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 bearded-illirian/trailmark --skill arch-firstgit clone --depth 1 https://github.com/bearded-illirian/trailmarkWrote 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/bearded-illirian/trailmark/arch-first)<a href="https://agentmods.dev/skills/bearded-illirian/trailmark/arch-first"><img src="https://agentmods.dev/badge/skills/bearded-illirian/trailmark/arch-first.svg" alt="Measured on agentmods" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Agent Snooping · line 206 Skill enumerates or reads other installed skills. Access to other skills' SKILL.md files or the skills directory reveals prompt instructions, capabilities, and secrets that should be invisible to peer skills.Fix: Remove all code or instructions that list or read other skills' files or directories. Skills should operate independently; cross-skill access is a privilege escalation.
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 | $0.00091 | $0.03616 |
| Opus 5 | $0.00046 | $0.01808 |
| Sonnet 5 | $0.00018 | $0.00723 |
| Haiku 4.5 | $0.00009 | $0.00362 |
Grade A, and why
arch-first scanned grade A with 1 finding 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
**Mandatory final block — UI/AI-first audit.** Last block of the plan = UI/AI-first audit via `/ui-ai-first`. Surfaces operations accessible only via code/curl/SQL, forms roadmap. Without this report the task isn't consi How it starts
The opening of the file, as written. The whole thing — 395 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Arch-First Protocol
A meta-skill for executing architectural tasks — too big for a fast-track, too small for a full feature-execution. Use when:
- The task decomposes into 5+ blocks with dependencies
- Affects several layers (DB + code + skills + prompts + configs)
- Requires decisions on multiple forks (not just execution)
- May spawn a follow-up epic after closure
- Architectural cleanliness over distance is principally important
Input
A 5+ block feature task (typically routed from idea-first); optionally a Mode Brief path.
Output
research-doc.md with baseline assessment; block decomposition (5-15+ atomic blocks) inserted into task_blocks; per-block reports as blocks close.
Hands off to
Per-block flow-first cycle for each open block, then ship-first at task-level closure.
Principles
1. Decomposition by BLOCKS, not tasks
A large task = numbered blocks with an explicit goal each. Blocks live inside one task, in one task.md. An epic appears when a block is deferred or extracted.
Execution order != numbering order — reflects real dependencies.
2. Research BEFORE library-first
If the task has an unfamiliar component (new skill, new DB, unstudied table) — a separate research phase before planning.
Research outputs a comparison table "as is vs should be" with criticality (HIGH/MED/LOW) per item.
Without research → library-first builds on guesses → wrong scope estimates → missed deadlines.
3. Explicit pinning of architectural decisions
After each fork — pin the decision with rationale in task.md or a dedicated decision-NN.md.
Structure:
### Decision N — Brief statement
We choose **X** (not Y, not Z).
**Rationale:**
1. Specific reason 1 (with example)
2. Specific reason 2
**Alternatives:**
- Y — rejected (why)
- Z — rejected (why)
**What changes:** [specific file-level deltas]
Without explicit pinning → two weeks later the developer re-opens the discussion or makes the wrong choice.
What ships with it
1 file 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.
- 4d ago Changed e3a7c92b840f
- 8d ago First seen · 395 lines · 91 tokens per session scan A f03787a14bc0
arch-first is a skill published in the GitHub repository bearded-illirian/trailmark (19 stars, last pushed 6d ago), licensed MIT. It adds 91 tokens to every session and 3,616 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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