mistral-ci-integration
Automates prompt regression testing and quality checks within GitHub Actions for Mistral AI.
Install
mkdir -p .claude/skills/mistral-ci-integration && curl -L -o skill.zip "https://agentskills.codes/api/skills/download/8156" && unzip -o skill.zip -d .claude/skills/mistral-ci-integration && rm skill.zipInstalls to .claude/skills/mistral-ci-integration
Activation
This is the description your AI agent reads to decide when to run this skill — the better it matches your request, the more reliably it fires.
Configure Mistral AI CI/CD integration with GitHub Actions and promptKey capabilities
- →Configure GitHub Actions for automated prompt regression testing
- →Implement deterministic assertions for AI model responses
- →Generate cost estimates for prompt usage in pull requests
- →Validate model references against allowed lists
- →Detect and warn about deprecated Mistral models
How it works
The skill integrates Mistral API calls into a CI pipeline using GitHub Actions, running regression tests with deterministic temperature settings and generating cost reports for PRs.
Inputs & outputs
When to use mistral-ci-integration
- →Running automated prompt regression tests
- →Implementing quality gates for prompt changes
- →Estimating AI costs in pull requests
- →Integrating Mistral into CI/CD pipelines
About this skill
Mistral CI Integration
Overview
Integrate Mistral AI validation into CI/CD pipelines: prompt regression tests, model response quality checks, cost estimation in PR comments, and deployment gates for prompt changes. Uses GitHub Actions with MISTRAL_API_KEY stored as a repository secret.
Prerequisites
MISTRAL_API_KEYstored as GitHub repository secret- GitHub Actions configured
- Test framework (Vitest recommended)
Instructions
Step 1: GitHub Actions Workflow
# .github/workflows/mistral-tests.yml
name: Mistral AI Tests
on:
pull_request:
paths:
- 'src/prompts/**'
- 'src/ai/**'
- 'tests/ai/**'
jobs:
prompt-tests:
runs-on: ubuntu-latest
timeout-minutes: 10
steps:
- uses: actions/checkout@v4
- uses: actions/setup-node@v4
with:
node-version: '20'
cache: 'npm'
- run: npm ci
- name: Run prompt regression tests
env:
MISTRAL_API_KEY: ${{ secrets.MISTRAL_API_KEY }}
run: npx vitest run tests/ai/ --reporter=verbose
- name: Cost estimation
env:
MISTRAL_API_KEY: ${{ secrets.MISTRAL_API_KEY }}
run: npx tsx scripts/estimate-costs.ts >> $GITHUB_STEP_SUMMARY
Step 2: Prompt Regression Tests
// tests/ai/mistral-prompts.test.ts
import { describe, it, expect } from 'vitest';
import { Mistral } from '@mistralai/mistralai';
const apiKey = process.env.MISTRAL_API_KEY;
describe.skipIf(!apiKey)('Mistral Prompt Regression', () => {
const client = new Mistral({ apiKey: apiKey! });
it('summarization produces 2-3 sentences', async () => {
const result = await client.chat.complete({
model: 'mistral-small-latest',
messages: [
{ role: 'system', content: 'Summarize in 2-3 sentences.' },
{ role: 'user', content: 'TypeScript is a typed superset of JavaScript that compiles to plain JavaScript. It adds optional static typing and class-based OOP.' },
],
maxTokens: 150,
temperature: 0, // Deterministic for regression
});
const content = result.choices?.[0]?.message?.content ?? '';
expect(content.length).toBeGreaterThan(20);
expect(content.split(/[.!?]+/).filter(Boolean).length).toBeGreaterThanOrEqual(2);
}, 15_000);
it('classification returns valid category', async () => {
const result = await client.chat.complete({
model: 'mistral-small-latest',
messages: [
{ role: 'system', content: 'Classify as: bug, feature, question. Reply with one word only.' },
{ role: 'user', content: 'The login page crashes on mobile devices' },
],
maxTokens: 10,
temperature: 0,
});
const category = result.choices?.[0]?.message?.content?.trim().toLowerCase();
expect(['bug', 'feature', 'question']).toContain(category);
}, 15_000);
it('JSON mode returns valid JSON', async () => {
const result = await client.chat.complete({
model: 'mistral-small-latest',
messages: [{ role: 'user', content: 'Return {"status": "ok"} as JSON' }],
responseFormat: { type: 'json_object' },
maxTokens: 50,
temperature: 0,
});
const content = result.choices?.[0]?.message?.content ?? '';
expect(() => JSON.parse(content)).not.toThrow();
}, 15_000);
