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 Mark393295827/graph-engineering-architectures --skill agentic-engineeringgit clone --depth 1 https://github.com/Mark393295827/graph-engineering-architecturesWrote 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/mark393295827/graph-engineering-architectures/agentic-engineering)<a href="https://agentmods.dev/skills/mark393295827/graph-engineering-architectures/agentic-engineering"><img src="https://agentmods.dev/badge/skills/mark393295827/graph-engineering-architectures/agentic-engineering/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/mark393295827/graph-engineering-architectures/agentic-engineering"><img src="https://agentmods.dev/badge/skills/mark393295827/graph-engineering-architectures/agentic-engineering.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 | $0.00033 | $0.01281 |
| Opus 5.5 | $0.00013 | $0.00512 |
| Sonnet 5.5 | $0.00007 | $0.00256 |
| Haiku 4.5 | $0.00003 | $0.00128 |
Grade A, and why
agentic-engineering 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 — 115 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Agentic Engineering
<skill_contract> An engineering objective, inspectable repository or workflow, acceptance criteria, permissions, risk, and state location. The smallest sufficient model-native process with bounded autonomy, evals, recovery, and verified write-back. Fresh task and adoption evidence support the observable end state without crossing authority boundaries. <non_goals>Agent complexity for its own sake, premature multi-agent topology, or unverified knowledge promotion.</non_goals>
An agent is a stateful engineering process, not a prompt. Its quality ceiling is the combination of objective, context, tools, taste/evaluation, permissions, recovery, and feedback latency.
Usage Template
Provide: engineering objective, repository/workflow, users, acceptance criteria, constraints, permissions, risk, current evidence, and durable state location.
Workflow
- Inspect repository guidance, code, tests, state, and current failure before proposing architecture.
- Define the observable end state, non-goals, owner, budget, and review bandwidth.
- Run the adoption gate: use an agent only when ambiguity/adaptation outweigh orchestration, verification, and maintenance cost. Prefer deterministic code for stable transformations.
<unknowns_gate>
Map unknowns into: known, probeable from tools/files, testable by prototype, and externally blocked. Probe boundary/interface unknowns before implementation. Return NEEDS_INPUT only when a missing business decision, permission, or irreversible tradeoff cannot be discovered locally; otherwise label assumptions and test them.
</unknowns_gate>
- Write the macro action:
trigger -> objective -> inputs -> constraints -> artifact -> verifier -> state -> stop/recovery. - Define quality with domain-specific examples, anti-examples, guardrails, and cheap checks; generic “good quality” is invalid.
- Decompose into the fewest independently verifiable units with one owner each.
- Select the lowest sufficient topology: one-shot for one bounded action,
loop-engineeringfor temporal correction,graph-engineeringfor explicit dependency width and joins, andagent-teams-commandonly when distinct worker processes and integration ownership add value. - Route by capability (reasoning, tool use, latency, context, modality, cost) and runtime policy; keep vendor/model names out of durable contracts.
- Establish harness controls: least privilege, tool schemas, timeouts, observability, checkpoints, idempotency, and rollback.
- Run a thin loop: understand -> plan -> smallest change -> targeted test -> inspect diff/state -> broader check.
- Use independent evaluation or adversarial review for consequential logic, interfaces, and claims.
- Remove temporary scaffolding, duplicate abstractions, and context that no longer changes decisions.
- Write back only reusable, verified deltas. Promotion into skills/SOPs requires repeated support or local verification plus a cheap objective check.
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 · 115 lines · 33 tokens per session scan A 209c7b327f42
agentic-engineering is a skill published in the GitHub repository Mark393295827/graph-engineering-architectures (3 stars, last pushed 2mo ago), licensed MIT. It adds 33 tokens to every session and 1,281 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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