LLM Mart Basic
@llm-mart · Joined Jun 2026
Official Feature-Sliced Design (FSD) v2.1 skill for applying the methodology to frontend projects. Use when the task involves organizing project structure with FSD layers, deciding where code belongs, placing static assets (images, icons, fonts, PDFs), grouping closely related sl
Compare two versions of an LLM-directed document - an original (teacher) and a candidate (student) - across a transfer set and return a per-case behavioural-equivalence verdict plus an efficiency signal. A transform-agnostic library capability other skills compose to gate a trans
Assess a codebase's readiness for AI agent contributors using the layered contract model, and generate a complexity hotspot SVG treemap (size = LOC, hue = cyclomatic complexity, saturation = recent git churn). TRIGGER when the user types /assess, asks for an AI-readiness review,
Renders the /assess report from the deterministic run-context.json and the layer scorecard - the scorecard, the verbatim cross-layer findings, lying signals, and the mandatory Top 3 Actions. TRIGGER when the /assess orchestrator reaches the report-writing step; not a standalone u
The /assess end-of-run offers - open a PR with the report, track the Top 3 Actions in the user's issue tracker, freeze the assessment into a CI gate, and file tool feedback. TRIGGER when the /assess orchestrator reaches the end-of-run offers; not a standalone user command.
Detect and remove the telltale signs of AI-generated 'slop' from any written text - articles, reports, emails, essays, bios, marketing copy, documentation, encyclopedia entries, or anything meant to read as if a thoughtful human wrote it. Apply silently as a quality gate before f
Read-only org repo state report. Reuses ghsync's repo discovery (teams union org-repo-list) but, instead of cloning, queries each repo's remote GitHub state - open PRs, CI on the default branch, open security alerts (Dependabot / code-scanning / secret-scanning), and branch prote
Bulk clone and keep in sync every GitHub repo you can access across an org or personal account. For an org it unions the repos reachable through the teams you belong to with the org's repo list (so direct-collaborator and public repos count even with no team membership); for a pe
Structured multi-perspective analysis using Six Thinking Hats with professional lens team members. TRIGGER when the user types /huddle, asks to run a huddle, wants a panel/board/team to analyze a decision, asks for multi-perspective analysis, debate, or red-team/blue-team review,
Run a list of work units to completion with an Agent Team: derive a dependency DAG and hot-file map, spawn one ephemeral teammate per unit (or combined group), drive each PR through pr-review-merge, smart-merge in waves, recover from crashes, and run a retrospective. Source-agnos
Drive a single pull request to merge-ready across all five criteria (sync, CI, inline comments, conversation, threads), then smart-merge it. Source-agnostic library skill invoked by the /tm, /issues, /fix-pr, and /fix-develop commands and by marathon teammates. TRIGGER when a com
Make an LLM-directed document smaller while preserving what it does. Two modes: a local span-level core->pointer pass, and an A/B-validated distill loop that produces the smallest document that behaves the same as the original. Point at core knowledge the model already holds (a c
Phase-1 (S4) of a comic movie — PRODUCE the canonical reusable references the whole spiral conditions on. Per asset it bakes ONE canonical 1:1 white-bg identity ref via the agent mcp__codex__codex sidecar bake (Codex native image_gen — conditioned, never hand-pasted) OR, for a de
Phase-1 (S5) UPSTREAM ref-asset gate — the bounded cross-model adversarial loop that LOCKS one reusable identity-locked asset (character sheet / location plate / prop cutout / text-panel / logo-free symbol) BEFORE it can be composited into any panel. This is NOT the panel_gate (c
Phase 1 ORCHESTRATOR of a movie/comic — turn a fuzzy story idea into the Authored Source of Truth (a schema-valid comic.json + its locked asset library) by driving the detailed author skills in order (intent → style → outline → storyboard → assets → blueprints → prompts → comic.j
Phase-1 comic-author step (post-final eval) — a DOUBLE-BLIND A/B of two FINAL whole comics: our cross-model-audited progressive render (the comic-author + comic-director output) vs a naive single-shot baseline. A single sealed coin-flip hides which is which; two cross-model revie
Phase-1 (S7 of the comic-author suite) — turn ONE locked panel_spec into a deterministic content-SVG blueprint that becomes the bake condition (reference #1 of the agent mcp__codex__codex sidecar bake), by WRITING A PYTHON GENERATOR (never raw SVG in chat — LLMs botch coordinates
The authoring-side DIEGETIC continuity AUDIT — it produces the world-state evidence that comic-cross-layer-gate `--gate continuity` adjudicates against the storyboard's MOTIF STATE TABLE (motif_ledger). It checks each baked panel STRICTLY against the pre-committed table row, then
The ONE parameterized score-fuser for EVERY comic-author authoring gate — `--gate intent|outline|asset|storyboard|blueprint|continuity|p0_proof|compile`. A single fuser (not a per-layer split) prevents drift. It NEVER re-runs a reviewer; it collects the reviewer score-nodes alrea
Phase 2/3 of a movie — bake + cross-model-verify a movie from an authored comic.json. Per frame: render the content-SVG blueprint, bake via the agent mcp__codex__codex sidecar, gate with a 3-reviewer cross-model panel (narrative [currently the codex CLI] ‖ Gemini visual ‖ Codex v
Use MCP Inspector to connect to local or remote servers, inspect capabilities, call tools, read resources, test prompts, and diagnose failures before release.
