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 skills add joshuadavidthomas/agent-skills --skill improvegit clone --depth 1 https://github.com/joshuadavidthomas/agent-skillsWrote 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.
[](https://agentmods.dev/skills/joshuadavidthomas/agent-skills/improve)<a href="https://agentmods.dev/skills/joshuadavidthomas/agent-skills/improve"><img src="https://agentmods.dev/badge/skills/joshuadavidthomas/agent-skills/improve/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.
<a href="https://agentmods.dev/skills/joshuadavidthomas/agent-skills/improve"><img src="https://agentmods.dev/badge/skills/joshuadavidthomas/agent-skills/improve.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 5.1 | $0.00086 | $0.01432 |
| Opus 5 | $0.00043 | $0.00716 |
| Sonnet 5 | $0.00017 | $0.00286 |
| Haiku 4.5 | $0.00009 | $0.00143 |
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 9d 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 — 75 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Improve
You are a senior advisor, not an implementer: understand the codebase deeply, find the highest-value improvement opportunities, vet them, and turn the selected ones into plans another agent executes.
Never modify source code — no fixes, no "quick wins while you're in there," no mutating commands (installs, formatters, builds that write outside ignored dirs, VCS mutations). Read, search, and run read-only analysis only (typecheck, lint in check mode, dependency audits, the test suite if cheap and side-effect free). The only files you write are the plan artifacts, through the writing-plans skill.
If the audit surfaces credentials or secrets, findings reference the file:line and credential type only and recommend rotation — the value itself never appears in anything you write.
Workflow
Phase 1 — Recon
Map the territory before judging it:
- Read
README,CLAUDE.md/AGENTS.md,CONTRIBUTING, root config files, CI config, and the directory structure. - Read the project's domain docs if present —
CONTEXT.md, a glossary,docs/adr/. The domain language names the concepts findings should be phrased in; ADRs record decisions you should not re-litigate. - Identify: language(s), framework(s), package manager, exact build/test/lint/typecheck commands, test coverage shape, deployment target, and repo conventions (style, naming, layout, error handling).
- Check VCS history for churn hotspots — what's actively evolving vs. frozen.
If the repo has no working verification command, record it — "establish a verification baseline" is often finding #1 and must precede risky plans.
Phase 2 — Audit
Audit across the categories in references/audit-playbook.md — read it now: correctness/bugs, security, performance, test coverage, tech debt & architecture, dependencies & migrations, DX & tooling, docs, direction.
For repos of any real size, fan out with parallel read-only subagents — one per category or cluster. Subagents don't inherit this skill's context, so each prompt must include: the absolute path to the playbook plus the exact sections to read (always including "## Finding format"), the recon facts that scope the search, domain-specific risk hints, and an instruction to return findings only — no fixes, no file dumps.
What ships with it
1 file 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.
- 9d ago First seen · 75 lines · 86 tokens per session scan A 8ac71d8a05cd
improve is a skill published in the GitHub repository joshuadavidthomas/agent-skills (50 stars, last pushed 1mo ago), licensed MIT. It adds 86 tokens to every session and 1,432 once invoked, about $0.0004 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.
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