engineer

A command for implementing written specifications with support from connected tools and a structured process for handling errors.

In plain words
What is it for?
Use it to build features from specifications and investigate implementation errors.
Why use it?
It gives implementation work a repeatable process and provides a defined way to respond when something goes wrong.

Command

Install

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.

agentmods
npx agentmods add commands/pwnk77/agentic-workflows/engineer
Clone the repo
git clone --depth 1 https://github.com/pwnk77/agentic-workflows
Per session 0 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 3,868 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin unknown No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5 $0.00000 $0.03868
Opus 5 $0.00000 $0.01934
Sonnet 5 $0.00000 $0.00774
Haiku 4.5 $0.00000 $0.00387

Measured yesterday against content hash 8755a6a1c5c4, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

engineer 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 yesterday.

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.

core-workflows/claude-code/commands/engineer.md · 360 lines

The source is not reproduced here

No licence file

A repository with no LICENSE is all rights reserved by default, so the body is not copied here. The metadata, the measurements and the link are.

Read it on GitHub

Changes

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.

  1. yesterday First seen · 360 lines · 0 tokens per session scan A 8755a6a1c5c4

Subscribe to this mod's changes

engineer is a command published in the GitHub repository pwnk77/agentic-workflows (59 stars, last pushed 3mo ago), with no licence file. It costs nothing until one of its globs matches a file; then it loads 3,868 tokens. 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.

Related

Other commands, from other repositories

aws-well-architected-framework-review

Perform a full AWS Well-Architected Framework review of the user's workload by analyzing code, IaC, and configurations to produce evidence-backed findings with prioritized remediation.

aws-samples/sample-well-architected-skills-and-steering · 0 tokens

wa-guardrails

Generate preventive Well-Architected guardrails — AWS Config rules, Service Control Policies, permission boundaries, CloudWatch alarms, and IaC policy checks (CDK Aspects, cfn-guard, OPA/Sentinel) — so a workload stays aligned with Well-Architected best practices over time instead of being assessed once.

aws-samples/sample-well-architected-skills-and-steering · 0 tokens

wa-builder

"Learn then Build" — help the user understand AWS Well-Architected best practices for their specific workload, then produce actionable visual artifacts (architecture diagrams with WA annotations, decision trees, improvement roadmaps) they can commit and use.

aws-samples/sample-well-architected-skills-and-steering · 0 tokens

cost-optimization-review

Run a pillar-scoped Well-Architected review focused on the Cost Optimization pillar.

aws-samples/sample-well-architected-skills-and-steering · 0 tokens

operational-excellence

Run a pillar-scoped Well-Architected review focused on the Operational Excellence pillar.

aws-samples/sample-well-architected-skills-and-steering · 0 tokens

performance-efficiency

Run a pillar-scoped Well-Architected review focused on the Performance Efficiency pillar.

aws-samples/sample-well-architected-skills-and-steering · 0 tokens