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 agentmods add skills/danicat/skills/engineering-flownpx skills add danicat/skills --skill engineering-flowgit clone --depth 1 https://github.com/danicat/skillsWrote 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/danicat/skills/engineering-flow)<a href="https://agentmods.dev/skills/danicat/skills/engineering-flow"><img src="https://agentmods.dev/badge/skills/danicat/skills/engineering-flow.svg" alt="Measured on agentmods" 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 | $0.00076 | $0.01203 |
| Opus 5 | $0.00038 | $0.00602 |
| Sonnet 5 | $0.00015 | $0.00241 |
| Haiku 4.5 | $0.00008 | $0.00120 |
Grade A, and why
engineering-flow 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 3d 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 — 125 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Engineering Flow
Engineering standards, decision pipelines, and code hygiene rules.
Delivery Principles
Ship working software in small, verifiable increments:
- Keep changes scoped to a single logical objective.
- Avoid speculative abstractions and overengineering.
- Implement thin, vertical slices from entrypoint to persistence.
- Verify each slice with automated tests and compiler checks before proceeding.
Design Pipeline: RFCs and ADRs
Separate exploration from permanent architectural choices:
graph TD
A[Ambiguous Goal / High Uncertainty] --> B[RFC in design/rfc/]
B -->|Consensus Reached| C[ADR in design/adr/]
C --> D[Implementation Tasks]
E[Trivial / Low-Uncertainty Task] --> D
- RFCs (
design/rfc/): Use during exploration when requirements are ambiguous, trade-offs need debate, or multiple viable architectures exist. RFCs are fluid working documents. - ADRs (
design/adr/): Use to record finalized decisions. ADRs are immutable historical logs capturing context, chosen architecture, and accepted trade-offs. - Tasks: Break ADR conclusions into concrete checklist items with clear acceptance criteria.
Task Prioritization
Categorize work by technical certainty and business value:
| High Technical Certainty | Low Technical Certainty | |
|---|---|---|
| High Value | Direct execution: Implement interactively with compiler feedback and tight test loops. | Research & Spikes: Do not write production code yet. Run throwaway spikes in scratch/ or draft an RFC. |
| Low Value | Delegate: Offload to background tasks or subagents. | Defer / Discard: Drop or postpone until certainty increases or value is demonstrated. |
Research & Evidence Hierarchy
Do not guess APIs, package syntax, or model behaviors. Ground technical decisions in primary sources:
[1] Source Code (highest authority)
└── [2] Official Documentation & API Reference
└── [3] Official Release Notes & Announcements
└── [4] Industry Expert Articles (< 3 months old)
└── [5] Community Posts (< 3 months old)
└── [6] Stale Articles (> 3 months old — discard)
└── [7] Social Media (unverified — cross-check first)
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.
- 3d ago First seen · 125 lines · 76 tokens per session scan A efb5186721d6
engineering-flow is a skill published in the GitHub repository danicat/skills (16 stars, last pushed 4d ago), licensed Apache-2.0. It adds 76 tokens to every session and 1,203 once invoked, about $0.0004 per session on Opus 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.
Other skills, from other repositories
systematic-debugging
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brainstorming
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auto-perf-optimize
Run agent-driven VS Code performance or memory investigations. Use when asked to launch Code OSS, automate a VS Code scenario, run the Chat memory smoke runner, capture renderer heap snapshots, take workflow screenshots, compare run summaries, or drive a repeatable scenario before heap-snapshot analysis.
chat-perf
Run chat perf benchmarks and memory leak checks against the local dev build or any published VS Code version. Use when investigating chat rendering regressions, validating perf-sensitive changes to chat UI, or checking for memory leaks in the chat response pipeline.
chat-pet-sprite-creation
Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.
cpu-profile-analysis
Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…