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ANGX 4e07a6a874 feat(stdlib)!: Agent Skills v1.0 conformance, resolver fix, and 17 new skills (#43 )

* 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withdrawn.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.

2026-09-05 04:35:30 +02:00

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name, version, description, category, tags, allowed-tools, metadata

name

version

description

Edge Computing Expert

Design and implement production-grade edge computing solutions including CDN optimization, fog computing architectures, and distributed edge workloads.

Learning Objectives

Master edge computing architectures and patterns
Implement CDN optimization and caching strategies
Build fog computing distributed systems
Deploy edge workloads with container orchestration
Optimize for ultra-low latency applications

Prerequisites

Strong understanding of distributed systems
Knowledge of networking and protocols
Experience with containers and orchestration
Familiarity with cloud computing concepts

Core Concepts

Edge Computing Architecture

Computing paradigm that brings data processing closer to data sources (IoT devices, users, sensors) rather than centralized cloud data centers. Reduces latency, bandwidth costs, and enables real-time processing.

Content Delivery Networks (CDN)

Geographically distributed network of proxy servers that cache and deliver content from locations closer to end users. Improves load times, reduces origin server load, and enhances availability.

Fog Computing

Decentralized computing infrastructure where data, compute, storage, and applications are distributed between the data source and the cloud. Extends cloud capabilities to the edge of the network.

Multi-Access Edge Computing (MEC)

Network architecture providing IT and cloud computing capabilities at the edge of mobile networks. Enables ultra-low latency applications by processing data near 5G base stations.

Edge Orchestration

Management and coordination of distributed edge workloads across heterogeneous edge infrastructure. Includes deployment, scaling, monitoring, and failover of edge services.

Best Practices

Edge Architecture Design

Place edge nodes based on user density and latency requirements
Implement multi-tier edge hierarchy (device → edge → regional → cloud)
Design for intermittent connectivity and offline operation
Use event-driven architectures for asynchronous processing
Implement circuit breakers and fallback mechanisms
Distribute workloads based on data locality
Plan for edge node heterogeneity

CDN Optimization

Implement intelligent cache warming for popular content
Use adaptive TTLs based on content access patterns
Enable compression and content optimization
Implement stale-while-revalidate patterns
Use cache keys that include relevant parameters
Monitor cache hit rates and optimize policies
Implement origin shielding to protect backend

Performance & Latency

Measure and optimize for P99 latency, not just average
Use connection pooling and keep-alive
Implement request coalescing for cache misses
Use HTTP/2 or HTTP/3 for multiplexing
Optimize payload sizes and use efficient formats
Implement predictive prefetching
Monitor network conditions and adapt

Security at the Edge

Implement DDoS protection at edge layers
Use TLS everywhere, including edge-to-edge
Validate and sanitize all inputs at edge
Implement rate limiting and throttling
Use secure boot and attestation for edge devices
Encrypt data at rest on edge nodes
Implement zero-trust network architecture

Anti-Patterns

Common Mistakes

Centralizing too much logic that should be at edge
Not handling network partitions gracefully
Ignoring data consistency challenges
Over-caching dynamic or personalized content
Not monitoring edge node health
Inadequate capacity planning for edge nodes
Treating edge nodes as simple caches

Design Issues

Single points of failure in edge architecture
Not accounting for edge node resource constraints
Ignoring data sovereignty and regulatory requirements
Poor cache invalidation strategies
Not implementing proper observability
Lack of automated failover mechanisms
Insufficient testing of edge scenarios

Reference Documentation

Detailed material lives alongside this skill and is read on demand:

Code Examples — Edge Computing Node Manager, CDN Cache Optimization System

Resources

Edge Platforms & Tools

AWS Wavelength - 5G edge computing
Azure Edge Zones - Edge services
Google Distributed Cloud Edge
Cloudflare Workers - Serverless edge
Fastly Compute@Edge
Section.io - Edge as a Service
KubeEdge - Kubernetes edge orchestration

CDN Providers

Cloudflare - Global CDN and edge
Akamai - Enterprise CDN
Fastly - Programmable CDN
AWS CloudFront
Azure CDN
Google Cloud CDN

Technologies

K3s - Lightweight Kubernetes for edge
EdgeX Foundry - IoT edge framework
Apache OpenWhisk - Serverless platform
NGINX - Edge proxy and load balancer
Envoy Proxy - Service mesh
Varnish - HTTP cache

Learning Resources

LF Edge - Linux Foundation edge projects
Edge Computing Consortium
ETSI MEC specifications
State of the Edge Report
Edge Native Application Principles
CDN performance optimization guides

Part of the PCL Standard Library - Deploy computing at the edge for ultra-low latency applications.

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