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/melodic-software/claude-code-plugins/improvenpx skills add melodic-software/claude-code-plugins --skill improvegit clone --depth 1 https://github.com/melodic-software/claude-code-pluginsWhat 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 | $0.00233 | $0.01760 |
| Opus 5 | $0.00117 | $0.00880 |
| Sonnet 5 | $0.00047 | $0.00352 |
| Haiku 4.5 | $0.00023 | $0.00176 |
Grade A, and why
improve 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 2d ago.
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.
How it starts
The opening of the file, as written. The whole thing — 83 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Pre-computed context
Current branch: !git branch --show-current 2>/dev/null || echo "unknown"
Recent commits: !git log --oneline -20 2>/dev/null || echo "no commits"
Working tree status (empty = clean): !{ git status --porcelain 2>/dev/null || echo "(git status unavailable)"; } | head -10
Variables
Arguments: $ARGUMENTS
Purpose
Improvement is distinct from review and planning. Review evaluates a DIFF against criteria (reactive). Planning designs NEW work (forward-looking). This skill scans EXISTING code for friction and proposes candidates for improvement (proactive).
The scan-present-pick process generalizes across improvement lenses. Each lens (action) brings its own analysis method and vocabulary via an actions/<lens>.md playbook plus a research/<lens>/ reference set, loaded only when that lens runs. The first lens, deepening, implements Ousterhout's deep-module concept: finding shallow modules (interface nearly as complex as implementation) and proposing how to deepen them. The aim is testability and AI/agent-navigability (AX): a deep module's small interface lets a reader, human or agent, grasp its purpose without traversing the whole import graph.
This finds existing friction. It does not plan new work, apply mechanical code-level tidyings, enforce rules on a diff, or review changes before a PR. Those are separate concerns handled by planning, tidying, rule-enforcement, and review tools respectively (see "Composition").
Actions
| Argument | Action | What it does |
|---|---|---|
| (empty) | Defaults to deepening |
Runs the deepening lens |
deepening |
Deepening (Ousterhout) | Shallow→deep module scan → HTML report → interview loop (with a Design-It-Twice branch for parallel interface exploration) → hand off an agreed candidate for planning. Full process: actions/deepening.md |
One lens per invocation. Lenses don't chain implicitly. Read the action's playbook for its full process.
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.
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.
- 2d ago First seen · 83 lines · 233 tokens per session scan A d852b54a3c9d
improve is a skill published in the GitHub repository melodic-software/claude-code-plugins (12 stars, last pushed 2d ago), licensed MIT. It adds 233 tokens to every session and 1,760 once invoked, about $0.0012 per session on Opus 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.
Other skills, from other repositories
deep-research
Conducts iterative deep research on any topic using web search, progressive exploration, and structured synthesis. Use when asked for comprehensive research, deep investigation, thorough analysis, or multi-source exploration of any topic. Triggers: research, investigate, deep dive, comprehensive analysis, explore…
error-ux
Principles and patterns for writing error messages that help users recover. Use when auditing, writing, or improving error messages in code. Triggers: error messages, user experience, error handling, exception messages, validation errors.
adversarial-patterns
Library of realistic adversarial attack vectors and anti-patterns to avoid. Contains examples of valid attacks and subtle gaming patterns to reject.
documentation-testing
Provides heuristics for identifying incomplete or broken documentation. Use when validating README setup instructions, testing onboarding flows, or auditing documentation quality. Triggers: docs, readme, onboarding, setup validation, documentation audit.
adversarial-analysis
Analyze code to identify explicit contracts, implicit usage patterns, and realistic boundary conditions. Contains concrete formulas for calculating input realism limits. Use before generating adversarial tests.
propagate-then-search
For constraint problems: eliminate impossibilities before guessing, reduce search space through inference, fail fast on contradictions.