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 jdrhyne/agent-skills --skill self-improving-agentgit clone --depth 1 https://github.com/jdrhyne/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/jdrhyne/agent-skills/self-improving-agent)<a href="https://agentmods.dev/skills/jdrhyne/agent-skills/self-improving-agent"><img src="https://agentmods.dev/badge/skills/jdrhyne/agent-skills/self-improving-agent/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/jdrhyne/agent-skills/self-improving-agent"><img src="https://agentmods.dev/badge/skills/jdrhyne/agent-skills/self-improving-agent.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high Privilege Escalation · line 128 Code accesses credential files (SSH keys, AWS credentials, etc.). This could indicate credential theft attempts.Fix: Remove references to credential paths. Use environment variables or secrets managers. For docs, use placeholder paths (e.g., /path/to/config). Never load .env or token files in production code paths.
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 | $0.00104 | $0.00876 |
| Opus 5 | $0.00052 | $0.00438 |
| Sonnet 5 | $0.00021 | $0.00175 |
| Haiku 4.5 | $0.00010 | $0.00088 |
Grade A, and why
self-improving-agent 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 — 131 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Self-Improvement Skill
Capture non-obvious lessons, failures, and feature requests in a small local knowledge base so the same mistakes are less likely to repeat.
When to Use
- A command, tool, or integration fails in a way worth remembering
- The user corrects an assumption or teaches a project-specific convention
- You discover a better repeatable workflow
- The user asks for a missing capability that should be tracked
- You are starting work in an area with known prior learnings
Storage
Keep entries in a local .learnings/ directory:
.learnings/LEARNINGS.md.learnings/ERRORS.md.learnings/FEATURE_REQUESTS.md
Create the directory on first use if it does not exist.
Record Types
Learning
Use for corrections, conventions, and better practices.
## [LRN-YYYYMMDD-XXX] category
**Logged**: ISO-8601 timestamp
**Priority**: low | medium | high | critical
**Status**: pending
**Area**: frontend | backend | infra | tests | docs | config
### Summary
One-line learning
### Details
What happened and what is now known to be correct
### Suggested Action
Specific follow-up or rule
### Metadata
- Source: conversation | error | user_feedback
- Related Files: path/to/file.ext
- Tags: tag1, tag2
- See Also: LRN-20250110-001
Error
Use for reproducible failures or flaky workflows.
## [ERR-YYYYMMDD-XXX] tool_or_workflow
**Logged**: ISO-8601 timestamp
**Priority**: high
**Status**: pending
**Area**: frontend | backend | infra | tests | docs | config
### Summary
Short failure description
### Error
Exact error text or symptoms
### Context
- Operation attempted
- Inputs or environment details
### Suggested Fix
Likely next step
Feature Request
Use for missing capabilities the user wants tracked.
## [FEAT-YYYYMMDD-XXX] capability_name
**Logged**: ISO-8601 timestamp
**Priority**: medium
**Status**: pending
**Area**: frontend | backend | infra | tests | docs | config
### Requested Capability
What the user wanted
### User Context
Why it matters
### Suggested Implementation
Likely extension point or implementation direction
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 · 131 lines · 104 tokens per session scan A 12dc9340d55e
self-improving-agent is a skill published in the GitHub repository jdrhyne/agent-skills (241 stars, last pushed 9d ago), licensed MIT. It adds 104 tokens to every session and 876 once invoked, about $0.0005 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
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Verify and debug native, React Native, Expo, or Flutter apps on an iOS Simulator with agent-device. Use when an agent needs to launch an app, inspect its live UI, tap, type, scroll, validate a code change, collect failure evidence, or reproduce a workflow on an iPhone or iPad Simulator.
auto-fix
A bug-fixing workflow that first reproduces a problem with a test, then applies the smallest code change needed. It also checks the related code path and runs tests afterward.
harness-doctor
Check whether this project's Agentsmith harness is installed correctly and healthy — fires on "is my harness set up right?", "harness doctor", "check my harness". Part of the Agentsmith harness; checks each selected agent's managed rules, settings, skills, hooks, verification, and leanness with a one-line fix for each…
debugging
A structured method for finding and fixing software bugs. It starts by writing a test that reproduces the failure, then investigates its underlying cause before making a small fix.
obra/superpowers@systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
ast-refactoring
A code-refactoring guide that uses an abstract syntax tree, a structured representation of source code, to make changes based on code meaning rather than text matching.