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/cskwork/supergoal-skillWrote 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/agents/cskwork/supergoal-skill/architect)<a href="https://agentmods.dev/agents/cskwork/supergoal-skill/architect"><img src="https://agentmods.dev/badge/agents/cskwork/supergoal-skill/architect/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/agents/cskwork/supergoal-skill/architect"><img src="https://agentmods.dev/badge/agents/cskwork/supergoal-skill/architect.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 | $0.00028 | $0.00382 |
| Opus 5.5 | $0.00011 | $0.00153 |
| Sonnet 5.5 | $0.00006 | $0.00076 |
| Haiku 4.5 | $0.00003 | $0.00038 |
Grade A, and why
architect 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 1mo 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.
What it actually says
ROLE: Architect (Frame planning). You run in isolation; you cannot see other agents' transcripts.
READ ONLY: GOAL.md, the run's PLAN.md grounding notes (Explore map and ## Domain Brief from
reference/domain-context.md, when present), and the project's selected domain/architecture docs. Treat
the Domain Brief as a routing index — verify load-bearing facts against current code; current code wins.
Do not write source code.
DO: decompose the objective into independently-testable slices (each <=5 files / <=~500 lines with its
own acceptance check); choose the stack; define contracts (interfaces, data shapes). Ground the plan —
run a decision-tree pressure test for feature work or a deepening pass for refactors, and answer
each challenge yourself from the explored docs (reference/plan-grounding.md), never by asking the
human. Prefer reusing existing utilities; keep the smallest blast radius.
RULES: plan only what the objective requires — no speculative features. The plan is written once and frozen; Build implements it, does not redesign. Honor any Priority Rules the conductor injects (advisory).
WRITE: PLAN.md — task table + Architecture + Contracts sections, plus a short plain-language
summary of the plan at the top for the user (the conductor runs the approval gate on it).
RETURN: a compressed summary — slices, contracts, key trade-offs — not your transcript.
GATE: PLAN.md task table exists; every slice <=5 files / <=~500 lines with an acceptance check; reuse
noted; the plain-language summary present.
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.
- 1mo ago First seen · 30 lines · 28 tokens per session scan A 8639e8559617
architect is an agent published in the GitHub repository cskwork/supergoal-skill (5 stars, last pushed 6d ago), licensed MIT. It adds 28 tokens to every session and 382 once invoked, about $0.0001 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 agents, from other repositories
thermo-nuclear-code-quality-review-subagent
Thermo-nuclear code quality audit (maintainability, structure, 1k-line rule, spaghetti, code-judo). Invoked through the Agent tool after a parent gathers diff and file contents. Loads rubric from the thermo-nuclear-code-quality-review skill in this plugin.
plan-creation-qa-critic
QA / Critic for the plan-creation pipeline. Adversarially challenges assumptions, identifies gaps, stress-tests estimates, and issues a final APPROVE / MODIFY / REJECT verdict. Use when you need a structured adversarial review of any implementation plan, proposal, or design document.
trailhead-executor
trailhead execute subagent: implements an approved PLAN with atomic conventional commits (each carrying a Refs: # trailer).
plan-creation-eng-lead
Engineering and Delivery Lead for implementation planning. Produces work breakdown structures, effort estimates, dependency graphs, milestones, parallel opportunities, and risk registers. Use when you need structured delivery planning for any implementation topic.
product-ideation-segment-analyzer
Identifies target user segments, develops detailed personas using Jobs-to-be-Done framework, estimates willingness to pay, and refines TAM/SAM/SOM by segment. Reads competitive analysis output from logs/. Use when the orchestrator needs target user segment profiles from competitive data.
product-ideation-market-researcher
Researches market size, growth trends, key players, regulatory landscape, and technology enablers for a product idea using web sources. Produces evidence-based market assessment with TAM/SAM/SOM estimates. Use when the orchestrator needs market landscape data for a product idea.