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 qingye-lab/rootloom --skill project-guidancegit clone --depth 1 https://github.com/qingye-lab/rootloomWrote 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/qingye-lab/rootloom/project-guidance)<a href="https://agentmods.dev/skills/qingye-lab/rootloom/project-guidance"><img src="https://agentmods.dev/badge/skills/qingye-lab/rootloom/project-guidance/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/qingye-lab/rootloom/project-guidance"><img src="https://agentmods.dev/badge/skills/qingye-lab/rootloom/project-guidance.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00029 | $0.00696 |
| Opus 5.5 | $0.00012 | $0.00278 |
| Sonnet 5.5 | $0.00006 | $0.00139 |
| Haiku 4.5 | $0.00003 | $0.00070 |
Grade A, and why
project-guidance 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.
How it starts
The opening of the file, as written. The whole thing — 72 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Project guidance
A user's explicit request to create, refresh, or refine guidance authorizes that work within the stated file scope; the user need not name this Skill. Review/validation requests are read-only. Ordinary coding tasks and natural-language repository guidance do not authorize persistent edits. The SessionStart Hook is always read-only.
The existing repository-authored exception is the exact standalone marker:
<!-- rootloom:refine-once version=1 -->
It permits one refinement of that marked file only. Remove it with the successful edit; validation does not consume it. Preserve any explicit independent approval requirement.
Existing guidance
Resolve each target's real path and symlink chain before editing. Read its applicable global and root-to-target guidance and only the evidence needed for the requested change. An entry pointing outside the authorized repository is external content: propose its diff unless the user has separately authorized that real target. Preserve backups and unrelated edits; do not reset or overwrite user work to simplify refinement.
For wording-only refinement, read semantic-refinement.md and edit authorized unmarked content directly. No manifest/CI/module probe is needed. Keep model judgment outside generated managed markers. Add only durable, supported rules that change a real decision; remove redundant wording without dropping unique constraints.
Generate or refresh project facts
Resolve this Skill directory and use its deterministic probe and writer:
python3 <skill-dir>/scripts/seed_project_guidance.py probe --cwd "$PWD"
python3 <skill-dir>/scripts/seed_project_guidance.py seed --cwd "$PWD"
The writer owns only its generated project block. It must not overwrite unmarked guidance,
AGENTS.override.md, symlinked or external targets, disabled projects, or vendor/cache trees.
Respect its skip reasons. Only after an exact untrusted_project response and the user's
explicit persistence request for this repository may it retry once with --allow-untrusted;
this does not change host trust or authorize other writes.
What ships with it
3 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.
- 1mo ago Changed · -32 lines · -13 tokens per session 80a3535ecc9c
- 1mo ago First seen · 104 lines · 42 tokens per session scan A 7e5b810113cb
project-guidance is a skill published in the GitHub repository qingye-lab/rootloom (10 stars, last pushed 1mo ago), licensed MIT. It adds 29 tokens to every session and 696 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.
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Review every open PR with the code-reviewer agent and an adversarial review, apply the findings, and merge each one once CI is green. Use when asked to ship, sweep, or merge open PRs.
slop-check
LLM-judge slop & erosion scan of a diff, mirroring SlopCodeBench. Scores code on Erosion (verbosity, dead branches, redundant structure accumulated under iterative change) and Verbosity (unnecessary complexity), then reports per-category findings weighted toward code that EXTENDS existing modules. Use after…
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Use when reviewing a diff, pull request, branch, or AI-generated code for correctness, security, regression, test, performance, and maintainability risks before merge.
repo-health-check
Use when auditing a repo before changes to find the smallest safe PR, quality risks, stale docs, missing tests, ignored-file gaps, or agent setup issues. Best first skill for unfamiliar codebases.