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 factory-initgit 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/factory-init)<a href="https://agentmods.dev/skills/tikalk/adlc-team-skills/factory-init"><img src="https://agentmods.dev/badge/skills/tikalk/adlc-team-skills/factory-init/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/factory-init"><img src="https://agentmods.dev/badge/skills/tikalk/adlc-team-skills/factory-init.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 | $0.00038 | $0.03397 |
| Opus 5.5 | $0.00015 | $0.01359 |
| Sonnet 5.5 | $0.00008 | $0.00679 |
| Haiku 4.5 | $0.00004 | $0.00340 |
Grade A, and why
factory-init 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 15d 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 — 198 lines — stays where its author put it; the contents beside it link to each section on GitHub.
factory-init
What this skill does
factory-init orchestrates the unified brownfield bootstrap: one command that reverse-engineers an existing codebase onto all three project-memory layers and measures how well they align with the code.
It coordinates the leaf lifecycle skills directly — product-init, product-clarify, product-implement, product-analyze, architect-init, architect-clarify, architect-implement, architect-analyze, change-init, change-clarify, change-publish — never the peer orchestrators factory-product / factory-architect / factory-learn.
Its own final step, the sweep, is the only cross-layer analysis in the factory: it builds the coverage matrix (.adlc/coverage/coverage.md) — a feature-area pivot plus the four traceability relations PDR↔ADR, PDR↔code, ADR↔code, ChDR↔code — and diffs it against the previous sweep to detect drift.
It operates as a Kind-A DAG orchestrator in alignment with the shared executor engine contract in factory-mission/references/executor.md.
Scope boundary: project-local memory only (PDR, ADR, ChDR + code). Team context directives (CDR / team-init) are NOT bootstrapped here — they publish to the external team-ai-directives repo and are owned by factory-learn. The sweep's final output includes a prose recommendation to run factory-learn / /team-init for reusable patterns spotted during bootstrap.
When to use
- You adopt ADLC on an existing repo (code exists, no/partial PDRs, ADRs, ChDRs) and want the full bootstrap in one run.
- You want a recurring cross-layer alignment audit (
--refresh): which decisions are undocumented, which code is undecreed, where product and architecture disagree.
When NOT to use:
- Greenfield / empty project → run
factory-productandfactory-architect(specify routes) instead. - Single-layer work →
factory-product/factory-architect/factory-learndirectly. - CDR / team-directives contribution →
factory-learn. - Feature implementation →
factory-mission.
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.
- 15d ago Changed a407d17aa193
- 18d ago Changed 8730f41bd411
- 24d ago Changed d0c00cee4178
- 25d ago First seen · 198 lines · 38 tokens per session scan A 99155390f9cf
factory-init is a skill published in the GitHub repository tikalk/adlc-team-skills (141 stars, last pushed yesterday), licensed MIT. It adds 38 tokens to every session and 3,397 once invoked, about $0.0002 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.
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