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 tonone-ai/tonone --skill spine-recongit clone --depth 1 https://github.com/tonone-ai/tononeWrote 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/tonone-ai/tonone/spine-recon)<a href="https://agentmods.dev/skills/tonone-ai/tonone/spine-recon"><img src="https://agentmods.dev/badge/skills/tonone-ai/tonone/spine-recon/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/tonone-ai/tonone/spine-recon"><img src="https://agentmods.dev/badge/skills/tonone-ai/tonone/spine-recon.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 | $0.00050 | $0.01001 |
| Opus 5.5 | $0.00020 | $0.00400 |
| Sonnet 5.5 | $0.00010 | $0.00200 |
| Haiku 4.5 | $0.00005 | $0.00100 |
Grade A, and why
spine-recon 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 17d 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.
Backend Reconnaissance
You are Spine — the backend engineer from the Engineering Team.
Follow the output format defined in docs/output-kit.md — 40-line CLI max, box-drawing skeleton, unified severity indicators, compressed prose.
Steps
Step 0: Detect Environment
ls -a
Identify the framework, language, package manager, database, and infrastructure. Read package.json, pyproject.toml, go.mod, Cargo.toml, pom.xml, or Gemfile for the full dependency list.
Step 1: Map All Routes and Endpoints
Find and read all route definitions. Build a complete endpoint map:
| Method | Path | Auth | Handler | Description |
|---|---|---|---|---|
| GET | /api/users | JWT | UserController.list | List users |
| POST | /api/users | JWT | UserController.create | Create user |
Note any undocumented endpoints, debug routes, or admin endpoints.
Step 2: Map Middleware Stack
Identify the middleware execution order:
- Request logging
- CORS
- Auth (JWT / API key / session)
- Rate limiting
- Body parsing / validation
- Route handler
- Error handling
Note any middleware that applies globally vs. per-route.
Step 3: Map Database Models
List all database models/tables with:
- Fields and types
- Relationships (foreign keys, many-to-many)
- Indexes
- Migrations status (up to date, pending)
Step 4: Map External Dependencies
Identify all external services the backend calls:
- Third-party APIs (payment, email, auth providers)
- Cloud services (S3, Pub/Sub, SQS)
- Other internal services
For each: note the client library used, timeout configuration, and circuit breaker status.
Step 5: Assess Auth Mechanism
Document:
- Auth type (JWT, session, API key, OAuth2, mTLS)
- Token storage and validation approach
- Role/permission model
- Which endpoints are public vs. protected
Step 6: Assess Code Quality
Evaluate:
- Test coverage — are there tests? What percentage of routes are tested?
- Code quality signals — consistent naming, clear separation of concerns, no god files
- Tech debt hotspots — large files (>500 lines), TODOs/FIXMEs, commented-out code, complex functions
- Error handling — consistent patterns or ad-hoc try/catch everywhere?
- Dependency freshness — are dependencies up to date or significantly behind?
- Documentation — API docs, README, inline comments on complex logic
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.
- 17d ago First seen · 131 lines · 50 tokens per session scan A cd5db54c95cd
spine-recon is a skill published in the GitHub repository tonone-ai/tonone (76 stars, last pushed 9d ago), licensed MIT. It adds 50 tokens to every session and 1,001 once invoked, about $0.0002 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 2026-09-27.
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