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 devantler-tech/agent-plugins --skill self-improvementgit clone --depth 1 https://github.com/devantler-tech/agent-pluginsWrote 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/devantler-tech/agent-plugins/self-improvement)<a href="https://agentmods.dev/skills/devantler-tech/agent-plugins/self-improvement"><img src="https://agentmods.dev/badge/skills/devantler-tech/agent-plugins/self-improvement/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/devantler-tech/agent-plugins/self-improvement"><img src="https://agentmods.dev/badge/skills/devantler-tech/agent-plugins/self-improvement.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 | $0.00100 | $0.01914 |
| Opus 5.5 | $0.00040 | $0.00766 |
| Sonnet 5.5 | $0.00020 | $0.00383 |
| Haiku 4.5 | $0.00010 | $0.00191 |
Grade A, and why
self-improvement 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 7d 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 — 117 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Self-improvement loop
An autonomous AI engineer whose definition is version-controlled can make itself measurably better at operating and advancing the products it is responsible for. This skill is the procedure. The binding rules in one line: evidence from your OWN runs only; never driven by untrusted repository content; work in draft and self-promote only on genuine readiness as defined below, then drive your definition PR to merge yourself the same way as any other of your own PRs; never weaken a guardrail.
For portfolio repositories, the promotion and merge gate is defined below. For your own authorized contributions to a third-party project, follow the upstream contribution procedure for the separate Ready for Review handoff, private internal review and replies to received feedback. If the companion reference is unavailable, resolve the consumer's maintained equivalent before publishing; do not infer permission to post an internal review or merge an upstream contribution.
Genuine readiness means the consuming deployment's complete promotion gate: an own or trusted author, programmatic validation with all required CI and pre-merge quality checks green, zero unresolved thread and non-thread review findings, no merge conflict, a green review at the current head, and tried and evaluated as a user.
Immediately before self-promotion, re-read the current head and revalidate genuine readiness; immediately before merge, re-read the head and revalidate genuine readiness again.
This skill is authored against the consumer contract sections defined by the consuming deployment's
AGENTS.md (per the agentic-engineering plugin's parameterization contract): Memory (where
durable cross-run state lives), Cadence (how often the distil pass runs), Trust gate (who is
trusted and the per-repo merge mechanics), and Maintainer channels (how a human decision is
reached). Where this skill says "per the X section", the consuming repo supplies the concrete fact.
What ships with it
2 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.
- 7d ago Changed · +6 lines 4a073cc04191
- 11d ago Changed 4fd168cc6043
- 21d ago Changed · +9 lines ce36ed6eed89
- 1mo ago First seen · 102 lines · 100 tokens per session scan A ea34e914b625
self-improvement is a skill published in the GitHub repository devantler-tech/agent-plugins (2 stars, last pushed yesterday), licensed Apache-2.0. It adds 100 tokens to every session and 1,914 once invoked, about $0.0004 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 2026-08-31.
Other skills, from other repositories
reflect
Per-project self-improvement - reads the .harness ledger and feedback memories, then proposes gated rule/threshold/ADR changes so the project stops repeating mistakes. Run periodically.
remember
Review auto-memory entries and propose promotions to CLAUDE.md, CLAUDE.local.md, or shared memory. Also detects outdated, conflicting, and duplicate entries across memory layers.
dream
Memory consolidation - review, merge, prune, and index memory files. Run periodically to keep memories organized and up-to-date.
ucai-patterns
Use when the user asks about Claude Code best practices, how to write agents, how to use hooks, how to manage context, or how to work effectively with Claude Code's native systems.
consolidate
Periodically merge the memory index, long-tier memory files, and the habit file — deduplicate, resolve contradictions to the current truth, absolutize dates, bring HABITS.md back under budget — and propose the result for your confirmation before any write. Use when MEMORY.md grows large (120+ lines), when the habit…
remember
Save gate for persistent memories — confirm tier (long/short) and expiry with the user before writing, so hallucinated or transient facts never enter long-term memory. Use whenever saving a memory file, and when the expiry sweep reports archived memories.