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4e07a6a874
* chore(stdlib): add conformance tooling for the skills library
The library had no gate that could express what a valid skill is, so
malformed frontmatter, over-budget files and credential literals went
unnoticed. These scripts define and enforce that contract.
- validate-skills.py: extend with Agent Skills v1.0 conformance -
naming rules and directory agreement, an explicit activation trigger
in the description, canonical layout, the 500-line SKILL.md budget,
relative-link integrity, and two security rules (no unrestricted
Bash(*) grant, no credential literals). Structural checks now read a
skill as a whole so material moved into references/ still counts.
- split-skill-references.py: move the heaviest sections of an oversized
SKILL.md into references/. Verifies line by line that every source
line lands in exactly one output and refuses to write otherwise.
- generate-skill-inventory.py: derive SKILLS_INVENTORY.md and
DIRECTORY_STRUCTURE.md from the tree so they cannot drift.
- generate-skill-catalog.py: index the canonical layout only, count
references/ toward a skill's size, and stop truncating descriptions
at 200 characters - the catalog is the discovery surface and must
carry the activation trigger.
- .gitignore: Python bytecode from these scripts, and local .pcl state.
Reviewed-by: Security Analyst, Compiler Engineer, DevX Engineer
* fix(stdlib): bring all 174 skills to Agent Skills v1.0 conformance
No skill in the library was conformant. 41 of 175 failed the existing
validator outright, and none satisfied the spec. This pass fixes the
tree and records the result. See stdlib/CHANGELOG.md for the per-skill
versioning and migration notes.
BREAKING CHANGE: one skill removed, five moved, seven with a capability
withdrawn. Library version 2.0.0 -> 3.0.0.
Structure
- Convert 71 skills stored as flat files to <category>/<name>/SKILL.md,
the layout the spec requires and the only one able to carry companion
resources.
- Rename 5 directories that disagreed with their declared name:, so
resolution by path and by name stop giving different answers. The
repository docs already referenced the corrected paths.
- Merge the duplicate legal-technology skills into legaltech-expert and
delete legal-tech-expert, carrying over everything the survivor
lacked.
Metadata
- Repair 20 unparseable frontmatters: an H1 preceded the --- block and
the key was skill_id: instead of name:, so any parser reading
frontmatter at offset 0 saw no metadata at all. The objectives,
prerequisites and outcome they carried become real body sections.
- Add the missing version: to 21 skills, fold keywords into tags, move
expertise_level under metadata, and align 10 category: values with
their directory.
Discoverability
- Give all 174 descriptions an explicit "Use when ..." trigger, built
from each skill's own tags and section headings. 152 previously said
only what the skill was about, leaving an agent nothing to match on.
Progressive disclosure
- Split 127 oversized skills, moving bulk material into references/ read
on demand and leaving an index behind. A SKILL.md now averages 277
lines instead of 644 and none exceeds 500. Verified lossless line by
line against the previous revision.
Security
- Withdraw the unrestricted Bash(*) grant from 7 skills; none of the six
non-financial ones contained a single shell command. finance-expert
documents code that charges cards and reads bank accounts, so it now
grants no shell at all.
- Remove credential literals: Stripe keys, two JWT signing keys, an
Express session secret, a Redis password and WiFi credentials, all
inlined in examples. They load from the environment now.
- finance-expert: idempotency keys were documented but absent, so a
retry double-charged; webhook signature failures were not caught, so a
forged event raised uncaught instead of being rejected;
int(amount * 100) truncated fractions of a cent. All fixed, plus a
money-movement guardrails section.
- trading-expert: place_order routed without any pre-trade risk check
although a RiskManager sat unused in the next section. Risk validation
now runs before routing and raises rather than returning a boolean a
caller can ignore; live routing is opt-in.
- Correct an Ansible Vault example that showed plaintext under an
"encrypted" label.
Also fixes 8 broken documentation links and a prematurely closed code
fence in skill-creator-expert that leaked template content into prose.
Per the repository file-organization policy, four stale progress reports
(SKILLS_MANIFEST, COMPLETE_SKILLS_LIBRARY, COMPLETE_LIBRARY_MANIFEST,
PHASE_3_DATA_ANALYTICS) move out of stdlib/ into the gitignored
.roadmap/status/stdlib/; they claimed 31 of 100 skills complete. They
appear here as deletions because .roadmap/ is not tracked.
