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/sflandergan/agentic-coding/workflow-bug-analysisnpx skills add sflandergan/agentic-coding --skill workflow-bug-analysisgit clone --depth 1 https://github.com/sflandergan/agentic-codingWhat 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 | $0.00034 | $0.00756 |
| Opus 5 | $0.00017 | $0.00378 |
| Sonnet 5 | $0.00007 | $0.00151 |
| Haiku 4.5 | $0.00003 | $0.00076 |
Grade A, and why
workflow-bug-analysis 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 yesterday.
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 — 110 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Bugfix Analysis
Systematic investigation methodology for bug reports. The calling agent uses this skill to trace a bug from symptom to root cause and produce a structured GitHub issue.
Investigation Steps
1. Classify the input
Determine the input type and extract key signals:
- Log/error input: stack trace, error message, HTTP status code. Extract: error message, stack trace frames, timestamps, affected module.
- Behavior description: unexpected output, missing data, wrong calculation. Extract: expected vs. actual behavior, affected feature, reproduction hints.
2. Reproduce or confirm
Run the relevant test or code path to confirm the symptom exists:
- Use the project's test commands (e.g.
pnpm test,pnpm test:integration, or targeted test commands). - If no existing test covers the bug, add a temporary reproduction test or log statement, run it, and report the result.
- Temporary working-tree changes must not be committed.
3. Trace the code path
Use @explore to map the affected code. Start from the error location or the described behavior and trace backwards to the root cause. Pay attention to:
- Architecture boundaries between packages
- Database queries and repository layers
- Service dependencies and injection
- Job orchestration and batch processing
Do not continue investigation from weak context — launch one or more explore subagents with focused questions. Multiple explore subagents may run in parallel when their questions are independent.
4. Inspect logs
When the bug involves runtime behavior, inspect the relevant logs. See docs/agents/bugfix.md for project-specific log locations and conventions.
- Look for error and warning entries near the reported symptom time.
- Match log levels to the project's conventions.
5. Form a hypothesis
Based on gathered evidence, state:
- What you believe the root cause is
- Why the evidence supports this conclusion
- What alternative explanations were considered and ruled out
What ships with it
3 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.
- yesterday First seen · 110 lines · 34 tokens per session scan A 7d031ec91609
workflow-bug-analysis is a skill published in the GitHub repository sflandergan/agentic-coding (2 stars, last pushed 2mo ago), licensed MIT. It adds 34 tokens to every session and 756 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 2026-08-31.
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