline
- Codex
- Windsurf
- Gemini CLI
- +20
- Trust score
- 88 / 100 · community maintained
- Author / version / license
- @tradermonty · no license declared
- Token usage
- Lean
- Setup complexity
- Guided setup
- External API key
- Not required
- Operating systems
- Unspecified (assume cross-platform)
- Runtime requirements
- Python >=3.9
- Permissions
-
- Read-only
- Write / modify
- Shell exec
- Network behavior
- Local-only
- Install commands
- 26 variants
Profile is derived at build time from SKILL.md and install vectors. Subject to drift from author intent.
Heads up: 未限定 allowed-tools,默认拥有全部工具权限。
---
name: skill-integration-tester
description: Validate multi-skill workflows defined in CLAUDE.md by checking skill existence, inter-skill dat…
category: data
runtime: Python
---
# skill-integration-tester output preview
## PART A: Task fit
- Use case: Validate multi-skill workflows defined in CLAUDE.md by checking skill existence, inter-skill data contracts (JSON schema compatibility), file naming conventions, and handoff integrity. Use when adding new workflows, modifying skill outputs, or verifying pipeline health before release..
- Inputs: target material, constraints, expected output, and acceptance criteria.
- Evidence boundary: follow “Overview / When to Use / Prerequisites” and do not present inference as author intent.
## PART B: Execution result
- **01** The card summarizes the use case; runtime output centers on “Validate multi-skill workflows defined in CLAUDE.md by checking skill existence, inter-skill data contracts (JSON schema compatibility), file naming conventions, and handoff integrity. Use when adding new workflows, modifying skill outputs, or verifying pipeline health before release.”.
- **02** When the source has headings, the agent prioritizes “Overview / When to Use / Prerequisites” so the result follows the author’s structure.
- **03** Typical output includes task judgment, concrete steps, required commands or file edits, validation, and follow-up options.
- **04** Risk context follows the fingerprint: read files, write/modify files, run shell commands; mostly runs locally; usually needs no extra API key.
## Running Rules
- read files, write/modify files, run shell commands; mostly runs locally; usually needs no extra API key.
- Validate with a small sample before expanding scope.
- Return the result, validation criteria, and next iteration options. The source does not require a stable slash command. After installation, invoke the skill by name and describe the task.
Name target files or source material, expected output, forbidden changes, and whether network or shell access is allowed. Permission fingerprint: read files, write/modify files, run shell commands.
Start with a small task and check whether the result follows “Overview / When to Use / Prerequisites”. Inspect diffs, logs, previews, or tests before expanding scope.
Confirm the final output includes a concrete result, evidence, and next action. If it stays generic, tighten inputs, boundaries, and acceptance criteria.
---
name: skill-integration-tester
description: Validate multi-skill workflows defined in CLAUDE.md by checking skill existence, inter-skill dat…
category: data
source: tradermonty/claude-trading-skills
---
# skill-integration-tester
## When to use
- Validate multi-skill workflows defined in CLAUDE.md by checking skill existence, inter-skill data contracts (JSON sche…
- Use it when the task has clear inputs, repeatable steps, and validation criteria.
## What to provide
- Target material, scope, expected result, and forbidden changes.
- Whether network, commands, file writes, or external services are allowed.
## Execution rules
- Organize steps around “Overview / When to Use / Prerequisites” and keep inference separate from source facts.
- read files, write/modify files, run shell commands; mostly runs locally; usually needs no extra API key.
- Validate with a small sample before expanding the task.
## Output requirements
- Return the deliverable, key evidence, validation method, and next action.
- Mark missing information as unknown; do not invent commands, platforms, or dependencies. The author source anchors workflow facts; repository files anchor sources and commands; Fluxly only adds fit, limitations, and quality judgment.
skill "skill-integration-tester" {
input -> user goal + target files + boundaries + acceptance criteria
context -> Overview / When to Use / Prerequisites
rules -> SKILL.md triggers / order / output contract
runtime -> Python | read files, write/modify files, run shell commands | mostly runs locally
guardrails -> usually needs no extra API key + small-sample validation + diff/log review
output -> copyable result + checklist + next iteration
} Skill Integration Tester
Overview
Validate multi-skill workflows defined in CLAUDE.md (Daily Market Monitoring, Weekly Strategy Review, Earnings Momentum Trading, etc.) by executing each step in sequence. Check inter-skill data contracts for JSON schema compatibility between output of step N and input of step N+1, verify file naming conventions, and report broken handoffs. Supports dry-run mode with synthetic fixtures.
When to Use
- After adding or modifying a multi-skill workflow in CLAUDE.md
- After changing a skill's output format (JSON schema, file naming)
- Before releasing new skills to verify pipeline compatibility
- When debugging broken handoffs between consecutive workflow steps
- As a CI pre-check for pull requests touching skill scripts
Prerequisites
- Python 3.9+
- No API keys required
- No third-party Python packages required (uses only standard library)
Workflow
Step 1: Run Integration Validation
Execute the validation script against the project's CLAUDE.md:
python3 skills/skill-integration-tester/scripts/validate_workflows.py \
--output-dir reports/
This parses all **Workflow Name:** blocks from the Multi-Skill Workflows
section, resolves each step's display name to a skill directory, and validates
existence, contracts, and naming.
Step 2: Validate a Specific Workflow
Target a single workflow by name substring:
python3 skills/skill-integration-tester/scripts/validate_workflows.py \
--workflow "Earnings Momentum" \
--output-dir reports/
Step 3: Dry-Run with Synthetic Fixtures
Create synthetic fixture JSON files for each skill's expected output and validate contract compatibility without real data:
python3 skills/skill-integration-tester/scripts/validate_workflows.py \
--dry-run \
--output-dir reports/
Fixture files are written to reports/fixtures/ with _fixture flag set.
Step 4: Review Results
Open the generated Markdown report for a human-readable summary, or parse the JSON report for programmatic consumption. Each workflow shows:
- Step-by-step skill existence checks
- Handoff contract validation (PASS / FAIL / N/A)
- File naming convention violations
- Overall workflow status (valid / broken / warning)
Step 5: Fix Broken Handoffs
For each FAIL handoff, verify that:
- The producer skill's output contains all required fields
- The consumer skill's input parameter accepts the producer's output format
- File naming patterns are consistent between producer output and consumer input
Output Format
JSON Report
{
"schema_version": "1.0",
"generated_at": "2026-03-01T12:00:00+00:00",
"dry_run": false,
"summary": {
"total_workflows": 8,
"valid": 6,
"broken": 1,
"warnings": 1
},
"workflows": [
{
"workflow": "Daily Market Monitoring",
"step_count": 4,
"status": "valid",
"steps": [...],
"handoffs": [...],
"naming_violations": []
}
]
}
Markdown Report
Structured report with per-workflow sections showing step validation, handoff status, and naming violations.
Reports are saved to reports/ with filenames
integration_test_YYYY-MM-DD_HHMMSS.{json,md}.
Resources
scripts/validate_workflows.py-- Main validation scriptreferences/workflow_contracts.md-- Contract definitions and handoff patterns
Key Principles
- No API keys required -- all validation is local and offline
- Non-destructive -- reads SKILL.md and CLAUDE.md only, never modifies skills
- Deterministic -- same inputs always produce same validation results
Decide Fit First
Design Intent
How To Use It
Boundaries And Review