Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/intercom/claude-plugin-external/intercom-analysisnpx skills add intercom/claude-plugin-external --skill intercom-analysisgit clone --depth 1 https://github.com/intercom/claude-plugin-externalWhat it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00072 | $0.01242 |
| Opus 5 | $0.00036 | $0.00621 |
| Sonnet 5 | $0.00014 | $0.00248 |
| Haiku 4.5 | $0.00007 | $0.00124 |
Grade A, and why
intercom-analysis scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 102 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Intercom Analysis
Use the Intercom MCP server to analyze customer conversations, look up contacts and companies, identify support patterns, and investigate customer issues.
Refer to references/mcp-tools.md for detailed tool reference, query DSL syntax, search strategies, and field-level documentation for each MCP tool.
Pattern Analysis Workflow
When the user asks to analyze patterns or trends in their support data, follow this workflow:
-
Define scope. Clarify what the user wants to analyze — a time period, topic, customer segment, or conversation state. Ask if unclear.
-
Fetch a representative sample. Search for conversations matching the scope. Retrieve at least 10–20 conversations to establish meaningful patterns. Paginate if the first page is insufficient.
-
Read conversation details. For each relevant conversation, fetch the full conversation to read the actual messages. Summaries from search results alone are often insufficient for pattern analysis.
-
Identify recurring themes. Group conversations by:
- Common topics or keywords
- Product areas or features mentioned
- Error messages or symptoms reported
- Resolution approaches used
- Time to resolution
-
Quantify and summarize. Present findings with counts and proportions (e.g., "8 of 15 conversations mention timeout errors"). Highlight the most common patterns first.
-
Recommend actions. Based on patterns, suggest concrete next steps — knowledge base articles to create, bugs to investigate, or process improvements.
Output artifact: Produce a markdown report with the following structure:
- Theme Summary — Table of identified themes with conversation counts and percentage of total
- Top Issues — The 3–5 most common issues with representative conversation excerpts
- Recommended Actions — Prioritized list of concrete next steps based on the patterns found
Issue Investigation Steps
When a user asks you to investigate a specific customer issue or incident:
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 2d ago First seen · 102 lines · 72 tokens per session scan A efdfac780ef0
intercom-analysis is a skill published in the GitHub repository intercom/claude-plugin-external (2 stars, last pushed 4mo ago), licensed MIT. It adds 72 tokens to every session and 1,242 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
next-cache-components-adoption
Turn on Cache Components in a Next.js app and resolve the blocking routes it surfaces. Use when the user wants to enable, adopt, or migrate to Cache Components, flip the cacheComponents flag, work through a flood of blocking-prerender / instant validation errors, run the cache-components-instant-false codemod, or…
babysit-pr
Babysit a GitHub pull request after creation by continuously polling review comments, CI checks/workflow runs, and mergeability state until the PR is merged/closed or user help is required. Diagnose failures, retry likely flaky failures up to 3 times, auto-fix/push branch-related issues when appropriate, and keep…
imagegen
Generate or edit raster images when the task benefits from AI-created bitmap visuals such as photos, illustrations, textures, sprites, mockups, or transparent-background cutouts. Use when Codex should create a brand-new image, transform an existing image, or derive visual variants from references, and the output…
cpu-profile-analysis
Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…
next-cache-components-optimizer
Drive a Next.js route to instant navigation by setting up an agentic loop, under Cache Components / PPR, on initial load (hard navigation) and client-side navigation (soft navigation). Encode the goal as a failing @next/playwright instant() e2e and work it to green, one verified route at a time; the shipped test then…