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 skills add Bilal140202/the-lord-of-the-skills --skill haaaiawd__anwsgit clone --depth 1 https://github.com/Bilal140202/the-lord-of-the-skillsWrote 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/bilal140202/the-lord-of-the-skills/haaaiawd__anws)<a href="https://agentmods.dev/skills/bilal140202/the-lord-of-the-skills/haaaiawd__anws"><img src="https://agentmods.dev/badge/skills/bilal140202/the-lord-of-the-skills/haaaiawd__anws/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/bilal140202/the-lord-of-the-skills/haaaiawd__anws"><img src="https://agentmods.dev/badge/skills/bilal140202/the-lord-of-the-skills/haaaiawd__anws.svg" alt="Reviewed on agentmods" width="80" 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 5.1 | $0.00082 | $0.02957 |
| Opus 5.5 | $0.00033 | $0.01183 |
| Sonnet 5.5 | $0.00016 | $0.00591 |
| Haiku 4.5 | $0.00008 | $0.00296 |
Grade A, and why
concept-modeler 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 1mo 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 — 231 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Domain Modeler
"If you cannot describe it clearly, you cannot build it." — Eric Evans
This skill turns user "feel words" into a clear domain model through interactive follow-up questions and persists a structured contract consumable by spec-writer and later steps.
<phase_context> You are the DOMAIN MODELER.
Mission: In /genesis Step 1, converge vague user wording into Ubiquitous Language and a machine-readable/writable concept_model.json; supply unambiguous nouns, verbs, and known gaps for PRD writing.
Capabilities: Vagueness scan (entities / verbs / dark matter / boundaries), controlled questioning (multiple choice or very short answers), incremental model maintenance on every answer, glossary and clarifications traceability.
Constraints: Output only one question to the user at a time (queue is internal only; do not dump the full list at the user); do not skip follow-up and fill JSON from memory; if the host provides a structured questioning tool (e.g. ask question), prefer the tool to ask.
Sub-agents (optional): Bounded slices only (e.g. "only generate vagueness candidates", "only reconcile glossary synonym conflicts"); after merge the parent agent is the sole writer of .anws/v{N}/concept_model.json; sub-agents must not race the same file.
Output Goal: .anws/v{N}/concept_model.json with field semantics matching the spec contract below; user-side closure on key terminology.
</phase_context>
CRITICAL methodology anchors
[!IMPORTANT] Clarify once, skip a rework round; written to disk is the contract.
- Awaken, do not proclaim: Scan and name "where it's fuzzy" first, then offer options; do not declare domain understood before vagueness is identified.
- One focus at a time: The user can only answer one question well per turn; however long the internal queue, only the current question is shown outward.
- Elevate, then ground: Lift colloquial speech into JSON fields (entity types, flows, missing-component categories and priority); "seems clear" is not deliverable.
- Incremental closure, not a final monologue: Update the on-disk model after every answer; do not wait until "all questions are done" to write.
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.
- 1mo ago First seen · 231 lines · 82 tokens per session scan A badab6b5ba3e
concept-modeler is a skill published in the GitHub repository Bilal140202/the-lord-of-the-skills (6 stars, last pushed 1mo ago), licensed MIT. It adds 82 tokens to every session and 2,957 once invoked, about $0.0003 per session on Opus 5.5. 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 2026-08-31.
Other skills, from other repositories
serpsmith
Publish SEO articles, verify unattended health, and gate source-to-runtime releases.
tool-calling-tutor
Use when a tool-calling agent does not call a tool, sends wrong arguments, loops without stopping, or needs a function schema. Guides a four-branch diagnosis and five-step schema repair. Do not use for framework-specific, MCP-server, or production-observability questions.
ainb-fleet:daemons
Runtime-health view of the four fleet daemons — phone bridge, notifyd, ATC, and the fleet auto-continue daemon — in one table. Each row reports state (running / stopped / unknown), pid, uptime, last activity, error count, and a HEALTH reason that distinguishes a clean stop from a crash (stale heartbeat) or a…
ainb-fleet:atc
ATC (Air Traffic Control) — the single operator-visible fleet supervisor, in exactly one mode: lite (no LLM: deterministic scan, known-transient retry policy, safety caps, evidence) or full (the persistent orchestrating brain). Full mode is a plain ainb session (Claude or Codex) running a generated policy, woken on a…
ainb-fleet:fleet-needs
Workflow-backed Jarvis control panel. Runs the deterministic hangar workflow with verb=needs (discover → enrich → prioritize), renders the Jarvis HUD from its render-ready cards, fires AskUserQuestion per blocked session, and routes each answer back via tmux send-keys (broker fallback only). Requires the workflow gate…
first-plan-lens-mobile
Stack lens para mobile - React Native, Swift (iOS), Kotlin (Android), Flutter. Use durante Discovery quando pubspec.yaml, Package.swift, build.gradle.kts (Android) ou react-native em deps for detectado. Cobre navegação, estado, módulos nativos, build/distribuição.