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 agentmods add skills/agent-rig/rig/rig-debugnpx skills add agent-rig/rig --skill rig-debuggit clone --depth 1 https://github.com/agent-rig/rigWhat 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 | $0.00082 | $0.01160 |
| Opus 5 | $0.00041 | $0.00580 |
| Sonnet 5 | $0.00016 | $0.00232 |
| Haiku 4.5 | $0.00008 | $0.00116 |
Grade A, and why
rig-debug 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 yesterday.
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 — 108 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Debug
Drive a bug to root cause and a single minimal fix using the four-phase methodology.
Configuration
Reads .rig/config.json:
test.command— how to run the test suite when collecting Phase 1 evidence (default:npm test).agents.debugger— the project's name for the canonicaldebuggerrole (default:debugger).agents.architect— the project's name for the canonicalarchitectrole, used only when escalating (default:architect).style.guideFile— the writing style for the report you hand back (default:.claude/STYLE.md).
If the file is absent, use the defaults above and note you're running unconfigured.
The four-phase methodology
The debugger agent runs this loop; the skill drives it and refuses to
let it skip ahead. Each phase has a deliverable that gates the next:
- Phase 1 — Root cause. Gather evidence: reproduce the failure, read
the failing code path, capture the exact error/stack/trace,
git diffthe suspect window. Deliverable: a stated root cause backed by observed evidence — not a guess. - Phase 2 — Pattern analysis. Ask whether this is an instance of a class. Does the same bug shape exist elsewhere in the tree? Deliverable: the blast radius (this one site, or N sites).
- Phase 3 — Hypothesis. State the specific change that should fix the root cause and why, plus how you'll confirm it. Deliverable: one falsifiable hypothesis.
- Phase 4 — Minimal fix. Apply the smallest change that addresses the root cause, add/extend a test that fails before and passes after. Deliverable: root cause + fix + test.
Arguments
$ARGUMENTS is a one-line description of what's broken. Examples:
test suite fails with a database connection-closed errorthe workspace-commit step times out on a fresh worktreePR #519 deprovision workflow leaves orphaned resources behind
If $ARGUMENTS is empty, ask the user for the symptom in one
sentence. Don't start the loop on a vague brief.
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.
- yesterday First seen · 108 lines · 82 tokens per session scan A 29140dcfba9b
rig-debug is a skill published in the GitHub repository agent-rig/rig (2 stars, last pushed 14d ago), licensed MIT. It adds 82 tokens to every session and 1,160 once invoked, about $0.0004 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 2026-08-31.
Other skills, from other repositories
audit-onboarding-proposal
Independently audit a brownfield onboarding transcript, operational map, or exact proposed documentation patch before application. Use when a fresh reviewer must verify an $onboard-repository first pass, distinguish environment-caused Unknowns from reasoning defects, score its safety and evidence gates, or run a…
engineering-wisdom
Provide an explicitly requested, repository-grounded engineering review using contextual heuristics for code clarity, SOLID and design, testing, refactoring, architecture, and professional practice. Use only when the user invokes $engineering-wisdom or explicitly asks for this installed engineering-wisdom pack; do not…
red-team-adversarial
Adversarial security and resilience analysis — auto-triggered during /review and /test based on task classification. Provides attack surface analysis, boundary testing, auth bypass attempts, dependency chain attacks, and Beast Mode stress testing.
product-decision-agent
中文产品决策 Agent。用于中国大陆互联网产品、运营、增长、商业化、数据、项目推进和组织协作场景：产品规划、需求分析、PRD、需求优先级、排期、版本规划、Roadmap、MVP、灰度、上线、迭代、增长停滞、拉新、投放、渠道、裂变、CAC、LTV、ROI、留存、转化、DAU/MAU、GMV、漏斗、社区运营、内容供给、创作者、用户运营、活动运营、私域、会员、定价、指标异常、数据口径、埋点、A/B…
marketing-copy
Write outbound promotional copy for a product or project: launch and update posts for community platforms and social media, store page descriptions and short blurbs, landing page headlines and calls to action, press-style announcements, and the naming of a product for another language or market. Covers what may be…
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.