Build an MCP server in TypeScript with focused tools, validated schemas, local and remote transports, Inspector tests, and production security controls.
An MCP server exposes tools, resources, or prompts through a standard protocol so an AI application can discover and use external capabilities.
Treat an AI agent skill as both an instruction package and a software dependency: inspect what it says, what it runs, what it can access, and how it updates.
Add remote HTTP or local stdio MCP servers to Claude Code, choose the right scope, protect credentials, verify the connection, and test with least privilege.
Skills teach Claude a repeatable method, connectors provide governed access to apps and live data, and plugins package related capabilities for installation and sharing.
Use an agent skill to package reusable know-how and workflow instructions. Use an MCP server when an agent needs live, governed access to external data or actions.
Custom commands and skills can both create a slash-invoked workflow in Claude Code. The important choice is how the workflow is discovered, shared, and permissioned.
A useful Claude skill solves one recurring engineering job, is easy to inspect, and saves more time than it creates in setup and review.
Claude skills can live in your Claude account, your local Claude Code setup, or a repository. Install them where the sessions that need them can load them.
Build a portable AI agent skill from one repeatable job: a precise description, concise instructions, focused resources, and tests that prove it works.
AI agent skills package instructions, scripts, references, and templates into portable folders an agent loads only when the task calls for them.
AI made publishing cheap, which is exactly the problem. What separates a page worth ranking from a competent summary of the first ten results.
A prompt that works once isn't a quality system. Five cases, an observable rubric, and a regression set will tell you whether a change helped.
One character of YAML, four pods that never started, and two safety nets I didn't know were holding. Every restart is an audit. Schedule them before they schedule you.
"Verify your work" isn't an instruction. It's a mood. Here's the version that's an instruction. Verify with a different mechanism than the one that made the claim.
A prompt that works once may still fail in production. A lightweight eval set gives you repeatable cases, a clear rubric, and a way to see whether a prompt change actually improved the workflow.
The best AI tool is not the one with the longest feature list. It is the one that solves a defined job reliably, fits the workflow, handles data appropriately, and remains useful after the novelty wears off.
Use AI to speed research without losing trust. Learn to find primary sources, verify claims, preserve uncertainty, and keep an auditable source trail.
Better prompts aren't magic wording. They're short briefs that hand the model a task, the context it can't infer, the limits, and a quality bar.
/lineage-discovery
Lineage discovery
Discover testnet↔mainnet subnet lineage from repo configs and open a PR for review (pass --dry-run to report only)
/capture
capture
Triage raw inbox notes into reviewed repository destinations without deleting their sources.
/clean-ai-writing
clean-ai-writing
Audit and rewrite content to remove AI writing patterns
/content-shipped
content-shipped
Log a completed piece of content to content/log.md after the user confirms it was published.
/dream-apply
dream-apply
Validate a dream artifact, review each proposal, and apply only individually accepted changes.
/dream
dream
Run a curator pass against the validated memory directory and produce a proposal artifact.
/end
end
End a session — log what happened, update state and the decision log, propose memory updates, and check for uncommitted or unpushed work
/find-context
find-context
Find relevant context files by topic. Use when you need to load files for a topic without a slash command, or when a task spans multiple domains.
/migrate-gemini
migrate-gemini
Inventory and migrate selected Gemini CLI workflows with dry-run review and parity checks.
/mine-gemini-workflows
mine-gemini-workflows
Find repeated workflows in selected Gemini CLI sessions and draft portable skills after review.
/reconcile
reconcile
Scan multi-session drift and offer individually reviewed fixes only after explicit approval.
/recover
recover
Scan orphaned worktrees and stale branches, then offer explicit approval-gated cleanup.
/setup
setup
Guided onboarding or import for durable workspace context
/start
start
Start a session — load state files, flag staleness, and give a briefing on current priorities, deadlines, and blockers
/today
today
Create a morning heartbeat from repository state and update the local heartbeat log.
/update
update
Mid-session checkpoint — append progress to today's session log and update state files if a priority shifted, without ending the session
/distribution-audit
distribution-audit
Maintainer-only. Find every file that would newly ship to adopters, classify each one against the written distribution-boundary categories, default to withhold on no clean match, and ask the maintainer only where the taxonomy does not settle it. Drives the release CLI, which refuses to produce a manifest until every shipping file has an answer.
/gaia-audit
gaia-audit
Audit memory, wiki, and auto-loaded files for duplication, conflicting instructions, and stale content. The default path researches, then asks you a single Apply / Discuss / Decline question; on Apply it applies the report, files any out-of-scope problem as a tech-debt issue, then commits, opens a PR, and merges it on a main-branch run like /update-deps. Pass --apply to re-run the apply-and-publish stage against the most recent report.
/gaia-debt
gaia-debt
Fix the tech-debt backlog, a single issue or a recommended related batch, highest severity then oldest first, on a fresh isolated branch through the audit gate, closing the issue(s) on merge. Pass `list` to see the ordered backlog, `why <issue-number>` to explain the recommendation, or a bare `<issue-number>` to fix that issue directly.
/gaia-fitness
gaia-fitness
Health-check and auto-heal this project's Claude integration, triage, heal, verify, and report an F-to-A+ grade.
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