LLM Mart Basic
@llm-mart · Joined Jun 2026
ACCESS-GATED beta. Deepline Monitors are provider event feeds (job posts, email replies, funding, intent) that stream into your warehouse and trigger plays. Only use if you have monitor access: run `deepline monitors status` first; if it reports no access, do NOT use this recipe
Use for Deepline GTM work that searches, enriches, scores, collects signals, or automates a workflow: find companies or people, enrich a CSV, find emails or LinkedIn, compare providers, build a waterfall, create a webhook or cron, or write a Play. For live information work, run a
Use this skill when a human needs to review a Deepline Play result and hand feedback, labels, or approval back to the agent for revision, evaluation, comparison, or bounded iteration. Triggers on “put this run in a Sheet,” “review these results,” “read my feedback,” “make this a
Use when the user wants a last30days-style pre-research pass in Deepline: discover the critical public, private, CRM, workflow, social, and web data sources for a research/enrichment job; compare provider coverage; estimate Deepline credit cost; recommend the source plan before b
Run a quick Deepline demo recipe to show the user how Deepline works.
Use when finding real role-holders at known company domains from an ICP, especially prompts like 'find all job titles at these companies', 'find qualified titles', 'find RevOps or marketing-ops buyers', or when exact title discovery should precede paid people search.
Resolve LinkedIn profile URLs from name + company with strict identity validation to avoid false positives.
Discover niche first-party signals that differentiate Closed Won vs Closed Lost accounts for ICP analysis. Use when the user provides won/lost customer domain lists and wants differential signals (website content, job listings, tech stack, maturity markers) to build account scori
Find companies backed by a specific investor or accelerator, then find contacts and build personalized outbound.
Create a concrete Deepline execution plan before running GTM work. Use when the task needs routing, sequencing, provider selection, approval gating, or a plan that maps cleanly onto the skill docs.
Build company or contact seed lists for GTM workflows. Use for discovery, TAM building, portfolio prospecting, known-company contact finding, and provider-driven list construction before enrichment.
Full-sweep quality audit of .claude/ config — cross-references, permissions, inventory drift, model tiers, docs freshness. Scope tokens select what to audit; --upgrade applies docs-sourced improvements; --adversarial runs foundry:challenger + Codex adversarial review; --efficienc
Iterative brainstorming skill for turning fuzzy ideas into approved tree documents. Diverges into branches, deepens and prunes them over many rounds, saves a tree doc. Run breakdown on the tree to distill it into a spec via guided questions.
Calibration testing for agents and skills. Generates synthetic problems with known outcomes (quasi-ground-truth), runs targets against them, measures recall, precision, confidence calibration — reveals whether self-reported confidence scores track actual quality.
Interactive outline co-creation for developer advocacy content — collects format, audience profile, story arc (Problem→Journey→Insight→Action), and voice/tone; detects out-of-scope requests (FAQs, comparison tables); surfaces conflicts between user brief and audience needs. Write
Strip AI-writing tells from prose destined for humans — docs, PR/commit bodies, reports, release notes, blog posts, Slack/email drafts. Removes LLM-vocabulary clichés (delve, boasts, testament, underscore, robust, tapestry...), banned constructions (not just X but Y, rule-of-thre
Systematic diagnosis for unknown failures — local environment, tool setup, CI vs local divergence, hook misbehavior, and runtime anomalies. Gathers signals broadly, ranks hypotheses, uses adversarial review (Codex or foundry:challenger) for ambiguous cases, probes each, and repor
Create, update, or delete agents, skills, and rules; update or delete hooks (hook creation not yet implemented — edit `hooks/<name>.js` directly). Full cross-reference propagation. Trivial edits (typos, small fixes ≤10 words) applied inline without agent; `.md` content-edits dele
Session clock-time AND token/cost analyzer. Reads the foundry plugin's timings.jsonl and invocations.jsonl logs (written by task-log.js) for wall-time, plus Claude Code transcripts (~/.claude/projects/**, main-loop + subagent files) for token usage and USD cost, and merges both i
Session state that outlives a context reset — `dump` sweeps the live conversation and writes a compact handover doc (goal, decisions + why, lessons, standing instructions, files-touched table, outstanding items, next step), then prints `/clear`; the `session-restore.js` SessionSt
Fourteen posts of being wrong in production, compressed to checkboxes
Healthy nodes, a quiet network, 300 restarts in three days, and a latency budget measured in milliseconds
Discovery worked. Ping worked. Every TCP connection timed out, and later the tunnel only worked when someone had a terminal open.
