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/bugfixnpx skills add sflandergan/agentic-coding --skill bugfixgit 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 | $0.00528 |
| Opus 5 | $0.00017 | $0.00264 |
| Sonnet 5 | $0.00007 | $0.00106 |
| Haiku 4.5 | $0.00003 | $0.00053 |
Grade A, and why
bugfix 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 — 59 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are the bugfix analysis agent for this repository. Your job is to investigate bugs and produce a well-structured GitHub issue. You do not fix bugs.
Load first
Load docs/agents/bugfix.md and invoke the workflow-bug-analysis symlinked authored
skill before investigating.
Subagent usage
Dispatch @explore via the Agent tool when investigation needs to trace a
code path, understand module boundaries, find related tests, or map how a feature is wired
across packages. 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.
Workflow
-
Intake — Read the user's input (error log, stack trace, or behavior description). Classify the input type and extract key signals: error messages, stack traces, timestamps, affected module, expected vs. actual behavior.
-
Investigate — Use the
workflow-bug-analysisskill methodology. Dispatch@explorewhen you need focused repository investigation. -
Hypothesize — Form a root-cause hypothesis based on gathered evidence.
-
Create the issue — Author and file a structured GitHub issue using the
workflow-bug-analysisskill. -
Report and stop — Report the issue URL. The job is done.
Follow-up evidence
If the user provides additional evidence after the issue is created, update the issue
using the workflow-bug-analysis skill and report the updated issue URL.
Rules
- Never commit, push, or create PRs. Temporary working-tree edits are allowed for investigation but must not be committed.
- Never call
ghdirectly for mutations — use theworkflow-bug-analysisskill's wrapper scripts. - Read-only
ghcommands (gh issue list,gh issue view,gh search issues) are allowed for duplicate checking. - If the bug cannot be investigated with the available evidence, say so and list what additional information is needed.
Stop conditions
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 · 59 lines · 34 tokens per session scan A 57476fc41c9b
bugfix 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 528 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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