Reviewed-by: Security Analyst, Language Designer, DevX Engineer,
Documentation Specialist
* fix(skills): resolve standard library skills from their real layout
No stdlib skill was resolvable. SkillResolver looked for
`<stdlibDir>/<name>.md` with stdlibDir defaulting to `stdlib/skills`, a
directory that does not exist, and the path ignored the category level
the library actually uses. The two LSP providers had the same wrong
directory and scanned only one level deep, so neither completion nor
go-to-definition could see a skill either.
- skill-discovery.ts: enumerate skill entry points across the shapes in
use - a flat `<name>.md`, `<name>/SKILL.md`, and
`<category>/<name>/SKILL.md` - while skipping references/, scripts/
and assets/, which hold supporting material rather than entry points.
- skill-resolver.ts: point stdlibDir at `stdlib`, and look the name up
as a directory under any category as well as the two legacy shapes, so
existing custom stdlib directories keep working. The name is used as a
single path segment and rejected if it contains a separator or
traversal, so a reference cannot escape the stdlib root.
- Rewire both LSP providers onto the shared discovery helper.
Tests: 13 new cases covering the layouts, ordering, the references/
exclusion, path-traversal rejection, and an integration test that
resolves real shipped skills by bare name.
Reviewed-by: Compiler Engineer, Runtime Architect, Security Analyst
* feat(stdlib): add 17 skills covering the library's largest gaps
Selection was driven by cross-referencing coverage against observed usage
rather than by intuition: Stack Overflow and GitHub Octoverse data for
which technologies matter, and install rankings on public agent-skill
registries for the shape of skills people actually use.
That comparison found a mismatch of form, not only of coverage. The
most-installed skills in the ecosystem are procedures - tdd, refactoring,
frontend design, browser automation - while all 174 existing skills were
domain knowledge shaped as <domain>-expert. The new workflows/ category
closes that gap and introduces the <activity>-workflow convention.
New category workflows/ (4)
- tdd-workflow, refactoring-workflow, debugging-workflow,
code-review-workflow. code-review-workflow complements rather than
replaces code-review-expert: procedure versus judgement. Merge them if
the split proves confusing in use.
Applied AI (3) - the largest coverage gap
- llm-engineering-expert, rag-expert, agent-engineering-expert.
The library referenced LLMs in tags but covered none of it: pgvector
appeared zero times across 174 skills, LangChain once, MCP twice
despite the repository shipping an MCP integration guide.
Capabilities (2)
- document-processing-expert (PDF, DOCX, XLSX, PPTX in one skill),
browser-automation-expert (a client against third-party sites,
distinct from playwright-expert which tests your own application).
Data (3)
- mysql-expert, the second most used database and previously absent
while rarer stores had skills; analytical-databases-expert;
stream-processing-expert.
Security (3)
- secrets-management-expert, supply-chain-security-expert (OWASP A03
2025), identity-access-expert (OWASP A01).
Interface and meta (2)
- accessibility-expert: design/ held only design-expert, which despite
its name covers software architecture, so WCAG had no home.
- skill-router: find and compose skills across 191, with a
disambiguation table for overlapping pairs.
Grouping by decision context rather than by technology keeps the growth
to 17 skills for roughly 50 topics. Five vector stores live inside
rag-expert; four document formats inside document-processing-expert.
Per-tool depth sits in references/, which the progressive-disclosure work
in 3.0.0 made cheap.
Four further gaps are better served by extending existing skills -
PyTorch and TensorFlow in ml-expert, OpenTelemetry in monitoring-expert,
Pulumi and Crossplane in terraform-expert - and are not in this change.
Library 3.0.0 -> 3.1.0, additive only. Adds the workflows label to the
inventory generator; catalogue, inventory, structure and CHANGELOG
regenerated.
Reviewed-by: Product Strategist, Security Analyst, DevX Engineer,
Documentation Specialist
* fix(test): create test directories with mkdtempSync
CodeQL flagged js/insecure-temporary-file (CWE-377) on both suites.
`join(tmpdir(), <predictable name>)` followed by mkdirSync creates a
world-readable directory at a guessable path, so another local user can
pre-create it or plant a symlink between the name being computed and the
directory being created.
mkdtempSync creates the directory atomically with a random suffix and
0700 permissions, which closes both the disclosure and the race.
One occurrence is new in this branch (skill-discovery); the other
pre-dates it in skill-resolver and is fixed in the same pass rather than
left as a known finding in a file this branch already touches.
