Softaworks Agent Toolkit is a collection of packaged instructions, scripts, agents, and commands that give AI coding assistants reusable procedures for development, documentation, planning, and professional work. Developers install its components individually in compatible coding-agent environments; catalogue entries represent parts of this toolkit.
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 softaworks/agent-toolkit --skill lesson-learnedgit clone --depth 1 https://github.com/softaworks/agent-toolkitWrote 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/softaworks/agent-toolkit/lesson-learned)<a href="https://agentmods.dev/skills/softaworks/agent-toolkit/lesson-learned"><img src="https://agentmods.dev/badge/skills/softaworks/agent-toolkit/lesson-learned/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/softaworks/agent-toolkit/lesson-learned"><img src="https://agentmods.dev/badge/skills/softaworks/agent-toolkit/lesson-learned.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk pass
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 | $0.00064 | $0.01097 |
| Opus 5.5 | $0.00026 | $0.00439 |
| Sonnet 5.5 | $0.00013 | $0.00219 |
| Haiku 4.5 | $0.00006 | $0.00110 |
Grade A, and why
lesson-learned 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 1mo 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.
This is a copy
100% identical to lesson-learned — 105 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 106 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Lesson Learned
Extract specific, grounded software engineering lessons from actual code changes. Not a lecture -- a mirror. Show the user what their code already demonstrates.
Before You Begin
Load the principles reference first.
- Read
references/se-principles.mdto have the principle catalog available - Optionally read
references/anti-patterns.mdif you suspect the changes include areas for improvement - Determine the scope of analysis (see Phase 1)
Do not proceed until you've loaded at least se-principles.md.
Phase 1: Determine Scope
Ask the user or infer from context what to analyze.
| Scope | Git Commands | When to Use |
|---|---|---|
| Feature branch | git log main..HEAD --oneline + git diff main...HEAD |
User is on a non-main branch (default) |
| Last N commits | git log --oneline -N + git diff HEAD~N..HEAD |
User specifies a range, or on main (default N=5) |
| Specific commit | git show <sha> |
User references a specific commit |
| Working changes | git diff + git diff --cached |
User says "what about these changes?" before committing |
Default behavior:
- If on a feature branch: analyze branch commits vs main
- If on main: analyze the last 5 commits
- If the user provides a different scope, use that
Phase 2: Gather Changes
- Run
git logwith the determined scope to get the commit list and messages - Run
git difffor the full diff of the scope - If the diff is large (>500 lines), use
git diff --statfirst, then selectively read the top 3-5 most-changed files - Read commit messages carefully -- they contain intent that raw diffs miss
- Only read changed files. Do not read the entire repo.
Phase 3: Analyze
Identify the dominant pattern -- the single most instructive thing about these changes.
Look for:
- Structural decisions -- How was the code organized? Why those boundaries?
- Trade-offs made -- What was gained vs. sacrificed? (readability vs. performance, DRY vs. clarity, speed vs. correctness)
- Problems solved -- What was the before/after? What made the "after" better?
- Missed opportunities -- Where could the code improve? (present gently as "next time, consider...")
What ships with it
3 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.
- 1mo ago First seen · 106 lines · 64 tokens per session scan A f2ee51f30ea8
lesson-learned is a skill published in the GitHub repository softaworks/agent-toolkit (2,558 stars, last pushed 7mo ago), licensed MIT. It adds 64 tokens to every session and 1,097 once invoked, about $0.0003 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 100% identical to lesson-learned, differing in 105 lines, and is treated as a copy.
Other skills, from other repositories
gh-find-prs
Survey open Codewhale PRs and triage each for mergeability and disposition against the real landing branch.
review
Diff-scoped correctness review that reads the codebase around the change — callers, contracts, and invariants — and returns line-anchored findings ranked by severity with confidence, then a merge-risk verdict. Use for reviewing a PR, diff, or named change set. Not for style review, approvals-as-rubber-stamp, or…
simplify
Review recent code changes for reuse, code quality, and efficiency, then directly apply straightforward cleanup improvements. Use when the user wants a post-implementation cleanup pass, pre-PR polish, or asks to simplify/refine recent changes. Invoke with /simplify or /simplify .
find-simplifications
Use for a periodic repo-wide sweep of qwen-code for accumulated excess surface — dead components and files, orphaned locale keys, exports nothing consumes, added-then-removed scaffolding — filing candidates on a tracking issue and landing only what a maintainer has said yes to. Repo-wide and evidence-first; every…
review-this-branch
Run the nohuman review gate (fresh-session adversarial reviewer + tamper guard) over the current branch or a GitHub pull request, with no server, no database, and no onboarding, and relay the pass/fail checklist.
fabric-spec
Starts a persistent Pi Fabric spec supervisor that audits the main session against a feature design spec and steers only when a requirement lacks verified evidence. Use for strict, unblocked spec compliance while the main agent keeps full freedom to orchestrate.