improve
Capture feedback about wf, util, or vault plugins and file it as a GitHub issue for improvement. Use after any session where the tooling fell short — wrong agent behavior, missing skill coverage, misleading prompts, workflow friction, or convention gaps. Only for generic plugin improvements that would help ANY project, not repo-specific config. Files to JSai23/claude-tooling with the plugin-feedback label.
What this skill does
## Feedback Context $ARGUMENTS --- Capture plugin feedback from this session and file it as a GitHub issue. ## Step 1: Reflect Look back at the session. Identify: - What went wrong or caused friction - What the user had to correct or override - What was missing that should exist - What was misleading in agent/skill prompts If $ARGUMENTS is provided, start from that. Otherwise, review the session for friction points. ## Step 2: Filter — Generic vs Repo-Specific This is the critical gate. Ask: **"Would this feedback help a different project using these plugins?"** **Generic** (file it): - Planner doesn't check for existing tests before proposing new ones - /doc skill doesn't handle monorepos - Verifier misses import-only changes **Repo-specific** (don't file — belongs in CLAUDE.md): - Custom auth headers, tool preferences (pnpm vs npm), project-specific test flags - Exception: if the same repo-specific issue keeps appearing across projects, it's generic ## Step 3: Distill For each piece of generic feedback, structure it: - **Plugin/skill/agent affected** — which specific component (e.g., `wf:planner`, `util:doc`, `vault:auto-process`) - **Current behavior** — what happened - **Expected behavior** — what should have happened - **Session context** — what project, what task, what conditions triggered it Group related feedback for the same plugin/agent cluster into one issue. Separate unrelated clusters into separate issues. ## Step 4: File For each issue, use: ```bash gh issue create \ --repo JSai23/claude-tooling \ --label plugin-feedback \ --title "<plugin>: <concise summary>" \ --body "<structured feedback from step 3>" ``` Issue body format: ```markdown ## Affected Component <plugin>/<skill or agent> (e.g., wf/planner, util/doc) ## Current Behavior <what happened> ## Expected Behavior <what should have happened> ## Context - **Project:** <what kind of project> - **Task:** <what the user was doing> - **Conditions:** <anything relevant about when/how this triggered> ## Notes <any additional context, workarounds used, severity> ``` ## Step 5: Report Show the user: - Issue URL(s) created - Summary of what was filed - Any feedback that was filtered out as repo-specific (and where it should go instead)
Related in AI Agents
skill-development
IncludedComprehensive meta-skill for creating, managing, validating, auditing, and distributing Claude Code skills and slash commands (unified in v2.1.3+). Provides skill templates, creation workflows, validation patterns, audit checklists, naming conventions, YAML frontmatter guidance, progressive disclosure examples, and best practices lookup. Use when creating new skills, validating existing skills, auditing skill quality, understanding skill architecture, needing skill templates, learning about YAML frontmatter requirements, progressive disclosure patterns, tool restrictions (allowed-tools), skill composition, skill naming conventions, troubleshooting skill activation issues, creating custom slash commands, configuring command frontmatter, using command arguments ($ARGUMENTS, $1, $2), bash execution in commands, file references in commands, command namespacing, plugin commands, MCP slash commands, Skill tool configuration, or deciding between skills vs slash commands. Delegates to docs-management skill for official documentation.
reprompter
IncludedTransform messy prompts into well-structured, effective prompts — single or multi-agent. Use when: "reprompt", "reprompt this", "clean up this prompt", "structure my prompt", rough text needing XML tags and best practices, "reprompter teams", "repromptception", "run with quality", "smart run", "smart agents", multi-agent tasks, audits, parallel work, anything going to agent teams. Don't use when: simple Q&A, pure chat, immediate execution-only tasks. See "Don't Use When" section for details. Outputs: Structured XML/Markdown prompt, quality score (before/after), optional team brief + per-agent sub-prompts, agent team output files. Success criteria: Single mode quality score ≥ 7/10; Repromptception per-agent prompt quality score 8+/10; all required sections present, actionable and specific.
adaptive-compaction
IncludedAdaptive add-on policy and recovery layer that decides WHEN to compact, prune, snapshot, or fork -- replacing fixed-percent auto-compaction across Claude Code, Codex, and MCP-capable hosts. Trigger on auto-compact timing or damage: "when should I compact", "is it safe to compact now or start a fresh session", "auto-compact fires too early/mid-task", "switching to an unrelated task but the window still has space", "context rot", "answers get worse the longer the session runs", "the agent forgot the plan or my decisions after it summarized", "add a layer on top that manages context without changing the agent", raising autoCompactWindow to give the policy room, or installing/tuning a cross-tool compaction policy or PreCompact hook -- even when "compaction" is never said but the problem is context-window pressure or post-summarization memory loss. Do NOT use to summarize a conversation, build RAG, write a summarization prompt (decides WHEN not HOW), or answer max-context-length trivia.
agent-skill-creator
IncludedCreate cross-platform agent skills from workflow descriptions. Activates when users ask to create an agent, automate a repetitive workflow, create a custom skill, or need advanced agent creation. Triggers on phrases like create agent for, automate workflow, create skill for, every day I have to, daily I need to, turn process into agent, need to automate, create a cross-platform skill, validate this skill, export this skill, migrate this skill. Supports single skills, multi-agent suites, transcript processing, template-based creation, interactive configuration, cross-platform export, and spec validation.
llm-wiki
IncludedUse when building or maintaining a persistent personal knowledge base (second brain) in Obsidian where an LLM incrementally ingests sources, updates entity/concept pages, maintains cross-references, and keeps a synthesis current. Triggers include "second brain", "Obsidian wiki", "personal knowledge management", "ingest this paper/article/book", "build a research wiki", "compound knowledge", "Memex", or whenever the user wants knowledge to accumulate across sessions instead of being re-derived by RAG on every query.
skill-master
IncludedAgent Skills authoring, evaluation, and optimization. Create, edit, validate, benchmark, and improve skills following the agentskills.io specification. Use when designing SKILL.md files, structuring skill folders (references, scripts, assets), ingesting external documentation into skills, running trigger evals, benchmarking skill quality, optimizing descriptions, or performing blind A/B comparisons. Keywords: agentskills.io, SKILL.md, skill authoring, eval, benchmark, trigger optimization.