10 KiB
10 KiB
name, version, description, category, tags, allowed-tools
| name | version | description | category | tags | allowed-tools | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| grpc-expert | 1.1.0 | Expert-level gRPC, Protocol Buffers, microservices communication, and streaming. Use when the user mentions Protocol Buffers, microservices, RPC, or streaming, or when the task involves gRPC Fundamentals, Communication Patterns, or Production Features. | api |
|
|
gRPC Expert
Expert guidance for gRPC services, Protocol Buffers, microservices communication, and streaming patterns.
Core Concepts
gRPC Fundamentals
- Protocol Buffers (protobuf)
- Service definitions
- RPC patterns (unary, server streaming, client streaming, bidirectional)
- HTTP/2 transport
- Code generation
- Interceptors and middleware
Communication Patterns
- Unary RPC (request-response)
- Server streaming RPC
- Client streaming RPC
- Bidirectional streaming RPC
- Deadline/timeout handling
- Error handling and status codes
Production Features
- Load balancing
- Service discovery
- Health checking
- Authentication (TLS, tokens)
- Monitoring and tracing
- Retry policies
Protocol Buffer Definition
syntax = "proto3";
package user.v1;
import "google/protobuf/timestamp.proto";
service UserService {
// Unary RPC
rpc GetUser(GetUserRequest) returns (GetUserResponse);
// Server streaming
rpc ListUsers(ListUsersRequest) returns (stream User);
// Client streaming
rpc CreateUsers(stream CreateUserRequest) returns (CreateUsersResponse);
// Bidirectional streaming
rpc Chat(stream ChatMessage) returns (stream ChatMessage);
}
message User {
string id = 1;
string email = 2;
string name = 3;
google.protobuf.Timestamp created_at = 4;
UserRole role = 5;
}
enum UserRole {
USER_ROLE_UNSPECIFIED = 0;
USER_ROLE_USER = 1;
USER_ROLE_ADMIN = 2;
}
message GetUserRequest {
string id = 1;
}
message GetUserResponse {
User user = 1;
}
message ListUsersRequest {
int32 page_size = 1;
string page_token = 2;
}
message CreateUserRequest {
string email = 1;
string name = 2;
}
message CreateUsersResponse {
repeated string user_ids = 1;
int32 created_count = 2;
}
message ChatMessage {
string user_id = 1;
string message = 2;
google.protobuf.Timestamp timestamp = 3;
}
Python gRPC Server
import grpc
from concurrent import futures
import logging
from typing import Iterator
import user_pb2
import user_pb2_grpc
class UserService(user_pb2_grpc.UserServiceServicer):
def __init__(self):
self.users = {}
def GetUser(self, request, context):
"""Unary RPC"""
user_id = request.id
if user_id not in self.users:
context.abort(grpc.StatusCode.NOT_FOUND, f"User {user_id} not found")
user = self.users[user_id]
return user_pb2.GetUserResponse(user=user)
def ListUsers(self, request, context):
"""Server streaming RPC"""
page_size = request.page_size or 10
for i, user in enumerate(self.users.values()):
if i >= page_size:
break
yield user
def CreateUsers(self, request_iterator, context):
"""Client streaming RPC"""
created_ids = []
for request in request_iterator:
user_id = self._generate_id()
user = user_pb2.User(
id=user_id,
email=request.email,
name=request.name
)
self.users[user_id] = user
created_ids.append(user_id)
return user_pb2.CreateUsersResponse(
user_ids=created_ids,
created_count=len(created_ids)
)
def Chat(self, request_iterator, context):
"""Bidirectional streaming RPC"""
for message in request_iterator:
# Echo back with modification
response = user_pb2.ChatMessage(
user_id="server",
message=f"Echo: {message.message}",
timestamp=message.timestamp
)
yield response
def serve():
server = grpc.server(futures.ThreadPoolExecutor(max_workers=10))
user_pb2_grpc.add_UserServiceServicer_to_server(UserService(), server)
server.add_insecure_port('[::]:50051')
server.start()
print("Server started on port 50051")
server.wait_for_termination()
if __name__ == '__main__':
logging.basicConfig()
serve()
Go gRPC Server
package main
import (
"context"
"io"
"log"
"net"
"time"
"google.golang.org/grpc"
"google.golang.org/grpc/codes"
"google.golang.org/grpc/status"
pb "example.com/user/v1"
)
type userServer struct {
pb.UnimplementedUserServiceServer
users map[string]*pb.User
}
func (s *userServer) GetUser(ctx context.Context, req *pb.GetUserRequest) (*pb.GetUserResponse, error) {
user, exists := s.users[req.Id]
