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/The-Artificer-of-Ciphers-LLC/skills-from-the-artificerWrote 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/the-artificer-of-ciphers-llc/skills-from-the-artificer/feature-builder)<a href="https://agentmods.dev/commands/the-artificer-of-ciphers-llc/skills-from-the-artificer/feature-builder"><img src="https://agentmods.dev/badge/commands/the-artificer-of-ciphers-llc/skills-from-the-artificer/feature-builder/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/the-artificer-of-ciphers-llc/skills-from-the-artificer/feature-builder"><img src="https://agentmods.dev/badge/commands/the-artificer-of-ciphers-llc/skills-from-the-artificer/feature-builder.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 | $0.00113 | $0.30985 |
| Opus 5.5 | $0.00045 | $0.12394 |
| Sonnet 5.5 | $0.00023 | $0.06197 |
| Haiku 4.5 | $0.00011 | $0.03098 |
Grade A, and why
feature-builder 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 13d 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 — 1,579 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Engineering Workflow Directive: Feature Implementation & Integrations
<runtime_parameters>
Tune before running. Parse from $ARGUMENTS; the defaults below apply when a flag is absent.
| Parameter | Flag | Default | Meaning |
|---|---|---|---|
BASE_BRANCH |
--base <branch> |
next |
Upstream integration branch every PR targets (rebase base). |
EPIC_MODE |
--epic auto|on|off |
auto |
auto detects an epic from the issue (Step 2); off forces the single-PR path; on forces decomposition. |
AUTONOMOUS |
--autonomous |
false |
When false, every outward write (branch push, PR open, PR merge, label change, sub-issue creation, posting review findings) is surfaced for confirmation before executing. Set true only for fully unattended runs. |
MAX_PARALLEL_PRS |
--max-parallel-prs N |
1 |
How many phase branches may be in flight at once. 1 = strictly stacked/sequential; >1 = parallel worktrees coordinated by Fleet (Step 2.6). |
Also: --issue N (the epic or feature issue — required), --repo owner/repo (default:
gh repo view --json nameWithOwner -q .nameWithOwner), --phase N (resume a partially-built
epic at one deliverable), --plan-only (run Steps 0–2, emit the deployment plan, write nothing).
Arguments: $ARGUMENTS
</runtime_parameters>
<follow_ups_and_questions>
💬 FOLLOW-UPS ARE NEVER IGNORED, AND QUESTIONS ARE ASKED, NOT PRINTED
1. Read the whole conversation before acting on any PR or issue. The default gh pr view / gh issue view output is not enough. For every PR or issue you touch, fetch ALL of:
- issue/PR comments and review summaries:
gh pr view $N --json comments,reviews(issues:gh issue view $N --json comments); - inline review comments, paginated:
gh api --paginate repos/{owner}/{repo}/pulls/$N/comments; - review threads with resolution state: GraphQL
reviewThreads { isResolved isOutdated comments { author body createdAt } }.
2. Surface every follow-up. A follow-up is any comment, review, or thread reply, from anyone other than you, that is a question, a request ("can you re-review", "please rebase", "why was X changed"), a disagreement with a finding, a reply on a thread you opened, or new information after your last comment/review. Anything unresolved or newer than your last action counts. "The last comment was mine" is NOT a reason to skip: check for follow-ups first. A thread you opened that the author answered is a follow-up, not silence.
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.
- 13d ago Changed · +18 lines · +11 tokens per session c6df3409183d
- 27d ago Changed · +16 lines e1f8e1db6ef7
- 1mo ago Changed · +71 lines f116eb877a6f
- 1mo ago First seen · 1,474 lines · 102 tokens per session scan A 74b7c8688e1b
feature-builder is a command published in the GitHub repository The-Artificer-of-Ciphers-LLC/skills-from-the-artificer (6 stars, last pushed yesterday), licensed MIT. It adds 113 tokens to every session and 30,985 once invoked, about $0.0005 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 2026-08-31.
Other commands, from other repositories
design-review
Workflow recipe — review a design end-to-end, ending in measured numbers rather than adjectives, by chaining 4 skills.
firm
Convene your standing AI staff — memos on every beat, a board session without you, minutes with dissent preserved.
setup-pm-skills
Onboard a new user — find out what they do, recommend the right bundles & top skills, and set up a project CONTEXT.md so every skill is tailored to them.
promote
The promote loop — scan your transcripts for what you keep asking, pick a pattern, draft the skill, test its triggers, publish the release package.
ship-an-mcp-server
Workflow recipe — make your product agent-usable by chaining 4 skills, spec to pricing.
rescue-an-account
Workflow recipe — diagnose an at-risk customer and build the full save play through to renewal by chaining 4 skills.