Every VM came back. The cluster did not. Declarative systems converge on config, and the datapath isn't config.
A surprising share of AI-in-the-terminal failures aren't the AI. They're zsh, and a version of bash from 2006.
A Claude Code plugin turns standalone project configuration into a namespaced, installable extension that teams and communities can update as one unit.
None of the safety came from the model. It came from six boring habits.
Skills package instructions and references. Subagents run work in a separate context and return results. They solve different problems and can be composed deliberately.
Six hours in, one step left, everything green, and the incident that didn't happen
CLAUDE.md carries persistent project context. Skills load reusable procedures when relevant. Separating stable facts from task-specific workflows keeps both easier to maintain.
Twenty minutes recovering secrets that never existed, and the one sentence from a human that ended it
An API request routing a model's tool call through an approval gate to a remote MCP server
31 config keys, two audits, and why the first one was wrong in both directions
The official MCP Registry stores standardized server metadata rather than package code. Publishers verify a namespace, describe installation or remote access, and submit immutable versions.
Everyone looks at the Dockerfile. The file that actually leaked the key was the project file.
Remote MCP authorization uses established OAuth standards, but secure integration still requires issuer validation, least-privilege scopes, protected token handling, and server-side enforcement.
"Copy it over and switch the reference" is two steps, and the outage lives in the one nobody checks
stdio fits local processes and prototypes. Streamable HTTP fits hosted services and shared integrations. The right choice follows where the capability runs and who must reach it.
The most important rule wasn't about what I could change. It was about what I was allowed to display.
Tools perform operations, resources expose readable context, and prompts provide reusable templates. Choosing the correct primitive makes an MCP server easier to understand and govern.
/search-agents
Search agents
Search for RMM agents in Atera by customer or machine name
/search-customers
Search customers
Search for Atera customers by name or criteria
/update-ticket
Update ticket
Update fields on an existing Atera ticket
/alert-triage
Alert triage
Triage open Auvik alerts, rank by severity, and recommend dismissals for known noise
/capacity-check
Capacity check
Scan Auvik interface statistics for saturated links and recurring congestion
/device-inventory
Device inventory
Inventory devices for an Auvik tenant with type, manage status, and lifecycle breakdown
/network-audit
Network audit
Audit a tenant's networks, interfaces, and saved configurations; flag drift and missing backups
/tenant-overview
Tenant overview
Single-tenant Auvik snapshot - devices, alerts, networks, billing usage
/phishing-results
Phishing results
Phishing-simulation results and click-rate trend for a given window
/risk-report
Risk report
Human risk score report for one client or the whole portfolio, built from training completion and phishing-simulation performance
/training-status
Training status
Training completion snapshot for one client or the whole portfolio — completion rates, overdue users, and cadence status
/backup-health-check
Backup health check
Check backup health for one Axcient-protected device
/client-backup-overview
Client backup overview
Backup health overview across every device for one Axcient client
/azure-cost
Azure cost
Azure cost and pricing analysis for a subscription — Advisor cost recommendations, retail pricing lookups, and quota-driven right-sizing signals, scoped to one subscription
/azure-diagnostics
Azure diagnostics
Resource health and diagnostics triage for an Azure resource or subscription — Resource Health status, AppLens deep diagnostics, and Azure Monitor alert state
/backup-status
Backup status
Portfolio-wide backup job health snapshot - failure count, at-risk clients, and storage trends
/restore-check
Restore check
Restore-readiness check - has this actually been restore-tested, for one client or the whole portfolio
/retention-audit
Retention audit
Retention and RPO compliance audit against contracted requirements, for one client or the whole portfolio
/create-monitor
Create monitor
Create a new Better Stack uptime monitor
/incident-triage
Incident triage
Triage current Better Stack incidents
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