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 huuanh20/awesome-ai-agent-skills --skill sr-savegit clone --depth 1 https://github.com/huuanh20/awesome-ai-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/huuanh20/awesome-ai-agent-skills/sr-save)<a href="https://agentmods.dev/skills/huuanh20/awesome-ai-agent-skills/sr-save"><img src="https://agentmods.dev/badge/skills/huuanh20/awesome-ai-agent-skills/sr-save/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/huuanh20/awesome-ai-agent-skills/sr-save"><img src="https://agentmods.dev/badge/skills/huuanh20/awesome-ai-agent-skills/sr-save.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 | $0.00036 | $0.01182 |
| Opus 5.5 | $0.00014 | $0.00473 |
| Sonnet 5.5 | $0.00007 | $0.00236 |
| Haiku 4.5 | $0.00004 | $0.00118 |
Grade A, and why
sr:save 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.
How it starts
The opening of the file, as written. The whole thing — 168 lines — stays where its author put it; the contents beside it link to each section on GitHub.
sr:save
Goal: distil the entire project into compact, re-loadable context files.
A future session should be able to load just _context/ and have full project understanding.
Step 0 — Identify Project
AskUserQuestion: "Which project to save context for? (slug)"
Read all available files in projects/{slug}/.
Run python .claude/scripts/srs_validator.py --dir projects/{slug}/srs/ --stats and use its
counts for quality_standards.md / session-notes.md below instead of recounting by hand.
Step 1 — Write Context Files
Write all files under projects/{slug}/_context/:
vision.md
# Vision — {Project Name}
## Problem Statement
{what problem does this system solve? 2–3 sentences from spec §1}
## Solution Summary
{what the system does, for whom, and the core value proposition}
## Success Metrics
{how we measure success — from spec §1 or brainstorm}
## Constraints
{top 3 hard constraints — tech, timeline, compliance}
features.md
# Features — {Project Name}
## IN Scope (v1.0)
| Feature | Priority | Key FRs | Notes |
|---------|----------|---------|-------|
{every confirmed in-scope feature, one row each}
## OUT of Scope (v1.0 — Deferred)
| Feature | Reason | Target version |
|---------|--------|---------------|
{every explicitly excluded feature}
tech_stack.md
# Tech Stack — {Project Name}
## Confirmed Stack
| Layer | Technology | Constraint type |
|-------|-----------|----------------|
{language, framework, DB, cloud, CI/CD — from spec §5}
## Integration Points
| System | Protocol | Direction | Auth |
|--------|----------|-----------|------|
{every external system — from SRS §3.1.3}
## Compliance Requirements
{applicable regulations + specific technical obligations}
glossary.md
# Glossary — {Project Name}
Copy of Appendix A from the SRS. Every term, acronym, abbreviation.
Include: domain terms | project-specific terms | role names | system names
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 · 168 lines · 36 tokens per session scan A f059d11cff7d
sr:save is a skill published in the GitHub repository huuanh20/awesome-ai-agent-skills (1 stars, last pushed 3mo ago), licensed MIT. It adds 36 tokens to every session and 1,182 once invoked, about $0.0001 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
handoff
Write one self-contained goal that a fresh session can continue from. Use when context is running out, before clearing a session, when usage passes 95% on a long run, or when asked for a handoff.
init
Set up Dory in this repo by exploring it, creating wiki/ from the template, and connecting the usage statusline. Use when a project has no wiki/ yet, or when asked to set up Dory.
obsidian
Read, search, and create Markdown notes inside an Obsidian vault on disk.
loop
Work through the taskboard unattended for hours, pacing against the usage limits and filling the gap before a reset with CPU-only jobs. Use when asked to run all day, work unattended, or loop over tickets.
personal-context
Interview the user to build or update a reusable "about me" context file for AI tools — who they are, their role and background, how they work, and how they want AI to help. Use when the user says "build my personal context", "create my about-me context", "set up my AI context", "update my personal context", or wants…
learn
Record a correction, decision, finding or failed attempt in the wiki. Use when the user corrects you, when a decision is made with a reason, when an experiment returns a verdict, or when you learn something the next session would otherwise re-derive.