A command for measuring how much context space skills use and checking their quality. Context space is the amount of conversation and instruction text an AI can consider at once.

Use it interactively or in scripts to compare targets, find the largest descriptions, inspect one target, and report missing fields, short descriptions, or absent trigger phrases.

It helps identify oversized skill descriptions and missing or weak metadata before they make an AI workflow harder to manage.

analyze — Command by runkids

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.

agentmods

npx agentmods add commands/runkids/skillshare/analyze

Clone the repo

git clone --depth 1 https://github.com/runkids/skillshare

Per session 0 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.

When invoked 1,909 The whole file, excluding the scripts and references it only reads on demand.

Security scan A 0 findings. Scan, not verified.

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.

Model	Per session	Once invoked
Fable 5	$0.00000	$0.01909
Opus 5	$0.00000	$0.00955
Sonnet 5	$0.00000	$0.00382
Haiku 4.5	$0.00000	$0.00191

Measured yesterday against content hash cda62ca63083, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

analyze 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.

website/docs/reference/commands/analyze.md · 260 lines

How it starts

The opening of the file, as written. The whole thing — 260 lines — stays where its author put it; the contents beside it link to each section on GitHub.

analyze

Analyze context window usage and skill quality for each target's skills.

skillshare analyze                    # Interactive TUI (default)
skillshare analyze claude             # Details for a single target
skillshare analyze --verbose          # Top 10 largest descriptions
skillshare analyze --json             # Machine-readable output
skillshare analyze -p                 # Project mode

When to Use

Optimize Context Budget

Identify which skills consume the most context window tokens:

skillshare analyze           # Browse all targets interactively

Compare Across Targets

See how context usage differs between targets (e.g., Claude vs Cursor):

skillshare analyze           # Tab to switch targets in TUI

Check Skill Quality

Find skills with missing fields, short descriptions, or no trigger phrases:

skillshare analyze           # Lint icons (✗/⚠) appear in TUI

CI/Scripting

Get machine-readable context metrics and lint results:

skillshare analyze --json | jq '.targets[].always_loaded.estimated_tokens'
skillshare analyze --json | jq '.targets[].skills[] | select(.lint_issues | length > 0)'

What It Does

analyze calculates two layers of context cost for each skill:

Always loaded — name + description from SKILL.md frontmatter (loaded into context on every request for skill matching)
On-demand — Skill body after frontmatter (loaded only when the skill is triggered)

Token estimates use chars / 4 as an approximation.

Skill Quality Lint

In addition to token analysis, analyze runs a built-in lint engine against every skill. Lint rules check SKILL.md structure and description quality, surfacing issues directly in the TUI and JSON output.

Rule	Severity	What it checks
`missing-name`	error	`name` field is empty or missing
`missing-description`	error	`description` field is empty or missing
`empty-body`	error	Skill body (after frontmatter) is empty
`description-too-short`	warning	Description under 50 characters
`description-too-long`	warning	Description exceeds 1024-character target limit
`description-near-limit`	warning	Description between 900–1024 characters
`no-trigger-phrase`	warning	Description lacks trigger phrases (e.g. "Use when…")

Read the full file on GitHub · 260 lines

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.

yesterday First seen · 260 lines · 0 tokens per session scan A cda62ca63083

Subscribe to this mod's changes

analyze is a command published in the GitHub repository runkids/skillshare (2,597 stars, last pushed 5d ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 1,909 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 2026-08-30.

Memory and context Writing and docs ai cli codex cursor skills team-management

analyze

Getting it into your agent

What it costs to keep this loaded

Grade A, and why

Nothing flagged

How it starts

analyze

When to Use

Optimize Context Budget

Compare Across Targets

Check Skill Quality

CI/Scripting

What It Does

Skill Quality Lint

What this file has done since we first saw it

Other commands, from other repositories

constraints

webperf

spec

plan

pm-arch

pm-done