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 gohypergiant/agent-skills --skill accelint-onboard-openspecgit clone --depth 1 https://github.com/gohypergiant/agent-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/gohypergiant/agent-skills/accelint-onboard-openspec)<a href="https://agentmods.dev/skills/gohypergiant/agent-skills/accelint-onboard-openspec"><img src="https://agentmods.dev/badge/skills/gohypergiant/agent-skills/accelint-onboard-openspec/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/gohypergiant/agent-skills/accelint-onboard-openspec"><img src="https://agentmods.dev/badge/skills/gohypergiant/agent-skills/accelint-onboard-openspec.svg" alt="Reviewed on agentmods" width="80" 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 Prompt Injection · line 3 Subtle instructions detected that may alter agent decision-making or introduce hidden biases.Fix: Review content for implicit steering or bias. Ensure instructions are explicit and align with the skill's stated purpose.
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 | $0.00124 | $0.07609 |
| Opus 5 | $0.00062 | $0.03805 |
| Sonnet 5 | $0.00025 | $0.01522 |
| Haiku 4.5 | $0.00012 | $0.00761 |
Grade A, and why
accelint-onboard-openspec 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 12d 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 — 708 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Onboard OpenSpec
Guide the user through a conversational interview to produce a complete,
project-specific openspec/config.yaml configured for the QRSPI methodology.
NEVER Do When Onboarding OpenSpec
- NEVER run codebase inference serially when subagents are available — Phase 3 spawns parallel subagents for different discovery domains. Serial scanning wastes time on codebases with many config files spread across directories. Spawn all 4 discovery agents simultaneously.
Companion Skill
This skill produces the project DNA layer of the agent instruction stack:
structural facts about what the project is. It is the companion to the
accelint-onboard-agents skill, which produces the behavior layer (AGENTS.md /
CLAUDE.md): how the agent acts, communicates, and makes decisions.
If during this interview the user volunteers behavioral content (commit
conventions, workflow steps, decision heuristics, tool preferences), acknowledge
it and redirect: "That's behavioral — it belongs in AGENTS.md. I'll note it
here for reference, but the accelint-onboard-agents skill is the right place to
capture it." Do not write behavioral content into config.yaml.
AGENTS.md / CLAUDE.md → accelint-onboard-agents skill → HOW the agent behaves
openspec/config.yaml → this skill → WHAT the project is
Mental Model
The config has two jobs:
context:— Objective facts about the codebase injected into every AI artifact. Think of it as the "DNA" that makes AI suggestions feel native to the project. Facts only, no opinions.rules:— Per-artifact checkpoints (proposal / design / tasks / spec) that encode the team's quality bar.
Phases
Phase 0 — File State Detection
Before any interview question is asked, check whether openspec/config.yaml
exists and assess its state. Never silently pick a mode — always announce the
detected mode to the user and confirm before proceeding.
Step 1 — Check for Related Documents
What ships with it
2 files 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.
- 12d ago First seen · 708 lines · 124 tokens per session scan A a407fbde4ab6
accelint-onboard-openspec is a skill published in the GitHub repository gohypergiant/agent-skills (24 stars, last pushed today), licensed Apache-2.0. It adds 124 tokens to every session and 7,609 once invoked, about $0.0006 per session on Opus 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 2026-08-30.
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