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 arozumenko/sdlc-skills --skill bugfix-workflowgit clone --depth 1 https://github.com/arozumenko/sdlc-skillsWrote 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/arozumenko/sdlc-skills/bugfix-workflow)<a href="https://agentmods.dev/skills/arozumenko/sdlc-skills/bugfix-workflow"><img src="https://agentmods.dev/badge/skills/arozumenko/sdlc-skills/bugfix-workflow.svg" alt="Measured on agentmods" 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 77 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 | $0.00016 | $0.00949 |
| Opus 5 | $0.00008 | $0.00475 |
| Sonnet 5 | $0.00003 | $0.00190 |
| Haiku 4.5 | $0.00002 | $0.00095 |
Grade A, and why
bugfix-workflow scanned grade A with 1 finding 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 7d 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
skill. UI bugs → Playwright MCP snapshots. API bugs → `curl`. Logic bugs → How it starts
The opening of the file, as written. The whole thing — 106 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Bugfix Workflow: Reproduce → Test → Fix → Verify
Core philosophy: reproduce before you fix, verify after you fix, link everything to the ticket.
Platform & systems
gh issue … / gh pr … below are the GitHub reference. Translate to your
tracker per .agents/profile.md § Project systems (jira / gitlab-issues /
azure-boards / linear) and your code host's CLI per .agents/workflow.md
(GitLab glab, Azure DevOps az repos, Bitbucket bb, Gitea tea). If scout
hasn't recorded them, ask before assuming GitHub.
The seven steps
1. Read & understand the bug
2. Reproduce
3. Write a failing test
4. Root cause analysis
5. Implement the fix
6. Verify
7. Document on ticket
1. Read & understand the bug
gh issue view <N>. Identify expected vs actual behavior, files involved,
error messages, stack traces, related issues. Post a 🔧 **Investigating**
comment on the ticket so the reporter knows work has started.
2. Reproduce
Confirm the bug exists and understand the exact conditions that trigger it.
For the full method — UI / API / logic / intermittent attempts and the
CONFIRMED / CANNOT-REPRODUCE confirmation gate — use the reproducing-issues
skill. UI bugs → Playwright MCP snapshots. API bugs → curl. Logic bugs →
minimal reproducer script.
If you cannot reproduce, stop and ask. Do not proceed to fix a bug you cannot observe. Post what you tried and request more details.
Once reproduced, post a ✅ **Reproduced** comment with steps, expected,
actual, and frequency (always / intermittent).
3. Write a failing test
Capture the bug as a regression test before fixing. Run it and confirm it fails — that's the whole point. A passing test for an unfixed bug means the test doesn't exercise the bug.
4. Root cause analysis
Trace the bug to its exact cause — for the full method (execution-path
tracing, root-cause classification, confidence, impact/regression analysis)
use the root-cause-analysis skill. Locate the code, trace execution from
entry point to failure, classify the cause (logic / data / concurrency /
config / integration / resource), and assess blast radius — what else uses
this code, could the fix break anything else?
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 7d ago First seen · 106 lines · 16 tokens per session scan A 233c22832aeb
bugfix-workflow is a skill published in the GitHub repository arozumenko/sdlc-skills (20 stars, last pushed 3d ago), licensed MIT. It adds 16 tokens to every session and 949 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
systematic-debugging
4-phase root cause debugging: understand bugs before fixing.
langsmith-observability
LLM observability platform for tracing, evaluation, and monitoring. Use when debugging LLM applications, evaluating model outputs against datasets, monitoring production systems, or building systematic testing pipelines for AI applications.
experimental-code-coverage-local-debugger
Runs code coverage locally via Universal Test Runner (UTR) or helper scripts, mimicking LUCI trybots. Activate when CQ tryjobs fail or underreport coverage, to test local GN/recipe repairs before uploading, or to debug hermetic crashes.
adversarial-reviewer
Adversarial code review that assumes bugs exist and hunts for them. Use when asked to review code, find bugs, audit for correctness, stress-test a PR, or when someone says "tear this apart" or "what's wrong with this". Give no benefit of the doubt — every line is guilty until proven innocent.
cli-e2e
Write, modify, or debug Docker-based Composio CLI end-to-end tests under ts/e2e-tests/cli, including binary invocation, fixture isolation, output assertions, and package manifests. Use for CLI E2E test suites only; use cli-command for CLI source implementation.
ios-simulator
Verify and debug native, React Native, Expo, or Flutter apps on an iOS Simulator with agent-device. Use when an agent needs to launch an app, inspect its live UI, tap, type, scroll, validate a code change, collect failure evidence, or reproduce a workflow on an iPhone or iPad Simulator.