client-health-dashboard
Generates a comprehensive client health overview across all accounts. Reads CRM data, support tickets, usage metrics, billing, and engagement logs. Calculates health scores, trend direction, and RAG status per client. Outputs a sorted risk report with recommended actions.
What this skill does
# Client Health Dashboard Generate a data-driven client health report: pull data from every available source, compute a weighted health score per client, and produce a prioritized risk report (`client-health-report.md`) sorted by risk with RAG status and actionable recommendations. ## Contents - `references/data-sources.md` -- what to pull from CRM, support, usage, billing, and communication channels - `references/scoring-model.md` -- dimensions, weights, scoring rules, composite formula, RAG thresholds, trend logic - `references/risk-and-recommendations.md` -- risk factor triggers, per-dimension recommendation menus, expansion assessment - `references/output-format.md` -- exact report structure, formatting rules, and missing-data handling ## Workflow 1. Collect data from every available source. Handle failures gracefully: log what was unavailable and proceed with partial data. Never fabricate data. See `references/data-sources.md` for the full source list and the fields to extract per client. 2. Score each client. Rate the five dimensions 0-100, apply weights, and compute the composite score. Assign RAG status and trend direction. See `references/scoring-model.md`. 3. Analyze risk and generate recommendations. Flag critical and warning risk factors, produce 2-4 specific recommendations targeting each client's weakest dimensions, and assess expansion potential for healthy accounts. See `references/risk-and-recommendations.md`. 4. Generate the report. Write `client-health-report.md` following the exact structure and formatting rules. Handle missing data by scoring neutral (50) and noting gaps. See `references/output-format.md`. 5. Validate before finalizing: - Verify RAG assignments match score ranges. - Confirm section ordering and within-section sorting. - Confirm every client appears exactly once. - Confirm each client has 2-4 specific, actionable recommendations. - Attribute each data point to its source. - Mark data gaps explicitly; never invent data that was not retrieved. ## Interaction - If the user specifies particular clients, filter the report to those only. - If the user specifies a data source, prioritize it. - If the user provides CSV/Excel files, parse them as a primary source. - If the user requests a format variation, adapt accordingly. - Confirm the output path before writing. - If no data sources are accessible, explain what is needed and what to provide. ## Constraints - Never fabricate or hallucinate data; report only what was retrieved, attributed to its source. - Never include credentials, API keys, or PII beyond business contact info. - Keep health scores mathematically correct per the weighting formula. - Keep recommendations specific and actionable, not generic. - Keep the report self-contained, professional, and direct. - Do not use emojis anywhere in the report or any output.
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.