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 dean0x/devflow --skill feature-knowledgegit clone --depth 1 https://github.com/dean0x/devflowWrote 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/dean0x/devflow/feature-knowledge)<a href="https://agentmods.dev/skills/dean0x/devflow/feature-knowledge"><img src="https://agentmods.dev/badge/skills/dean0x/devflow/feature-knowledge/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/dean0x/devflow/feature-knowledge"><img src="https://agentmods.dev/badge/skills/dean0x/devflow/feature-knowledge.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00013 | $0.03792 |
| Opus 5.5 | $0.00005 | $0.01517 |
| Sonnet 5.5 | $0.00003 | $0.00758 |
| Haiku 4.5 | $0.00001 | $0.00379 |
Grade A, and why
feature-knowledge 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 6d 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 — 374 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Feature Knowledge Base Creation
Iron Law
Capture the institutional knowledge that lives in developers' heads — the things obvious to them but invisible to newcomers.
A feature knowledge entry exists to save the NEXT agent from rediscovering patterns that span multiple files, modules, or layers. If it's obvious from a single file read, don't capture it.
The Four Phases
Follow these four phases in order. Do not skip ahead.
Phase 1: Scan
Map the landscape. Get a high-level understanding before going deep.
- Use
Globto discover directory structure and file organization - Identify language(s), framework(s), and major dependencies
- Locate key entry points, configuration files, and existing documentation
- Note how the code is organized — monorepo, modules, layers, etc.
Goal: Answer "Where does the code related to this area live, and how is it structured?"
Phase 2: Extract
Go deep. Read the actual code and pull out the real patterns.
- Read key files using
Read - Use
Grepto find recurring patterns across the codebase - Trace data flow: how does information move through the system?
- Identify naming conventions, structural patterns, error handling approaches
- Look for implicit rules — things the team clearly follows but never wrote down
- Pay attention to what's consistent (conventions) vs. what varies (knowledge gaps)
- Look at how similar things are implemented — find ALL instances and see what they share
For complex domains, go further:
- Map core entities and their relationships
- Identify decision points — where does business logic branch?
- Extract implicit knowledge — what's obvious to current developers but invisible to newcomers?
- Document state transitions, business rules, and edge case handling
Self-check before moving on:
- "Am I understanding the full scope of what's being requested?"
- "What connections am I seeing that weren't obvious initially?"
- "Are there patterns I'm missing because I'm too focused on one area?"
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.
- 6d ago Changed · +31 lines 07bb4fdd06f8
- 13d ago Changed · +1 lines de8cd6fa3187
- 1mo ago First seen · 342 lines · 13 tokens per session scan A 0c0c17609e81
feature-knowledge is a skill published in the GitHub repository dean0x/devflow (20 stars, last pushed today), licensed MIT. It adds 13 tokens to every session and 3,792 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 2026-08-30.
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totem-compiled-rules
Compiled architectural rules and lessons from the Totem knowledge base. Load this when reviewing code for pattern violations or architectural drift.
setup-obsidian
Turn a repo's markdown docs folder into a searchable retrieval graph — router, generated indexes, state file — and open it as an Obsidian vault.
continuity-brief
Start a session knowing what the last one did and decided. Use when the task involves: session start, what did i do last, wake up, continuity, resume, brief.
context-checkpoint
Summarise, pin what matters, drop the noise, keep going. Use when the task involves: checkpoint, compact, context, summarize, phase, long session.