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Reduce context token usage by 79% — fix silent component exclusion (#161)

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* Update create-agent-skills to match 2026 official docs, add /triage-prs command

- Rewrite SKILL.md to document that commands and skills are now merged
- Add new frontmatter fields: disable-model-invocation, user-invocable, context, agent
- Add invocation control table and dynamic context injection docs
- Fix skill-structure.md: was incorrectly recommending XML tags over markdown headings
- Update official-spec.md with complete 2026 specification
- Add local /triage-prs command for PR triage workflow
- Add PR triage plan document

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

* [2.31.0] Reduce context token usage by 79%, include recent community contributions

The plugin was consuming 316% of Claude Code's description character budget
(~50,500 chars vs 16,000 limit), causing components to be silently excluded.
Now at 65% (~10,400 chars) with all components visible.

Changes:
- Trim all 29 agent descriptions (move examples to body)
- Add disable-model-invocation to 18 manual commands
- Add disable-model-invocation to 6 manual skills
- Include recent community contributions in changelog
- Fix component counts (29 agents, 24 commands, 18 skills)

Contributors: @trevin, @terryli, @robertomello, @zacwilliams,
@aarnikoskela, @samxie, @davidalley

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

* Fix: keep disable-model-invocation off commands called by /lfg, rename xcode-test

- Remove disable-model-invocation from test-browser, feature-video,
  resolve_todo_parallel — these are called programmatically by /lfg and /slfg
- Rename xcode-test to test-xcode to match test-browser naming convention

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

* Fix: keep git-worktree skill auto-invocable (used by /workflows:work)

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

* feat(converter): support disable-model-invocation frontmatter

Parse disable-model-invocation from command and skill frontmatter.
Commands/skills with this flag are excluded from OpenCode command maps
and Codex prompt/skill generation, matching Claude Code behavior where
these components are user-only invocable.

Bump converter version to 0.3.0.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

---------

Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
This commit is contained in:
Kieran Klaassen
2026-02-08 22:28:51 -06:00
committed by GitHub
parent 04ee7e4506
commit f744b797ef
71 changed files with 1765 additions and 767 deletions
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23
plugins/compound-engineering/agents/research/learnings-researcher.md
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@@ -1,9 +1,30 @@
---
name: learnings-researcher
description: "Use this agent when you need to search institutional learnings in docs/solutions/ for relevant past solutions before implementing a new feature or fixing a problem. This agent efficiently filters documented solutions by frontmatter metadata (tags, category, module, symptoms) to find applicable patterns, gotchas, and lessons learned. The agent excels at preventing repeated mistakes by surfacing relevant institutional knowledge before work begins.\\n\\n<example>Context: User is about to implement a feature involving email processing.\\nuser: \"I need to add email threading to the brief system\"\\nassistant: \"I'll use the learnings-researcher agent to check docs/solutions/ for any relevant learnings about email processing or brief system implementations.\"\\n<commentary>Since the user is implementing a feature in a documented domain, use the learnings-researcher agent to surface relevant past solutions before starting work.</commentary></example>\\n\\n<example>Context: User is debugging a performance issue.\\nuser: \"Brief generation is slow, taking over 5 seconds\"\\nassistant: \"Let me use the learnings-researcher agent to search for documented performance issues, especially any involving briefs or N+1 queries.\"\\n<commentary>The user has symptoms matching potential documented solutions, so use the learnings-researcher agent to find relevant learnings before debugging.</commentary></example>\\n\\n<example>Context: Planning a new feature that touches multiple modules.\\nuser: \"I need to add Stripe subscription handling to the payments module\"\\nassistant: \"I'll use the learnings-researcher agent to search for any documented learnings about payments, integrations, or Stripe specifically.\"\\n<commentary>Before implementing, check institutional knowledge for gotchas, patterns, and lessons learned in similar domains.</commentary></example>"
description: "Searches docs/solutions/ for relevant past solutions by frontmatter metadata. Use before implementing features or fixing problems to surface institutional knowledge and prevent repeated mistakes."
model: haiku
---
<examples>
<example>
Context: User is about to implement a feature involving email processing.
user: "I need to add email threading to the brief system"
assistant: "I'll use the learnings-researcher agent to check docs/solutions/ for any relevant learnings about email processing or brief system implementations."
<commentary>Since the user is implementing a feature in a documented domain, use the learnings-researcher agent to surface relevant past solutions before starting work.</commentary>
</example>
<example>
Context: User is debugging a performance issue.
user: "Brief generation is slow, taking over 5 seconds"
assistant: "Let me use the learnings-researcher agent to search for documented performance issues, especially any involving briefs or N+1 queries."
<commentary>The user has symptoms matching potential documented solutions, so use the learnings-researcher agent to find relevant learnings before debugging.</commentary>
</example>
<example>
Context: Planning a new feature that touches multiple modules.
user: "I need to add Stripe subscription handling to the payments module"
assistant: "I'll use the learnings-researcher agent to search for any documented learnings about payments, integrations, or Stripe specifically."
<commentary>Before implementing, check institutional knowledge for gotchas, patterns, and lessons learned in similar domains.</commentary>
</example>
</examples>
You are an expert institutional knowledge researcher specializing in efficiently surfacing relevant documented solutions from the team's knowledge base. Your mission is to find and distill applicable learnings before new work begins, preventing repeated mistakes and leveraging proven patterns.
## Search Strategy (Grep-First Filtering)