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/bisect-regressionnpx skills add loopdive/js2 --skill bisect-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 | $0.00023 | $0.00833 |
| Opus 5 | $0.00012 | $0.00417 |
| Sonnet 5 | $0.00005 | $0.00167 |
| Haiku 4.5 | $0.00002 | $0.00083 |
Grade A, and why
bisect-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 — 108 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Bisect Regression
Find which commit introduced a regression using git bisect with an automated test script.
Prerequisites
- Two archived test262 JSONL files: one "good" (higher pass count) and one "bad" (current/lower)
- Or: a known good commit hash and the current HEAD
Step 1: Identify the regression
# 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['status']
# Find archived files
archives = sorted([f for f in os.listdir('benchmarks/results')
if f.startswith('test262-results-') and f.endswith('.jsonl')])
print('Available archives:', archives)
# Compare with most recent archive
if archives:
prev = {}
with open(f'benchmarks/results/{archives[-1]}') as f:
for line in f:
r = json.loads(line)
prev[r['file']] = r['status']
regressions = [t for t in prev if prev[t] == 'pass' and current.get(t) != 'pass']
fixes = [t for t in current if current[t] == 'pass' and prev.get(t) != 'pass']
print(f'Regressions: {len(regressions)}, Fixes: {len(fixes)}')
# Pick a sample regression test for bisecting
if regressions:
print(f'Sample test for bisect: {regressions[0]}')
"
Step 2: Run git bisect with vitest filter
GOOD="<commit-hash-where-test-passed>" # e.g., the baseline commit
BAD="HEAD"
git bisect start
git bisect bad $BAD
git bisect good $GOOD
# vitest exits 0 on pass, 1 on fail — perfect for bisect
git bisect run npx vitest run tests/test262-vitest.test.ts -t "<test-name-pattern>"
Example with a specific test file:
git bisect run npx vitest run tests/test262-vitest.test.ts -t "S7.9.2_A1_T6"
This binary-searches through commits (~7 steps for 90 commits) and reports the first bad commit.
Step 3: Analyze the result
# git bisect will print: "<hash> is the first bad commit"
git show <hash> --stat
git show <hash>
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 · 108 lines · 23 tokens per session scan A abaa56d041bb
bisect-regression is a skill published in the GitHub repository loopdive/js2 (59 stars, last pushed 2d ago), licensed Apache-2.0. It adds 23 tokens to every session and 833 once invoked, about $0.0001 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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