it('respects maxTokens limit', async () => {
const result = await client.chat.complete({
model: 'mistral-small-latest',
messages: [{ role: 'user', content: 'Write a long essay about AI' }],
maxTokens: 50,
});
expect(result.usage?.completionTokens).toBeLessThanOrEqual(50);
}, 15_000);
});
Step 3: Cost Estimation Script
// scripts/estimate-costs.ts
import { readdirSync, readFileSync } from 'fs';
import { join } from 'path';
const PRICING: Record<string, { input: number; output: number }> = {
'mistral-small-latest': { input: 0.1, output: 0.3 },
'mistral-large-latest': { input: 0.5, output: 1.5 },
'codestral-latest': { input: 0.3, output: 0.9 },
'mistral-embed': { input: 0.1, output: 0 },
};
console.log('## Prompt Cost Estimates\n');
console.log('| File | Est. Tokens | Cost/call (small) | Cost/call (large) |');
console.log('|------|-------------|-------------------|-------------------|');
const promptDir = join(process.cwd(), 'src/prompts');
if (readdirSync(promptDir, { withFileTypes: true })) {
for (const file of readdirSync(promptDir)) {
const content = readFileSync(join(promptDir, file), 'utf-8');
const tokens = Math.ceil(content.length / 4);
const smallCost = (tokens / 1e6) * PRICING['mistral-small-latest'].input;
const largeCost = (tokens / 1e6) * PRICING['mistral-large-latest'].input;
console.log(`| ${file} | ~${tokens} | $${smallCost.toFixed(6)} | $${largeCost.toFixed(6)} |`);
}
}
Step 4: Model Validation Gate
# .github/workflows/model-gate.yml
name: AI Model Gate
on:
pull_request:
paths: ['src/ai/**', 'src/prompts/**']
jobs:
validate:
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v4
- uses: actions/setup-node@v4
with: { node-version: '20', cache: 'npm' }
- run: npm ci
- name: Validate model references
run: |
echo "## Model References" >> $GITHUB_STEP_SUMMARY
grep -rn "mistral-\|codestral-\|pixtral-" src/ --include="*.ts" | \
grep -oP "(mistral|codestral|pixtral)-[\w-]+" | sort -u | while read model; do
echo "- \`$model\`" >> $GITHUB_STEP_SUMMARY
done
- name: Check for deprecated models
run: |
DEPRECATED="open-mistral-7b open-mixtral-8x7b mistral-tiny mistral-medium"
for model in $DEPRECATED; do
if grep -rq "$model" src/ --include="*.ts"; then
echo "::warning::Deprecated model found: $model"
fi
done
Error Handling
| Issue | Cause | Solution |
|---|---|---|
| Tests fail in CI | Missing API key secret | Add MISTRAL_API_KEY to repo Settings > Secrets |
| Flaky prompt tests | Non-deterministic output | Set temperature: 0 for regression tests |
| High CI costs | Running on every push | Only trigger on prompt/AI file changes via paths: |
| Test timeout | Slow API response | Set generous timeout (15s) per test |
Examples
Minimal Smoke Test
- name: Mistral smoke test
env:
MISTRAL_API_KEY: ${{ secrets.MISTRAL_API_KEY }}
run: |
curl -sf -X POST https://api.mistral.ai/v1/chat/completions \
-H "Authorization: Bearer $MISTRAL_API_KEY" \
-H "Content-Type: application/json" \
-d '{"model":"mistral-small-latest","messages":[{"role":"user","content":"ping"}],"max_tokens":5}' \
| jq '.choices[0].message.content'
Resources
Output
- GitHub Actions workflow for prompt testing
- Regression test suite with deterministic assertions
- Cost estimation in PR summaries
- Model validation gate catching deprecated references
When not to use it
- →When the project lacks a test framework
- →When GitHub Actions cannot be configured
Prerequisites
Limitations
- →Flaky tests if temperature is not set to zero
- →High CI costs if triggered on every push
- →Slow API response times causing test timeouts
How it compares
This approach automates quality gates and cost tracking for AI prompts, whereas manual testing often overlooks regression risks in prompt changes.
Compared to similar skills
mistral-ci-integration side by side with the closest alternatives in the catalog.
| Skill | Installs | Updated | Safety | Difficulty |
|---|---|---|---|---|
| mistral-ci-integration (this skill) | 0 | 27d | Caution | Intermediate |
| e2e-testing-patterns | 8 | 2mo | No flags | Intermediate |
| testing-workflow | 16 | 9mo | Review | Intermediate |
| perf-lighthouse | 13 | 5mo | Review | Intermediate |
Try saying
Example prompts that trigger this skill in your AI assistant.
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