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 backend-mindsetgit 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/backend-mindset)<a href="https://agentmods.dev/skills/huuanh20/awesome-ai-agent-skills/backend-mindset"><img src="https://agentmods.dev/badge/skills/huuanh20/awesome-ai-agent-skills/backend-mindset/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/backend-mindset"><img src="https://agentmods.dev/badge/skills/huuanh20/awesome-ai-agent-skills/backend-mindset.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 | $0.00083 | $0.00501 |
| Opus 5.5 | $0.00033 | $0.00200 |
| Sonnet 5.5 | $0.00017 | $0.00100 |
| Haiku 4.5 | $0.00008 | $0.00050 |
Grade A, and why
backend-mindset 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.
What it actually says
Backend Development Skill
References
| File | Covers |
|---|---|
references/api-design.md |
REST, GraphQL, gRPC — URL rules, status codes, protocol selection |
references/authentication.md |
OAuth 2.1, JWT, RBAC, MFA, session management |
references/security.md |
OWASP Top 10 (2025), rate limiting, headers, secrets |
references/performance.md |
Indexing, caching patterns, load balancing, async processing |
references/architecture.md |
Microservices patterns, event-driven, CQRS, scalability |
references/testing.md |
TDD, test pyramid, contract testing, migration tests |
references/code-quality.md |
SOLID, design patterns, refactoring signals |
Quick Decision Guide
| Need | Choose |
|---|---|
| Public / browser clients | REST or GraphQL |
| Internal high-throughput | gRPC |
| Flexible data fetching | GraphQL |
| Streaming | gRPC (bidirectional) or SSE |
| Single team, unclear domain | Monolith |
| Multiple autonomous teams | Microservices |
| Audit trail required | Event Sourcing |
| Read/write load imbalance | CQRS |
| Task >200ms off request path | Async queue |
| Session across instances | Redis (never in-process) |
Defaults
- Every non-public endpoint requires explicit auth declaration — no implicit defaults
- Input validation at every system boundary (API handler, event consumer, file parser)
- Parameterized queries everywhere — never string-interpolate user input
- Rate limiting on auth endpoints, public API, and file upload (see security.md for thresholds)
- Integration tests hit real infrastructure (Testcontainers) — in-memory substitutes hide real bugs
- TDD: write the failing test before any implementation
- Services return result envelopes — never throw for expected failures (404, 400, 409)
What ships with it
7 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 · 46 lines · 83 tokens per session scan A 886ff1082a1f
backend-mindset is a skill published in the GitHub repository huuanh20/awesome-ai-agent-skills (1 stars, last pushed 3mo ago), licensed MIT. It adds 83 tokens to every session and 501 once invoked, about $0.0003 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
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.
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.
wrap-up
Close out a task by folding what it taught into the wiki, updating the taskboard, and linting. Use at the end of a task, before clearing a session, or when asked to wrap up.
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.
mcp-tool-integration
Implementing and connecting Model Context Protocol (MCP) tools. Use when you need to extend an agent's capabilities with external data sources or execution environments.