if !exists {
return nil, status.Errorf(codes.NotFound, "user %s not found", req.Id)
}
return &pb.GetUserResponse{User: user}, nil
}
func (s *userServer) ListUsers(req *pb.ListUsersRequest, stream pb.UserService_ListUsersServer) error {
pageSize := req.PageSize
if pageSize == 0 {
pageSize = 10
}
count := 0
for _, user := range s.users {
if count >= int(pageSize) {
break
}
if err := stream.Send(user); err != nil {
return err
}
count++
}
return nil
}
func (s *userServer) CreateUsers(stream pb.UserService_CreateUsersServer) error {
var userIds []string
for {
req, err := stream.Recv()
if err == io.EOF {
return stream.SendAndClose(&pb.CreateUsersResponse{
UserIds: userIds,
CreatedCount: int32(len(userIds)),
})
}
if err != nil {
return err
}
userId := generateID()
user := &pb.User{
Id: userId,
Email: req.Email,
Name: req.Name,
}
s.users[userId] = user
userIds = append(userIds, userId)
}
}
func (s *userServer) Chat(stream pb.UserService_ChatServer) error {
for {
msg, err := stream.Recv()
if err == io.EOF {
return nil
}
if err != nil {
return err
}
response := &pb.ChatMessage{
UserId: "server",
Message: "Echo: " + msg.Message,
Timestamp: msg.Timestamp,
}
if err := stream.Send(response); err != nil {
return err
}
}
}
func main() {
lis, err := net.Listen("tcp", ":50051")
if err != nil {
log.Fatalf("failed to listen: %v", err)
}
s := grpc.NewServer()
pb.RegisterUserServiceServer(s, &userServer{
users: make(map[string]*pb.User),
})
log.Println("Server listening on :50051")
if err := s.Serve(lis); err != nil {
log.Fatalf("failed to serve: %v", err)
}
}
gRPC Client with Interceptors
import grpc
import user_pb2
import user_pb2_grpc
from typing import Callable
def logging_interceptor(
method: Callable,
request,
call_details: grpc.ClientCallDetails
):
"""Unary-unary interceptor for logging"""
print(f"Calling method: {call_details.method}")
print(f"Request: {request}")
response = method(request, call_details)
print(f"Response: {response}")
return response
class UserClient:
def __init__(self, host: str = 'localhost:50051'):
# Create channel with interceptor
self.channel = grpc.insecure_channel(host)
self.channel = grpc.intercept_channel(
self.channel,
logging_interceptor
)
self.stub = user_pb2_grpc.UserServiceStub(self.channel)
def get_user(self, user_id: str) -> user_pb2.User:
"""Call unary RPC"""
request = user_pb2.GetUserRequest(id=user_id)
try:
response = self.stub.GetUser(
request,
timeout=5.0 # 5 second timeout
)
return response.user
except grpc.RpcError as e:
print(f"RPC failed: {e.code()} - {e.details()}")
raise
def list_users(self, page_size: int = 10):
"""Call server streaming RPC"""
request = user_pb2.ListUsersRequest(page_size=page_size)
try:
for user in self.stub.ListUsers(request):
yield user
except grpc.RpcError as e:
print(f"RPC failed: {e.code()} - {e.details()}")
def create_users_batch(self, users: list):
"""Call client streaming RPC"""
def request_generator():
for user in users:
yield user_pb2.CreateUserRequest(
email=user['email'],
name=user['name']
)
try:
response = self.stub.CreateUsers(request_generator())
return response
except grpc.RpcError as e:
print(f"RPC failed: {e.code()} - {e.details()}")
def close(self):
self.channel.close()
Best Practices
Design
- Use semantic versioning for protobuf packages
- Design backward-compatible changes
- Use proper field numbering (never reuse)
- Include metadata in messages
- Use enums with explicit UNSPECIFIED value
- Design for pagination in list operations
Performance
- Enable HTTP/2 connection pooling
- Use streaming for large data transfers
- Implement proper timeouts
- Use compression for large payloads
- Batch operations when possible
- Monitor and tune thread pool sizes
Production
- Implement health checks
- Use TLS for secure communication
- Add authentication/authorization
- Implement retry policies with backoff
- Monitor gRPC metrics (latency, errors)
- Use service discovery for dynamic endpoints
Anti-Patterns
❌ Breaking backward compatibility ❌ No timeout configuration ❌ Ignoring error status codes ❌ Not handling stream cancellation ❌ Over-sized messages ❌ No health checking ❌ Missing authentication
Resources
- gRPC: https://grpc.io/
- Protocol Buffers: https://protobuf.dev/
- gRPC-Go: https://github.com/grpc/grpc-go
- gRPC-Python: https://grpc.io/docs/languages/python/