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 ejentum/ejentum-mcp --skill codegit clone --depth 1 https://github.com/ejentum/ejentum-mcpWrote 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/ejentum/ejentum-mcp/code)<a href="https://agentmods.dev/skills/ejentum/ejentum-mcp/code"><img src="https://agentmods.dev/badge/skills/ejentum/ejentum-mcp/code/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/ejentum/ejentum-mcp/code"><img src="https://agentmods.dev/badge/skills/ejentum/ejentum-mcp/code.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 | $0.00192 | $0.00543 |
| Opus 5.5 | $0.00077 | $0.00217 |
| Sonnet 5.5 | $0.00038 | $0.00109 |
| Haiku 4.5 | $0.00019 | $0.00054 |
Grade A, and why
code 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
Code Harness
When this skill triggers, call the code tool from the ejentum MCP server. Pass a 1-2 sentence framing of WHAT you are coding or reviewing as the query argument. Include the failure risk to avoid where possible.
Good query: review a Python refactor that converts raise UserNotFound to silent default return; tests still pass
Bad query: look at this code
The tool returns a structured scaffold containing:
[CODE FAILURE]: engineering failure pattern to avoid[ENGINEERING PROCEDURE]: steps to follow[REASONING TOPOLOGY]: decision flow[CORRECT PATTERN]: shape correct code should take[VERIFICATION]: self-checkAmplify:andSuppress:signals
Absorb internally. Do NOT echo bracket labels in the user-facing reply. Apply the scaffold's failure-pattern check against your draft before responding; if your code exhibits the named failure, rewrite.
If the API is unreachable, proceed with native engineering. The scaffold enhances; it is not a hard dependency.
Latency cost: ~1 second. Benefit: catches the kinds of behavioral changes and silent contract violations that look plausible but break under real conditions.
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 · 33 lines · 192 tokens per session scan A d69860c71ff6
code is a skill published in the GitHub repository ejentum/ejentum-mcp (16 stars, last pushed 4mo ago), licensed MIT. It adds 192 tokens to every session and 543 once invoked, about $0.0008 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 2026-08-30.
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Audit an existing codebase, then autonomously implement the fixes it judges worth making against your next goal — composing deep-dive → triage → prompt-pack → build-loop to turn "here's what's wrong" into verified, receipted local commits + an honest ledger. ALWAYS invoke when the user says any of "audit this repo and…
backpropagation
Trace runtime bugs back to spec gaps — identify missing acceptance criteria, update specs, generate regression tests, and detect patterns.
reviewing
Claude-on-Claude code review protocol — reviews implementation against spec requirements and code quality standards.