moss-pikachu

moss-pikachu is a skill for Cursor from usemoss/moss. It costs 80 tokens per session (710 once invoked), scanned A, original, BSD-2-Clause.

A project-specific guide for building Moss Pikachu, a macOS menu-bar app that searches files by meaning and includes a small animated pet.

In plain words
What is it for?
Use it when changing the Moss Pikachu app, its Python search worker, file indexing, search overlay, animations, or Moss integration.
Why use it?
It keeps development aligned with the project's required architecture, platform limits, dependencies, credential handling, and validation checks.

Skill for Cursor

Written for Cursor: installed under .cursor/.

Good fit Use it when changing the Moss Pikachu app, its Python search worker, file indexing, search overlay, animations, or Moss integration.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/usemoss/moss/moss-pikachu
Install

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.

Any agent
npx skills add usemoss/moss --skill moss-pikachu
Clone the repo
git clone --depth 1 https://github.com/usemoss/moss

Made for: Cursor.

Wrote 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.

agentmods badge for moss-pikachu

README.md
[![agentmods](https://agentmods.dev/badge/skills/usemoss/moss/moss-pikachu/github.svg)](https://agentmods.dev/skills/usemoss/moss/moss-pikachu)
Your own site
<a href="https://agentmods.dev/skills/usemoss/moss/moss-pikachu"><img src="https://agentmods.dev/badge/skills/usemoss/moss/moss-pikachu/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.

agentmods 80×15 button for moss-pikachu

Your own site · 80×15
<a href="https://agentmods.dev/skills/usemoss/moss/moss-pikachu"><img src="https://agentmods.dev/badge/skills/usemoss/moss/moss-pikachu.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 80 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 710 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00080 $0.00710
Opus 5.5 $0.00032 $0.00284
Sonnet 5.5 $0.00016 $0.00142
Haiku 4.5 $0.00008 $0.00071

Measured 1mo ago against content hash fd2e7e2a67d7, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

moss-pikachu 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.

The scan reads SKILL.md. This mod also ships 2 executable files (scripts/setup-moss-venv.sh, scripts/validate-phase.sh), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

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.

examples/moss-pikachu/.cursor/skills/moss-pikachu/SKILL.md · 72 lines

How it starts

The opening of the file, as written. The whole thing — 72 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Moss Pikachu Agent Skill

Project docs (start here)

Doc Purpose
project-summary.md Full technical reference
how-to.md End-user guide
contribution.md Contributor guide

Specialized skills

Skill Use when
moss-pikachu-semantic-search Search quality, query tuning
moss-pikachu-indexing Indexing, manual/automatic modes
moss-pikachu-contributing PRs and contribution workflow

Read order

  1. architecture.md
  2. moss-integration.md
  3. macos-patterns.md
  4. ui-animations.md
  5. pitfalls.md

Non-negotiables

  • Xcode .app bundle — not a root-level SPM executable for the menu bar app
  • macOS-only target — SUPPORTED_PLATFORMS = macosx
  • Python worker for Moss on macOS — Moss Swift SPM is iOS-only
  • pip install moss>=1.6.0 — GitHub main sdks/python/sdk lacks SessionIndex API
  • No hardcoded credentials — MOSS_PROJECT_ID / MOSS_PROJECT_KEY via Keychain or env
  • NSPanel for search overlay — not WindowGroup (avoids Dock icon)

Phase gates

Run before advancing phases:

./.cursor/skills/moss-pikachu/scripts/validate-phase.sh 1
./.cursor/skills/moss-pikachu/scripts/validate-phase.sh 2
./.cursor/skills/moss-pikachu/scripts/validate-phase.sh 3

Task sequencing

Phase Scope
1 Menu bar, hotkey, search overlay shell, settings window
2 FileMonitor, moss_worker.py, MossBridge, SearchService
3 Pikachu animations, live search UI, settings, polish

Do not wire live search before MossBridge + SearchService compile and pass Phase 2 validation.

Code conventions

  • @MainActor for all UI updates
  • async/await for MossBridge calls (not callbacks)
  • Line-delimited JSON between Swift and Python (\n terminated)
  • Debounce FSEvents (100ms) and search input (200ms)
  • MARK: sections in Swift service files

Read the full file on GitHub · 72 lines

Files

What ships with it

7 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.

Changes

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.

  1. 1mo ago First seen · 72 lines · 80 tokens per session scan A fd2e7e2a67d7

Subscribe to this mod's changes

moss-pikachu is a skill published in the GitHub repository usemoss/moss (798 stars, last pushed today), licensed BSD-2-Clause. It adds 80 tokens to every session and 710 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 2026-08-30.

Related

Other skills, from other repositories

rag-expert

Design retrieval-augmented generation systems: chunking, embeddings, vector and hybrid search, reranking, grounding and evaluation. Use when the user mentions RAG, retrieval, semantic search, embeddings, vector databases, pgvector, Chroma, Qdrant, Pinecone, chunking or reranking, wants an assistant answering over…

personamanagmentlayer/pcl · 97 tokens

rag-retrieval-audit

Diagnose missing evidence in a RAG pipeline with labeled queries, chunk/trace inspection, Recall@K/MRR/nDCG, and stage-isolated failure taxonomy. Use when retrieval looks fine but answers fail, or when index/chunk/filter/rerank changes need before/after proof.

alivirgo/Major-AI-Skills · 66 tokens

Hybrid Search Architect

Designs a hybrid retrieval pipeline combining dense vector search and BM25 sparse search with reciprocal rank fusion, and explains when to use each configuration.

Notysoty/openagentskills · 32 tokens

molecular-rag

Retrieve structurally similar compounds with known properties from ChEMBL/ZINC to ground predictions and inform optimization. Based on MolRAG (Xian 2025, ACL).

synthetic-sciences/openscience · 40 tokens

nemotron-retrieval-recipes

Use when planning, debugging, tuning, evaluating, exporting, or deploying public Nemotron embed/rerank retrieval recipes.

NVIDIA-NeMo/Nemotron · 36 tokens

memex-search

Discover prior agent work across sessions, projects, providers, and machines. Use for historical investigations and analogous solutions, or as a fallback when native conversation and history tools cannot recover the needed evidence.

nicosuave/memex · 42 tokens