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.
git clone --depth 1 https://github.com/TechNickAI/ai-coding-configWrote 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/commands/technickai/ai-coding-config/knowledge)<a href="https://agentmods.dev/commands/technickai/ai-coding-config/knowledge"><img src="https://agentmods.dev/badge/commands/technickai/ai-coding-config/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/commands/technickai/ai-coding-config/knowledge"><img src="https://agentmods.dev/badge/commands/technickai/ai-coding-config/knowledge.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 | $0.00020 | $0.02077 |
| Opus 5.5 | $0.00008 | $0.00831 |
| Sonnet 5.5 | $0.00004 | $0.00415 |
| Haiku 4.5 | $0.00002 | $0.00208 |
Grade A, and why
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 25d 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 — 211 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Product Knowledge
When understanding is true, every decision - human or AI - is grounded in reality. When understanding is stale or wrong, we build the wrong things.
This is the first thing you do in a repo, and how you maintain the product over time. The same process that creates also maintains.
You're not documenting a product. You're maintaining the product kernel - the accumulated understanding that enables everything else.
When a signal arrives, you ask: Does this change what we should build? Does this change how we think about our users? Does this reveal something we didn't know? Should we act?
Synthesis: Connect dots across signals. Ten user complaints about different things might point to one underlying problem. A competitor move plus a usage pattern plus a bug report might reveal a strategic opportunity.
Skepticism: Question every signal. Is this person representative of our users? Is this data reliable? Is this competitor announcement real or vaporware? One angry user is an anecdote. Twenty is a pattern.
Prioritization: Patterns matter more than anecdotes. Feedback from target users matters more than non-users. Signals that challenge core assumptions deserve more attention than signals that confirm what we know.
Judgment: Some signals just update understanding. Some demand action. Some require human decision. You determine which is which.
User feedback arrives: "I can't figure out how to export my data." Think: Is this person in our target audience? Check personas. Is export something we should offer? Check vision and boundaries. Have we heard this before? Look for patterns. What does this tell us about our UX? Should we build this, or is it outside scope? Update relevant knowledge. If action warranted, identify what kind.
Competitor news arrives: "Cursor just shipped multi-file editing." Think: What exactly did they ship? Understand before reacting. Does this affect our positioning? Check differentiation. Do our users need this? Check personas. How urgent is response? What can we learn from their approach? Update their file. Note implications.
Bug report arrives: "Login fails on Safari." Think: How many affected? Pattern or isolated? What does this reveal about our testing or architecture? Update the component file with the learning. If this changes how we think about browser support, update boundaries.
YouTube video or article: "New approach to AI-first development." Think: What's the core insight? Is source credible? Relevant to what we're building? Does it change how we should think about architecture? If valuable, capture in relevant knowledge file.
Analytics signal: "Only 5% complete onboarding." Think: Does this match assumptions? If not, what's wrong - assumptions or funnel? What are users actually doing? Update personas with behavioral insight. Identify if this demands product changes.
The pattern: Understand the signal. Compare to existing knowledge. Evaluate credibility and relevance. Decide what to update and whether action is needed.
Update knowledge only: The signal adds understanding but doesn't demand immediate action. Capture the insight in the relevant file. Most signals fall here.
Flag for human decision: The signal suggests strategic changes, contradicts established vision, or involves trade-offs you can't evaluate. Surface it clearly and ask.
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.
- 25d ago First seen · 211 lines · 20 tokens per session scan A 61fe4eaf9d98
knowledge is a command published in the GitHub repository TechNickAI/ai-coding-config (24 stars, last pushed 26d ago), licensed MIT. It adds 20 tokens to every session and 2,077 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 2026-09-22.
Other commands, from other repositories
set-tracker
Set the issue tracker for the current project.
specmanager-build
Build one phase of a SpecManager feature's plan via the builder subagent. Stops at the phase boundary; never advances.
execute
Execute the next available tracked task with TDD, pre-commit validation, PR creation, AI code review, and issue tracker bridge sync.
planning-triage
Quick-score an enhancement, bug, or feedback item using the impact-first triage framework. Creates an issue if approved.
standup
Show a daily standup summary with completed, in-progress, and blocked tasks across all active epics.
bucle
Lanzar bucle autónomo para ejecutar historias del PRD.