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 agentmods add skills/knitli/toolshed/codexnpx skills add knitli/toolshed --skill codexgit clone --depth 1 https://github.com/knitli/toolshedWhat 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 | $0.00000 | $0.00351 |
| Opus 5 | $0.00000 | $0.00176 |
| Sonnet 5 | $0.00000 | $0.00070 |
| Haiku 4.5 | $0.00000 | $0.00035 |
Grade A, and why
.codex 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 yesterday.
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.
What it actually says
Context Hygiene Skill
name
context-hygiene
description
Audit and maintain AI context files across tool ecosystems. Detects stale claims, contradictions between CLAUDE.md/AGENTS.md/Serena/Cursor/etc., and validates references against the actual codebase.
instructions
When activated, perform a context hygiene audit on the current repository:
1. Discovery
Scan for AI context files across all known tool ecosystems: CLAUDE.md, AGENTS.md, GEMINI.md, .claude/, .gemini/, .codex/, .cursor/, .serena/, .specify/, .roo/, .continue/, claudedocs/, specs/, plans/, docs/, info/, .mcp.json, .cursorrules, .windsurfrules, .continuerules, .clinerules, .aider.conf.yml
Report what you find as an inventory grouped by tool.
2. Staleness check
For each memory/instruction file, extract factual claims (paths, versions, symbols, counts, commands, dependencies) and validate them against the actual codebase. Report what's valid, what's stale, what's broken.
3. Drift detection
Compare memory files against each other to find contradictions and gaps. Flag where different tools have divergent views of the same project.
4. Summary
Produce a prioritized list of issues to fix, with specific file paths, line numbers, and recommended corrections.
After code changes
When you make significant changes to file structure, dependencies, versions, or architecture, proactively check whether any context files need updating. The most important files to keep current are the root-level memory files (CLAUDE.md, AGENTS.md).
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.
- yesterday First seen · 29 lines · 0 tokens per session scan A 73da82198706
.codex is a skill published in the GitHub repository knitli/toolshed (1 stars, last pushed 4mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 351 tokens. 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
motherduck-build-cfa-app
Design a MotherDuck-backed customer-facing analytics app. Use for embedded analytics, multi-tenant SaaS reporting, or product analytics for external users -- whenever the decision depends on per-customer isolation, backend routing, service-account boundaries, read scaling, or Hypertenancy-style patterns.
motherduck-build-dashboard
Build a live MotherDuck dashboard as a Dive. Use when composing one shareable KPI, trend, and breakdown story over existing MotherDuck data, especially when the result should stay a saved workspace artifact rather than a full application.
motherduck-build-data-pipeline
Design an end-to-end MotherDuck data pipeline. Use for ETL/ELT workflows -- choosing raw, staging, and analytics boundaries, bulk ingestion paths, transformation sequencing, dlt/dbt integration, publication targets, or whether DuckLake is actually required.
motherduck-connect
Connect to MotherDuck from any application. Use when setting up database connectivity via the Postgres endpoint (recommended), pgduckdb, native DuckDB API, or JDBC. Covers connection strings, authentication, SSL, and environment variable configuration.
motherduck-create-dive
Create, edit, manage, share, or embed MotherDuck Dives — live React + SQL dashboards, charts, and data apps saved in the workspace. Use for any dashboard, chart, KPI display, or data visualization over MotherDuck data, and for Dive authoring mechanics such as getdiveguide, useSQLQuery, local preview, version history…
motherduck-create-flight
Create, schedule, run, and debug MotherDuck Flights — Python jobs that run on MotherDuck compute. Use whenever someone wants to create a flight, schedule a Python script or recurring job on MotherDuck, set up scheduled ingestion from Postgres, dlt sources, S3, BigQuery, Snowflake, or APIs, refresh aggregates or…