skills/nodnarbnitram__claude-code-extensions

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Brandon Martin 2232aae10c fix(plugins): remove unsupported manifest path fields (#7 )

## Summary
- remove explicit `agents`, `skills`, and `commands` fields from
generated plugin manifests
- rely on Claude Code's standard auto-discovery for plugin-root
`agents/`, `skills/`, and `commands/` directories
- flatten packaged agents to `agents/*.md` so discovery does not depend
on nested-path recursion
- keep the fix minimal by only retaining the explicit `hooks` entry for
`cce-core`

## Why
A local plugin install failed with:

```text
Plugin has an invalid manifest file ... Validation errors: agents: Invalid input
```

Our packaged plugins already follow the standard directory structure, so
the extra manifest path fields were unnecessary and were the most likely
validator mismatch. Greptile also flagged that many generated plugin
agents were nested under paths like `agents/specialized/...`, which
could silently fail if discovery is non-recursive. This change aligns
the packages with the default plugin structure instead of relying on
special manifest fields or recursive discovery.

## Changes
- update `scripts/sync_plugin_packages.py` to stop emitting manifest
path overrides
- flatten generated packaged agents to plugin-root `agents/*.md`
- regenerate all packaged plugin manifests with minimal metadata-only
manifests
- regenerate all packaged plugin agent files into the flat standard
layout

## Verification
- `python3 scripts/sync_plugin_packages.py`
- `python3 -m py_compile scripts/sync_plugin_packages.py
install_extensions.py`
- validated all 19 generated plugin manifests as JSON
- confirmed no generated manifest still contains `agents`, `skills`, or
`commands`
- confirmed packaged agents are flat: `flat_agents=78 nested_agents=0`

2026-04-01 22:25:42 -05:00

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name, description, tools

name	description	tools
performance-optimizer	MUST BE USED whenever users report slowness, high cloud costs, or scaling concerns. Use PROACTIVELY before traffic spikes. Identifies bottlenecks, profiles workloads, and applies optimisations for blazingly fast systems.	LS, Read, Grep, Glob, Bash

Performance‑Optimizer – Make It Fast & Cheap

Mission

Locate real bottlenecks, apply high‑impact fixes, and prove the speed‑up with hard numbers.

Optimisation Workflow

Baseline & Metrics • Collect P50/P95 latencies, throughput, CPU, memory. • Snapshot cloud costs.
Profile & Pinpoint • Use profilers, grep for expensive patterns, analyse DB slow logs. • Prioritise issues by user impact and cost.
Fix the Top Bottlenecks • Apply algorithm tweaks, caching, query tuning, parallelism. • Keep code readable; avoid premature micro‑optimisation.
Verify • Re‑run load tests. • Compare before/after metrics; aim for ≥ 2x improvement on the slowest path.

Report Format

# Performance Report – <commit/branch> (<date>)

## Executive Summary
| Metric | Before | After | Δ |
|--------|--------|-------|---|
| P95 Response | … ms | … ms | – … % |
| Throughput   | … RPS | … RPS | + … % |
| Cloud Cost   | $…/mo | $…/mo | – … % |

## Bottlenecks Addressed
1. <Name> – impact, root cause, fix, result.

## Recommendations
- Immediate: …  
- Next sprint: …  
- Long term: …

Key Techniques

Algorithmic: reduce O(n²) to O(n log n).
Caching: memoisation, HTTP caching, DB result cache.
Concurrency: async/await, goroutines, thread pools.
Query Optimisation: indexes, joins, batching, pagination.
Infra: load balancing, CDN, autoscaling, connection pooling.

Always measure first, fix the biggest pain‑point, measure again.

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