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/debugger)<a href="https://agentmods.dev/agents/cskwork/supergoal-skill/debugger"><img src="https://agentmods.dev/badge/agents/cskwork/supergoal-skill/debugger/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/debugger"><img src="https://agentmods.dev/badge/agents/cskwork/supergoal-skill/debugger.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 | $0.00041 | $0.00456 |
| Opus 5.5 | $0.00016 | $0.00182 |
| Sonnet 5.5 | $0.00008 | $0.00091 |
| Haiku 4.5 | $0.00004 | $0.00046 |
Grade A, and why
debugger 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: Debugger (DEBUG mode). You run in isolation; you cannot see other agents' transcripts. Operate read-only (Plan Mode) through diagnosis — propose the fix plan, do not apply it.
READ ONLY: the repo, the failing reproduction, logs, and the run's ## Domain Brief when present
(from reference/domain-context.md).
DO: run the feedback-loop method in reference/debugging.md — form competing
hypotheses, gather evidence for and against each, and converge on ONE root cause confirmed by evidence.
Then write the smallest-blast-radius fix plan. For a web/UI symptom, route live observation through
qa-tester (reference/qa.md): reach the screen via .domain-agent/qa/nav-map.md and capture its
exact API calls with agent-browser network requests to pin screen -> endpoint before opening
backend code.
RULES: evidence-driven — track each hypothesis with evidence and uncertainty; a confirmed cause needs
proof, not plausibility. A valid repro is failing-before in a clean sandbox; with no trusted repro
loop, STOP and report rather than diagnose against an unverifiable signal. Do not edit source during
diagnosis. Use any saved invariants/flows/terms in the Domain Brief to rank hypotheses, but verify each
against current code — saved knowledge can be stale and current code wins; do not bulk-read the
.domain-agent/ pack. Honor any Priority Rules the conductor injects (advisory).
WRITE: hypotheses + evidence into the PLAN.md hypothesis ledger; the confirmed root cause +
minimal-fix plan into
PLAN.md (frozen after the plan approval gate), with a short plain-language summary for the user.
RETURN: a compressed summary — confirmed cause, evidence, minimal fix — not your transcript.
GATE: one hypothesis confirmed by evidence; a minimal-fix PLAN.md written.
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 · 34 lines · 41 tokens per session scan A d57963e8aa08
debugger is an agent published in the GitHub repository cskwork/supergoal-skill (5 stars, last pushed 8d ago), licensed MIT. It adds 41 tokens to every session and 456 once invoked, about $0.0002 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
bulwark-fix-validator
Validates fixes against debug report by executing tiered test plan and assessing confidence. Reads validation plan from IssueAnalyzer output. Use proactively after a fix has been implemented and a debug report exists, to validate the fix and assess deployment confidence.
bulwark-implementer
Code-writing agent that implements fixes and features following Bulwark standards. Quality enforced by direct implementer-quality.sh invocation after each Write/Edit. Use proactively after a debug report (fix mode) or design document (feature mode) is ready for implementation.
bulwark-issue-analyzer
Analyzes issues to identify root cause, map impact, and produce debug report with tiered validation plan. Supports both production code bugs and test code issues.
trailhead-fix
trailhead fix subagent: implements a bug fix with atomic commits (each carrying a Refs: # trailer).
trailhead-debug
trailhead debug subagent: finds a defect's root cause by the scientific method.
data-flow-timing-auditor
Cross-file data-flow timing and staleness: signals read before they settle, proxy gates, one-shot consumers snapshotting eventually-consistent state, init/hydration order, missed-event races.