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/techwolf-ai/ai-first-toolkit/customer-statusnpx skills add techwolf-ai/ai-first-toolkit --skill customer-statusgit clone --depth 1 https://github.com/techwolf-ai/ai-first-toolkitWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/techwolf-ai/ai-first-toolkit/customer-status)<a href="https://agentmods.dev/skills/techwolf-ai/ai-first-toolkit/customer-status"><img src="https://agentmods.dev/badge/skills/techwolf-ai/ai-first-toolkit/customer-status.svg" alt="Measured on agentmods" height="20"></a>What 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 | $0.00067 | $0.01045 |
| Opus 5 | $0.00034 | $0.00522 |
| Sonnet 5 | $0.00013 | $0.00209 |
| Haiku 4.5 | $0.00007 | $0.00104 |
Grade A, and why
customer-status 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 5d 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 — 112 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Customer Status Overview
Principle: "Narrow scope, high impact." One synthesised view across all accounts, so you can spot what needs attention without channel-hopping.
Produces a dashboard-style overview of active customer accounts and internal projects for the manager's team.
When to Use
- Weekly check-in on account health
- Before leadership meetings where customer status is discussed
- When the manager says "how are our accounts?", "customer status", "any customer risks?"
- Can be filtered: "customer status for [account]", "customer status for [team member]'s accounts"
Instructions
If any MCP connector is unavailable, follow the connector unavailability protocol in references/operating-principles.md.
1. Load Context
Read from manager-context/:
sources.md: customer/project channels, account mappingsmanager-profile.md: team members and their account assignmentsteam/: individual team member profiles and project assignments
If no manager-context exists:
⚠️ No manager context found. Run /setup first so I know which accounts and channels to monitor.
I can still search broadly, but results will be less targeted.
2. Identify Active Accounts
From manager-context, get the list of active accounts/projects and their:
- Slack channels
- Key contacts (team member and customer-side)
- Notion project pages
- Current delivery phase (if documented)
If filtering by account or team member, narrow the scope.
3. Scan Per Account
For each active account, gather:
Slack (last 7 days):
- Recent messages in the project/customer channel
- Volume of activity (high/normal/low compared to usual)
- Any messages with escalation signals: "blocked", "risk", "delayed", "urgent", "escalate", "concerned"
- Any positive signals: "shipped", "live", "approved", "happy", "great feedback"
- Most recent message timestamp (to detect silent accounts)
Gmail (last 14 days):
- Email threads related to this customer
- Any emails with escalation or risk language
- Communication frequency
What ships with it
2 files 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.
- 5d ago First seen · 112 lines · 67 tokens per session scan A e38db8e2289b
customer-status is a skill published in the GitHub repository techwolf-ai/ai-first-toolkit (98 stars, last pushed 1mo ago), licensed MIT. It adds 67 tokens to every session and 1,045 once invoked, about $0.0003 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
todos
This chat has a shared, live TODO plan — your tasks for the conversation, which the user also edits. Read this skill and reach for the todo tools whenever a request takes more than a couple of steps. It covers the plan model (group = task, items = its steps; loose items are the user's lane), how to work it: propose…
writing-workflow-skills
Use when adding a new workflow skill to pi-thinkrail-workflow, changing an existing workflow skill's role, trigger, handoff, or structure, or checking a workflow skill against the workflow system's rules. Not for authoring general-purpose skills outside this package.
writing-specs
Use when a workflow step drafts or revises a spec artifact — a goal-and-requirements, an architecture, or a module SPEC — or when a workflow skill names it at such a step. The shared quality bar for specs — not a workflow, nothing to execute.
clarify
Adaptive requirements clarification with auto-depth routing. Shallow (Q&A) for simple tasks, Deep (exploration + DRAFT + PLAN) for complex ones. Escalates automatically when ambiguity persists.
detecting-process-injection-techniques
Detects and analyzes process injection techniques used by malware including classic DLL injection, process hollowing, APC injection, thread hijacking, and reflective loading. Uses memory forensics, API monitoring, and behavioral analysis to identify injection artifacts. Activates for requests involving process…
extracting-iocs-from-malware-samples
Extracts indicators of compromise (IOCs) from malware samples including file hashes, network indicators (IPs, domains, URLs), host artifacts (file paths, registry keys, mutexes), and behavioral patterns for threat intelligence sharing and detection rule creation. Activates for requests involving IOC extraction, threat…