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/patrickjaja/claude-desktop-extra/debugnpx skills add patrickjaja/claude-desktop-extra --skill debuggit clone --depth 1 https://github.com/patrickjaja/claude-desktop-extraWhat 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 | $0.00068 | $0.01663 |
| Opus 5 | $0.00034 | $0.00831 |
| Sonnet 5 | $0.00014 | $0.00333 |
| Haiku 4.5 | $0.00007 | $0.00166 |
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 2d 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 — 89 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Debug - collect evidence, then diagnose
The issue to debug: $ARGUMENTS
You are debugging the patched Claude Desktop. Cowork runs on the .deb's bundled native VM backend (the old claude-cowork-service daemon is deprecated). Gather the relevant local evidence below (skip what's clearly irrelevant to "$ARGUMENTS"), form a hypothesis, then ask the user for anything you can't collect yourself. Read /architecture for how the pieces fit and /linux for session/CU specifics if relevant.
0. Resolve the config/log dir FIRST (1p vs 3p) - or you read stale evidence
The runtime dir is conditional: a 3p/enterprise deployment relocates everything to ~/.config/Claude-3p/.
Resolve it once and use $CFG everywhere below.
# 3p (inference-gateway / Bedrock / managed) -> Claude-3p ; otherwise -> Claude
if [ -f /etc/claude-desktop/managed-settings.json ]; then CFG=~/.config/Claude-3p; else CFG=~/.config/Claude; fi
# sanity-check against the running process (named profiles add a further -<profile> suffix):
pgrep -af claude | grep -o -- '--user-data-dir=[^ ]*' | head -1
echo "using CFG=$CFG"
If pgrep shows a --user-data-dir that differs from $CFG (e.g. a named profile), prefer that path.
1. Last local-agent-mode session transcript (the single source of truth for Cowork/Dispatch/agent runs)
audit.jsonl records exactly what the model saw and did. Find the newest one and read the user's last prompt + the assistant tool calls + any errors:
AUDIT=$(find "$CFG"/local-agent-mode-sessions -name audit.jsonl -printf '%T@ %p\n' 2>/dev/null | sort -n | tail -1 | cut -d' ' -f2)
echo "newest audit: $AUDIT"
python3 -c "
import json,sys
p='$AUDIT'
if not p: sys.exit('no audit.jsonl found')
rows=[json.loads(l) for l in open(p) if l.strip()]
# user's first/last prompt
users=[r for r in rows if r.get('type')=='user']
def text(m):
c=(m or {}).get('content')
if isinstance(c,str): return c
if isinstance(c,list): return ' '.join(x.get('text','') for x in c if isinstance(x,dict))
return str(c)
if users: print('FIRST USER PROMPT:', text(users[0].get('message'))[:500])
if len(users)>1: print('LAST USER PROMPT:', text(users[-1].get('message'))[:500])
# tool calls + errors
for i,r in enumerate(rows):
t=r.get('type')
if t=='assistant':
for c in (r.get('message',{}).get('content') or []):
if isinstance(c,dict) and c.get('type')=='tool_use': print(f'[{i}] tool_use: {c.get(\"name\")} {str(c.get(\"input\",{}))[:120]}')
elif t=='user' and ('Error' in str(r) or 'Permission' in str(r) or 'denied' in str(r)):
print(f'[{i}] error/result: {str(r)[:200]}')
elif t=='result': print(f'[{i}] RESULT: {str(r.get(\"result\",r))[:200]}')
"
If "$ARGUMENTS" is about dispatch/cowork/skills not working, this transcript usually shows the failing tool call, a permission denial, or a wrong path.
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.
- 2d ago First seen · 89 lines · 68 tokens per session scan A c021edacc151
debug is a skill published in the GitHub repository patrickjaja/claude-desktop-extra (610 stars, last pushed 4d ago), licensed MIT. It adds 68 tokens to every session and 1,663 once invoked, about $0.0003 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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