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 WellApp-ai/Well --skill debuggit clone --depth 1 https://github.com/WellApp-ai/WellWrote 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/wellapp-ai/well/debug)<a href="https://agentmods.dev/skills/wellapp-ai/well/debug"><img src="https://agentmods.dev/badge/skills/wellapp-ai/well/debug/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/wellapp-ai/well/debug"><img src="https://agentmods.dev/badge/skills/wellapp-ai/well/debug.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
SkillSpector: 1 finding, up to low
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 →
- low Excessive Agency · line 150 Skill's behavior or capabilities extend beyond its stated purpose. Scope creep allows an agent to perform actions unrelated to its documented functionality, increasing the attack surface.Fix: Limit the skill's scope to its documented purpose. Remove instructions that enable the agent to perform actions outside its stated functionality.
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 | $0.00021 | $0.02654 |
| Opus 5.5 | $0.00008 | $0.01062 |
| Sonnet 5.5 | $0.00004 | $0.00531 |
| Haiku 4.5 | $0.00002 | $0.00265 |
Grade A, and why
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 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 — 499 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Debug Skill
Diagnose and fix issues systematically. Enhanced with MCP integrations for deeper analysis and automatic regression test generation.
When to Use
- Auto-invoked by qa-commit skill on RED verdict
- Error messages appearing in console/terminal
- Feature not working as expected
- Build/runtime failures
- "Something is broken" situations
Modes
| Mode | Trigger | Context Provided |
|---|---|---|
| Auto | qa-commit RED verdict | Failed G#N/AC#N, error messages |
| Manual | User invokes | User describes issue |
The Enhanced Flow
Phase 0: Context Loading (if auto-invoked)
↓
Phase 0.5: Jidoka Escalation Check ──→ [Tier 2/3] ──→ ESCALATE to human
↓ [Tier 1]
Phase 1: Gather (ReadLints, Browser MCP, Context7)
↓
Phase 2: Reproduce (Browser MCP)
↓
Phase 3: Isolate (Known Issues DB query)
↓
Phase 4: Diagnose
↓
Phase 5: Fix
↓
Phase 6: Verify ──→ [FAIL] ──→ Phase 8 ──→ Phase 0.5
↓ [PASS]
Phase 7: Harden (generate regression test)
↓
Phase 8: Update Jidoka Counters (reset on success)
Phase 0: Context Loading (Auto-Invoke Only)
When invoked from qa-commit, receive context:
## Debug Context (from qa-commit)
**Failed Criteria:**
- [G#N or AC#N]: [Description]
**Verification Report:**
- ReadLints errors: [list]
- Shell errors: [list]
- Browser errors: [list if applicable]
**Expected Behavior:**
[From QA Contract]
**Actual Behavior:**
[Observed during verification]
Skip this phase if manually invoked.
Phase 0.5: Jidoka Escalation Check (NEW)
Before attempting fix, check escalation tier to determine if human intervention is needed.
Track Error History
Maintain error_history across debug invocations:
| Field | Description |
|---|---|
| error_signature | Hash of error type + location |
| count | Times this exact error seen |
| fixes_attempted | List of fix descriptions |
Tier Evaluation
| Tier | Condition | Action |
|---|---|---|
| Tier 1 | error_count < 3 | Continue to Phase 1 (normal debug) |
| Tier 2 | error_count >= 3 (same error) | ESCALATE to human |
| Tier 3 | total_errors >= 5 (any) | ESCALATE to human |
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 · 499 lines · 21 tokens per session scan A 16481079f2e0
debug is a skill published in the GitHub repository WellApp-ai/Well (345 stars, last pushed 6d ago), licensed MIT. It adds 21 tokens to every session and 2,654 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 2026-08-30.
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