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 team-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/team-boot)<a href="https://agentmods.dev/skills/tikalk/adlc-team-skills/team-boot"><img src="https://agentmods.dev/badge/skills/tikalk/adlc-team-skills/team-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/team-boot"><img src="https://agentmods.dev/badge/skills/tikalk/adlc-team-skills/team-boot.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 | $0.00074 | $0.02325 |
| Opus 5.5 | $0.00030 | $0.00930 |
| Sonnet 5.5 | $0.00015 | $0.00465 |
| Haiku 4.5 | $0.00007 | $0.00232 |
Grade A, and why
team-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 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.
How it starts
The opening of the file, as written. The whole thing — 164 lines — stays where its author put it; the contents beside it link to each section on GitHub.
team-boot
Overview
Assembles team AI directives context and injects it into the system prompt at session start. The CDR index lists all available team context modules with descriptors — read full module files on demand when a task matches.
team-boot injects only the always-relevant layer: constitution titles, the compact CDR index, the Class Boots catalog, the skills registry, and MCP servers. The four record classes (ADR/PDR/ChDR/CDR deep-dive) plus technology selection load on demand through the class boots below — each pairs its index injection with decision capture.
Discovery is native: the injected CDR index is matched by the LLM
against the current task on its own, per prompt, with no skill invocation.
/team-discover is a separate, user-invoked command for explicit
structured re-discovery (e.g., starting a complex feature) and is not part
of the bootstrap loop.
Fast path: if the team AI directives context (constitution, CDR index, Class Boots catalog) is already in your system prompt or first user message, the event hook already ran — do nothing.
Class Boots
| Boot | Injects | Invoke When | Capture Via |
|---|---|---|---|
architect-boot |
ADR index (docs/adlc/memory/adr/ + legacy .adlc/memory/adr/) |
architecture work; tech-stack/pattern choice | direct write to .adlc/drafts/adr/ |
product-boot |
PDR index (docs/adlc/memory/pdr/ + legacy .adlc/memory/pdr/) |
product/feature scope, personas, monetization | direct write to .adlc/drafts/pdr/ |
change-boot |
ChDR index (docs/adlc/memory/chdr.md + legacy .adlc/memory/chdr.md) |
change-history rationale, reverts, issue-linked commits | direct write to .adlc/drafts/chdr/ |
team-levelup |
CDR module bodies (team-ai-directives) | session end; CDR descriptor match; reusable team pattern | direct write to adlc branch drafts/cdr/ |
tech-radar-boot |
Tikal Tech Radar context | choosing/evaluating technology | radar context + direct write to .adlc/drafts/adr/ |
What ships with it
4 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- yesterday Changed 28aec0c058ea
- 16d ago Changed · +8 lines 751709fbee21
- 23d ago Changed · +23 lines c39904227230
- 25d ago Changed · +2 lines f2e2af9dfac3
- 26d ago Changed · +4 lines ec397a40af42
- 1mo ago Changed · +36 lines 8f5037ae4d61
- 1mo ago Changed · +44 lines · +13 tokens per session be1011e160f3
- 1mo ago Changed · +20 tokens per session 5adc7b9859f7
- 1mo ago First seen · 47 lines · 41 tokens per session scan A f21834629674
team-boot is a skill published in the GitHub repository tikalk/adlc-team-skills (143 stars, last pushed today), licensed MIT. It adds 74 tokens to every session and 2,325 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 2026-08-30.
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.