LLM Mart Basic
@llm-mart · Joined Jun 2026
Discovers dimensional vocabulary for codebases by analyzing naming conventions and protocol patterns
Propagates dimensional annotations through arithmetic and call chains, reporting mismatches found during propagation
Validates dimensional consistency and detects dimensional bugs in annotated code
Analyzes data flow from source to vulnerability sink, mapping trust boundaries, API contracts, environment protections, and cross-references. Spawned by fp-check during Phase 1 verification.
Verifies whether a suspected vulnerability is actually exploitable by proving attacker control, mathematical bounds, and race condition feasibility. Spawned by fp-check during Phase 2 verification.
Creates proof-of-concept exploits (pseudocode, executable, and unit tests) demonstrating a verified vulnerability, plus negative PoCs showing exploit preconditions. Spawned by fp-check during Phase 4 verification.
Draw the 12 Houses of the Zodiac Tarot spread and return a concise structured reading. Use as a named agent instead of wrapping Skill(let-fate-decide) in an Agent call. Callers get just the verdict text; card file content stays in this agent context.
Deduplication judge for the rust-review pipeline. Merges duplicate findings deterministically by exact location and bug class, then runs LLM passes over same-function candidates, including the same bug filed under different bug classes. Spawned by the rust-review skill orchestrat
Second-stage judge in the rust-review pipeline. Runs after dedup-judge on merged primaries only. Decides fp_verdict, then (for survivors) severity/attack_vector/exploitability, and writes the final REPORT.md + REPORT.sarif. Spawned by the rust-review skill orchestrator only.
Runs one assigned rust-review cluster task and writes finding files to the run's output directory. Spawned by the rust-review skill orchestrator only.
Evaluates APIs, configurations, and library interfaces for misuse resistance and footgun potential. Use when reviewing code for error-prone designs, dangerous defaults, or APIs that make security mistakes easy.
Checks one documented requirement against the code that should implement it, and returns a verdict with the lines that evidence it. Writes its analysis to disk and returns a compact record. Use for a single requirement; use the spec-compliance workflow for a whole document.
Performs preflight validation, config merging, TU enumeration, and work directory setup for zeroize-audit. Produces merged-config.yaml, preflight.json, and orchestrator-state.json.
Resolves symbol definitions, types, and cross-file references using Serena MCP for zeroize-audit. Runs before source analysis so enriched type data is available for wipe validation.
Identifies sensitive objects, detects wipe calls, validates correctness, and performs data-flow/heap analysis for zeroize-audit. Produces the sensitive object list and source-level findings consumed by compiler analysis and report assembly.
Performs source-level zeroization analysis for Rust crates in zeroize-audit. Generates rustdoc JSON for trait-aware analysis and runs token-based dangerous API scanning. Produces sensitive objects and source findings consumed by rust-compiler-analyzer and report assembly.
Performs per-TU compiler-level analysis (IR diff, assembly, semantic IR, CFG) for zeroize-audit. One instance runs per translation unit, enabling parallel execution across TUs.
Performs crate-level MIR and LLVM IR analysis for Rust in zeroize-audit. A single instance runs per crate (unlike 3-tu-compiler-analyzer which runs one per C/C++ TU). Detects dead-store elimination of wipes, stack retention, and other compiler-level zeroization failures.
Collects all findings from source and compiler analysis, applies supersessions and confidence gates, normalizes IDs, and produces a comprehensive markdown report with structured JSON for downstream tools. Supports dual-mode invocation: interim (findings.json only) and final (merg
Crafts bespoke proof-of-concept programs demonstrating that zeroize-audit findings are exploitable. Reads source code and finding details to generate tailored PoCs — each PoC is individually written, not templated. Each PoC exits 0 if the secret persists or 1 if wiped. Mandatory
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.
/review-ui
review-ui
Run a UI code review on the current snippet — Before / After / Why table per review-format, scoped to review-checklist.
/scan-ai-tells
scan-ai-tells
Scan UI or marketing copy for AI-default tells and content-authenticity misses — deletion list, not a redesign brief.
/sound-pass
sound-pass
Decide which moments earn a sound, design one material family, generate the files (ElevenLabs if keyed, synth or CC0 if not), and wire them in — returns a sound map table.
/svg-animate
svg-animate
Animate an SVG — icon, logo reveal, stroke draw, morph, mascot loop — or turn a frame sequence / flat clip into one editable animated SVG, with the engine chosen for where the file lives.
/svg-create
svg-create
Author or clean up an SVG asset — icon, illustration, mascot pose, logo mark — so it scales from viewBox, recolors from tokens, animates without a rewrite, is optimized, and has an accessible name.
/context-end
Context end
Close a Context OS session through its review-gated workflow
/context-setup
Context setup
Set up Context OS through its review-gated lifecycle workflow
/context-start
Context start
Start a read-only Context OS continuity review
/context-update
Context update
Save a review-gated Context OS checkpoint
/token-optimizer
token-optimizer
Route broad repository discovery to a cheaper worker model using each client's native subagents, keeping the main agent for decisions and targeted verification. Use when asked to "reduce token usage", "delegate bulk reading", "set up a cheap reader agent", or "why is my context filling up".
/aggregate-logs
aggregate-logs
Generate LEARNINGS.md from skill execution logs over a configurable time window.
/analyze-skill
analyze-skill
Analyze skill file complexity metrics and generate modularization recommendations for splitting or progressive loading.
/context-report
context-report
Generate context optimization report for skill directories
/create-command
create-command
Create slash commands with brainstorming and best practices
/create-hook
create-hook
Create hooks with brainstorming and security-first design
/create-skill
create-skill
Scaffold new Claude Code skills with brainstorming, TDD methodology, and proper frontmatter and module structure.
/evaluate-skill
evaluate-skill
Manually evaluate a recent skill execution to record qualitative feedback.
/hooks-eval
hooks-eval
Evaluate all hooks in a plugin for quality and compliance
/improve-skills
improve-skills
Identify and implement skill improvements from execution logs and user evaluations.
/make-dogfood
make-dogfood
Analyze and enhance Makefiles for complete functionality coverage with auto-generation capability
MCP server for reusable prompt templates, multi-step workflow chains, and quality gates. Compose agentic workflows with an operator syntax; export as native ski…
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