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/loopdive/js2/handle-regressionnpx skills add loopdive/js2 --skill handle-regressiongit clone --depth 1 https://github.com/loopdive/js2What 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 | $0.00032 | $0.02004 |
| Opus 5 | $0.00016 | $0.01002 |
| Sonnet 5 | $0.00006 | $0.00401 |
| Haiku 4.5 | $0.00003 | $0.00200 |
Grade A, and why
handle-regression 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 — 223 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Handle Regression Protocol
Use this when a test262 run shows fewer passes than the baseline or previous run.
Step 1: Confirm the regression is real
# Compare current vs archived results
python3 -c "
import json, os
current = {}
with open('benchmarks/results/test262-results.jsonl') as f:
for line in f:
r = json.loads(line)
current[r['file']] = r
archives = sorted([f for f in os.listdir('benchmarks/results')
if f.startswith('test262-results-') and f.endswith('.jsonl')])
if not archives:
print('No archived results to compare. Run was the first.')
exit()
prev = {}
with open(f'benchmarks/results/{archives[-1]}') as f:
for line in f:
r = json.loads(line)
prev[r['file']] = r
regressions = [(t, prev[t]['status'], current[t]['status'], current[t].get('error','')[:60])
for t in prev if prev[t]['status'] == 'pass' and current.get(t,{}).get('status') != 'pass']
fixes = [t for t in current if current[t]['status'] == 'pass' and prev.get(t,{}).get('status') != 'pass']
print(f'Regressions: {len(regressions)}, Fixes: {len(fixes)}, Net: {len(fixes) - len(regressions):+d}')
from collections import Counter
patterns = Counter()
for t, old, new, err in regressions:
if 'SyntaxError' in err: patterns['expected SyntaxError but compiled'] += 1
elif 'null' in err.lower(): patterns['null deref'] += 1
elif new == 'compile_error': patterns['new compile error'] += 1
elif 'returned' in err: patterns['wrong return value'] += 1
else: patterns[err[:30] or new] += 1
print()
print('Regression patterns:')
for p, c in patterns.most_common(10):
print(f' {c:4d} {p}')
print()
print('Sample regressions (1 per pattern):')
shown = set()
for t, old, new, err in sorted(regressions):
for p in patterns:
if p not in shown and p[:15] in (err + new)[:60]:
print(f' {t}')
shown.add(p)
break
if len(shown) >= 5: break
"
If regressions < 10 and could be flaky tests, re-run test262 to confirm.
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 · 223 lines · 32 tokens per session scan A f06de6b5e22a
handle-regression is a skill published in the GitHub repository loopdive/js2 (59 stars, last pushed 2d ago), licensed Apache-2.0. It adds 32 tokens to every session and 2,004 once invoked, about $0.0002 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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