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 Contentrain/ai --skill contentrain-doctorgit clone --depth 1 https://github.com/Contentrain/aiWrote 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/contentrain/ai/contentrain-doctor)<a href="https://agentmods.dev/skills/contentrain/ai/contentrain-doctor"><img src="https://agentmods.dev/badge/skills/contentrain/ai/contentrain-doctor/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/contentrain/ai/contentrain-doctor"><img src="https://agentmods.dev/badge/skills/contentrain/ai/contentrain-doctor.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 | $0.00028 | $0.00373 |
| Opus 5.5 | $0.00011 | $0.00149 |
| Sonnet 5.5 | $0.00006 | $0.00075 |
| Haiku 4.5 | $0.00003 | $0.00037 |
Grade A, and why
contentrain-doctor 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
Skill: Diagnose Project Health with contentrain doctor
Run health checks and turn failures into actionable fixes.
When to Use
Use this when:
- setup seems broken
- generated client may be stale
- Git/content structure may be inconsistent
- the user asks "what is wrong with this project?"
Steps
1. Run contentrain doctor
Use the CLI health check as the first diagnostic pass.
2. Group Findings
Interpret failures by category:
- Git problems
- missing
.contentrain/structure - bad config/model parsing
- orphan content
- too many pending branches
- stale SDK client
3. Fix in Priority Order
Recommended order:
- Git / initialization blockers
- config/model parse failures
- orphan content or missing paths
- branch pressure
- stale generated client
4. Apply the Correct Follow-up
- not initialized →
contentrain init - stale client →
contentrain generate - branch pressure →
contentrain diff - invalid content/model state →
contentrain validate
5. Re-run Doctor
After changes, run contentrain doctor again to confirm the project is healthy.
Related Skills
- contentrain-init — Re-initialize if project structure is broken
- contentrain-validate-fix — Fix content/model validation errors
- contentrain-generate — Regenerate SDK if client is stale
- contentrain — Core architecture and MCP tool catalog
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 · 70 lines · 28 tokens per session scan A 08cae0f617ff
contentrain-doctor is a skill published in the GitHub repository Contentrain/ai (6 stars, last pushed today), licensed MIT. It adds 28 tokens to every session and 373 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.
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