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 tikalk/adlc-team-skills --skill architect-bootgit clone --depth 1 https://github.com/tikalk/adlc-team-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/tikalk/adlc-team-skills/architect-boot)<a href="https://agentmods.dev/skills/tikalk/adlc-team-skills/architect-boot"><img src="https://agentmods.dev/badge/skills/tikalk/adlc-team-skills/architect-boot/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/tikalk/adlc-team-skills/architect-boot"><img src="https://agentmods.dev/badge/skills/tikalk/adlc-team-skills/architect-boot.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 | $0.00069 | $0.01503 |
| Opus 5.5 | $0.00028 | $0.00601 |
| Sonnet 5.5 | $0.00014 | $0.00301 |
| Haiku 4.5 | $0.00007 | $0.00150 |
Grade A, and why
architect-boot 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 10d 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 — 145 lines — stays where its author put it; the contents beside it link to each section on GitHub.
architect-boot
Overview
One of the five class boots surfaced by team-boot's Class Boots
catalog. team-boot injects the always-relevant team context (constitution,
CDR index, skills registry) at session start; this skill loads the
architecture decision layer on demand — the Accepted ADR index from
project memory — and pairs it with decision capture so new architecture
choices are recorded (/architect-specify) rather than evaporating.
Reading decisions and recording decisions are one loop: existing ADRs inform the work; decisions made during the work flow back into the same record system.
When to Use
Invoke at the START of a matching task — before planning the todo list and before implementation — so the ADR context informs planning. Never defer to session end.
Invoke when:
- Starting architecture work (system design,
AD.mdgeneration, cross-subsystem refactoring, dependency changes). - The session turns architectural mid-flight: a tech-stack choice, pattern selection, or "we chose X over Y" moment emerges.
- Reviewing or validating an architecture and you need the decision backdrop.
For technology selection, also invoke tech-radar-boot — radar context
should inform the ADR.
Core Process
Step 1: Locate the ADR Index
From the current working directory (do NOT walk up parent directories):
- Primary:
docs/adlc/memory/adr/adr.md(generated/architect-implement; rows start| ADR) - Fallback:
.adlc/memory/adr/adr.md(legacy layout, pre-ADR-401)
If neither exists but docs/adlc/memory/adr/ADR-*.md or legacy
.adlc/memory/adr/ADR-*.md files do, synthesize a
lean table from each file (ID from filename; Sub-System/Decision/Status
from frontmatter or first heading). If the directory is empty or absent,
report 0 ADRs — never fabricate rows.
When no local memory index exists in the current working directory and the
directory sits inside a workspace (detected via a .gitmodules marker in an
ancestor), read the workspace root's docs/adlc/memory/ index instead
(ADR-401 dual-read order applies: docs/adlc/memory first, legacy
.adlc/memory fallback).
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.
- 10d ago Changed · +4 lines b26a2b0deb14
- 13d ago Changed · +15 lines eb9943ee120a
- 15d ago Changed · +4 lines · +12 tokens per session d3d2d3a110c2
- 16d ago Changed · +3 lines 7b8d33bf624e
- 24d ago Changed · +15 lines ee632cc2c22d
- 25d ago First seen · 104 lines · 57 tokens per session scan A 76c1b025453a
architect-boot is a skill published in the GitHub repository tikalk/adlc-team-skills (141 stars, last pushed yesterday), licensed MIT. It adds 69 tokens to every session and 1,503 once invoked, about $0.0003 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 2026-09-21.
Other skills, from other repositories
building-agents
Use when building or restructuring an LLM agent — provider adapter, tool calling, structured output, RAG, agent loop, eval gate, cost routing, tracing, MCP server — model-agnostic across OpenAI/Anthropic/Gemini/OSS so a model swap is a config change. NOT vector-store SQL alone (that is postgresdb) or service…
agent-eval
Use when measuring whether an LLM or agent system actually got better and gating merges on it: golden sets, fixing an inflated LLM-as-judge, scoring RAG (faithfulness, contextual recall) or agent trajectories (tool correctness, completion), or picking an eval framework. NOT building the agent loop, tools or RAG…
decision-records
Use when a hard-to-reverse choice (database, framework, vendor, auth model) must be frozen as an immutable numbered ADR — the context that forced it, the options weighed, the decision, consequences both ways — or superseded without erasing history. NOT a meeting recap (that is meeting-notes), NOT standing project…
prompt-engineering
Use when one prompt must give the same right answer across reruns, models, and pasted-in hostile input: forcing a fixed schema, picking the few-shot set, ordering the prompt blocks, or the inline cases you run while tuning. NOT the agent loop, tools, or retrieval (that is building-agents), NOT a standing CI eval…
respect-the-oracle
Use when working against a test suite, spec, or graded harness you do not own - especially inside an automated loop scored on how many tests pass - and tempted to change the tests, weaken assertions, hardcode expected outputs, special-case inputs, or overfit the visible examples to turn things green.
refocusing
Use when a long-running agent may have lost the product outcome, when context was compacted, when work has crossed a major boundary, or when a fresh path-based trace is needed before the next consequential action. Not for fresh-session orientation, waking an idle seat, or checkpoints.