Borrowing it
Nothing to install: this file belongs to drujensen/aiagent. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/drujensen/aiagent/main/.claude/skills/adlc/SKILL.mdgit clone --depth 1 https://github.com/drujensen/aiagentWrote 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/drujensen/aiagent/adlc)<a href="https://agentmods.dev/skills/drujensen/aiagent/adlc"><img src="https://agentmods.dev/badge/skills/drujensen/aiagent/adlc/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/drujensen/aiagent/adlc"><img src="https://agentmods.dev/badge/skills/drujensen/aiagent/adlc.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 | $0.00061 | $0.01562 |
| Opus 5.5 | $0.00024 | $0.00625 |
| Sonnet 5.5 | $0.00012 | $0.00312 |
| Haiku 4.5 | $0.00006 | $0.00156 |
Grade A, and why
adlc 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 4d 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 — 208 lines — stays where its author put it; the contents beside it link to each section on GitHub.
ADLC Pipeline
Run the full development pipeline for:
Stage 1: Pre-Coding (no code is written until the plan is approved)
Step 1: Story Refinement (interactive)
Use the Agent tool with subagent_type: "story-refiner" to:
- Explore the relevant aiagent codebase
- Generate a focused batch of 3–6 clarifying questions
- Present the questions to the user and collect answers
- Repeat until the story is complete (all unknowns resolved)
Capture and store the full story-refiner output (both Refined Story and Relevant Codebase Context sections). This is passed verbatim to every subsequent agent.
When complete, ask: "Story looks complete — ready to design?"
Step 2: Design (review loop)
Use the Agent tool with subagent_type: "architect" to design the solution. Pass it:
- The full story-refiner output (Refined Story + Relevant Codebase Context)
The architect defaults to maintaining existing DDD patterns. Deviations are explicitly justified.
Then use the Agent tool with subagent_type: "architect-reviewer" to review the design. Pass the architect's full output.
Iterate up to 3 times until score >= 8/10 (APPROVED) or escalate to user.
Capture and store the approved design document — passed verbatim to the planner.
Step 3: Implementation Plan (review loop + human checkpoint)
Use the Agent tool with subagent_type: "planner" to produce an implementation plan. Pass it:
- The full story-refiner output
- The approved design document
The planner produces a Reuse Inventory and numbered implementation steps.
Then use the Agent tool with subagent_type: "planner-reviewer" to verify the plan.
Iterate up to 3 times until score >= 8/10.
PAUSE. Present the complete plan to the user (Reuse Inventory + all numbered steps).
Ask explicitly: "Does this plan look correct? Type 'proceed' to begin coding, or describe any changes needed."
Do NOT advance to Stage 2 until the user types 'proceed' or equivalent confirmation.
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.
- 4d ago First seen · 208 lines · 61 tokens per session scan A eaa23a371b00
adlc is a skill published in the GitHub repository drujensen/aiagent (5 stars, last pushed 2mo ago), licensed MIT. It adds 61 tokens to every session and 1,562 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 2026-10-03.
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