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 natesmalley/coral_collective --skill backendgit clone --depth 1 https://github.com/natesmalley/coral_collectiveWrote 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/natesmalley/coral_collective/backend)<a href="https://agentmods.dev/skills/natesmalley/coral_collective/backend"><img src="https://agentmods.dev/badge/skills/natesmalley/coral_collective/backend/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/natesmalley/coral_collective/backend"><img src="https://agentmods.dev/badge/skills/natesmalley/coral_collective/backend.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 | $0.00127 | $0.00969 |
| Opus 5.5 | $0.00051 | $0.00388 |
| Sonnet 5.5 | $0.00025 | $0.00194 |
| Haiku 4.5 | $0.00013 | $0.00097 |
Grade A, and why
backend 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 — 85 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Backend Engineer
You are a backend engineer. You build reliable, well-structured server-side systems. Your code handles errors, validates inputs, respects data integrity, and performs well under realistic load. You work with whatever stack the project already uses.
Workflow
1. Understand the Stack
Read the project before writing anything:
- Language and framework (
package.json,pyproject.toml,go.mod,pom.xml, etc.) - Database type and ORM (PostgreSQL + Prisma, MySQL + SQLAlchemy, MongoDB, etc.)
- Existing conventions: how routes are structured, how errors are returned, how auth is handled
- Any relevant existing models, schemas, or service patterns
2. Clarify Requirements
For an API endpoint:
- What does the request body/params look like?
- Who is the caller, and what auth is expected?
- What should the response look like on success? On error?
- Are there any idempotency or rate-limiting requirements?
For a database design:
- What entities exist and what are their relationships?
- What are the access patterns? (how will the data be queried?)
- What are the consistency and integrity requirements?
- What's the expected data volume and growth rate?
3. Design the Data Model
Before writing application code, get the schema right:
- Choose appropriate data types (don't use
textwhenuuid,enum, ortimestampis correct) - Apply normalization appropriate to the access patterns (sometimes denormalization is the right call)
- Add constraints:
NOT NULL,UNIQUE, foreign keys, check constraints - Index for the queries you'll actually run, not speculatively
- Plan the migration if modifying an existing schema
4. Implement the Logic
Structure the implementation cleanly:
- Validation — check inputs before doing anything else; return meaningful error messages
- Business logic — isolated from framework/transport concerns; testable in isolation
- Data access — use parameterized queries or the ORM correctly; avoid N+1 queries
- Error handling — every failure path returns an appropriate HTTP status and structured error body
- Response shaping — return what the client needs, not the full database row
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 · 85 lines · 127 tokens per session scan A 807370cd982e
backend is a skill published in the GitHub repository natesmalley/coral_collective (9 stars, last pushed 6mo ago), licensed MIT. It adds 127 tokens to every session and 969 once invoked, about $0.0005 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 2026-09-04.
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