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/sjarmak/coding-agent-workflows/bisectnpx skills add sjarmak/coding-agent-workflows --skill bisectgit clone --depth 1 https://github.com/sjarmak/coding-agent-workflowsWhat 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 | $0.00052 | $0.01616 |
| Opus 5 | $0.00026 | $0.00808 |
| Sonnet 5 | $0.00010 | $0.00323 |
| Haiku 4.5 | $0.00005 | $0.00162 |
Grade A, and why
bisect 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 — 163 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Bisect: Binary Search for Root Cause
Define a search space and a pass/fail oracle, then iteratively test midpoints, halving the space each step until the root cause is isolated. Works across git history, configuration, dependencies, or code modules.
Arguments
"description of the bug or regression"
- A description of the symptom or regression is the search target.
- Missing or unclear: ask the user to clarify the symptom before starting.
Phase 1: Define the Search Space
Work with the user to establish four things before starting the search loop.
1. The symptom: what is broken? What does failure look like? Get a concrete, observable description (error message, wrong output, crash, performance degradation).
2. The search dimension: where to search. Determine which applies:
- Git history: a known-good commit and a known-bad commit (classic git bisect)
- Configuration space: a set of config keys that might cause the issue
- Dependency versions: which dependency upgrade broke things
- Code modules: which module is responsible for the regression
3. The test oracle: how to determine pass/fail at each midpoint:
- A specific test command (
npm test,pytest tests/foo.py,go test ./..., etc.) - A behavior check (does endpoint X return 200? does the build succeed?)
- A manual check (agent reads code or output and assesses)
4. Known bounds: the "good" end and "bad" end of the search space.
For git history bisect, run:
git log --oneline <good_commit>..<bad_commit>
to enumerate the search space. Count the candidates and report: "Search space: N commits. Expected steps: ceil(log2(N))."
For configuration bisect, list all changed config keys between the working and broken state. For dependency bisect, list all upgraded packages with old and new versions sorted by update date.
Present the search space summary to the user and confirm before proceeding. Adjust if the user gives feedback.
Phase 2: Binary Search Loop
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 · 163 lines · 52 tokens per session scan A 143d35ce05e1
bisect is a skill published in the GitHub repository sjarmak/coding-agent-workflows (2 stars, last pushed 1mo ago), licensed MIT. It adds 52 tokens to every session and 1,616 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 2026-08-31.
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