agentmods.dev

bug-report

bug-report is a command for coding agents from vladm3105/aidoc-flow-framework. It costs 55 tokens per session (1,944 once invoked), scanned A, original, MIT.

A command that turns a user's description of a software problem into a pre-filled GitHub issue, where GitHub is a website for hosting code and tracking project work.

In plain words
What is it for?
It uses the prompt and conversation context to draft an issue title and body, then prints a URL that opens GitHub with those fields filled in. It does not submit the issue automatically.
Why use it?
It saves time writing a clear bug report while leaving the user in control of reviewing and submitting it.

Command

Installs and runs on its own, but its text points at files inside its plugin — anything it tells you to read at a ${CLAUDE_PLUGIN_ROOT} path is only there once the plugin is installed. Installing the plugin gets both.

Part of the aidoc-flow plugin — 52 skills, 12 commands, 11 agents, 1 hook shipped together

not rated 17 today A scan 55 tokens original MIT
View the source file vladm3105/aidoc-flow-framework
Install

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.

agentmods
npx agentmods add commands/vladm3105/aidoc-flow-framework/bug-report
Clone the repo
git clone --depth 1 https://github.com/vladm3105/aidoc-flow-framework

Or install aidoc-flow, the plugin that ships this one along with the rest of its 52 skills, 12 commands, 11 agents, 1 hook.

Wrote 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.

agentmods badge for bug-report

README.md
[![agentmods](https://agentmods.dev/badge/commands/vladm3105/aidoc-flow-framework/bug-report.svg)](https://agentmods.dev/commands/vladm3105/aidoc-flow-framework/bug-report)
Your own site
<a href="https://agentmods.dev/commands/vladm3105/aidoc-flow-framework/bug-report"><img src="https://agentmods.dev/badge/commands/vladm3105/aidoc-flow-framework/bug-report.svg" alt="Measured on agentmods" height="20"></a>
Per session 55 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 1,944 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5 $0.00055 $0.01944
Opus 5 $0.00028 $0.00972
Sonnet 5 $0.00011 $0.00389
Haiku 4.5 $0.00006 $0.00194

Measured 3d ago against content hash 34bdaabd22c9, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

bug-report 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 3d 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.

platforms/claude-code-plugin/commands/bug-report.md · 208 lines

How it starts

The opening of the file, as written. The whole thing — 208 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Bug Report

Turn a one-line user complaint into a well-structured GitHub bug-report issue that is prefilled with both title and body, then hand the user a URL to review and submit on github.com. The user types one sentence describing what broke; the LLM does the writing work using the prompt and the current conversation context (what command was running, what error appeared, what files were involved).

Invocation

/aidoc-flow:bug-report <one-sentence description of what's broken>

Examples:

/aidoc-flow:bug-report this feature generate error and does not work as expected
/aidoc-flow:bug-report /aidoc-flow:status crashes on projects without docs/
/aidoc-flow:bug-report doc-brd-autopilot timed out at the audit step

Running /aidoc-flow:bug-report with no text still works — the LLM uses only the conversation context.

Instructions

  1. Capture the user's argument — every word after /aidoc-flow:bug-report is the user's report. Store it as user_complaint. If empty, set user_complaint = "(none provided — using conversation context only)".

  2. Gather the environment stamp — read:

    • ${CLAUDE_PLUGIN_ROOT}/VERSION → plugin_version
    • ${CLAUDE_PLUGIN_ROOT}/FRAMEWORK_SPEC_VERSION → framework_spec
    • uname -srm → os_arch
    • claude --version → claude_version (fall back to (unknown) if unavailable)

  3. Read the conversation context — review the recent messages and tool calls in the current chat. Look for:

    • The most recent error message, traceback, or failure output (if any).
    • The most recent command or skill the user was running when the problem appeared (e.g. /aidoc-flow:status, doc-brd-autopilot, gh pr create).
    • Files referenced in the failure (paths from tool calls, error locations).
    • What the user appeared to expect vs what happened, if it can be inferred from the surrounding conversation.

    If the conversation is fresh and contains no failure context, do not fabricate one — leave Steps to reproduce and Actual behaviour as placeholders the user fills in.

Read the full file on GitHub · 208 lines

Changes

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.

  1. 3d ago First seen · 208 lines · 55 tokens per session scan A 34bdaabd22c9

Subscribe to this mod's changes

bug-report is a command published in the GitHub repository vladm3105/aidoc-flow-framework (17 stars, last pushed today), licensed MIT. It adds 55 tokens to every session and 1,944 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.

Data and AIDebuggingactivemeta
Related

Other commands, from other repositories

create-command

Create a new obsidian-second-brain command via interview - zero markdown editing required.

eugeniughelbur/obsidian-second-brain · 16 tokens

obsidian-health

Run a vault health check - grouped by severity, detects contradictions, concept gaps, stale claims, and structural issues.

eugeniughelbur/obsidian-second-brain · 22 tokens

obsidian-visualize

Generate a visual canvas map of your vault - see the shape of your second brain and how knowledge connects.

eugeniughelbur/obsidian-second-brain · 20 tokens

obsidian-architect

Scan a codebase and write a maintained set of architecture notes into the vault - overview, per-module notes, key decisions. Re-run to refresh without clobbering your edits.

eugeniughelbur/obsidian-second-brain · 36 tokens

obsidian-export

Export a clean structured snapshot of the vault that any agent or tool can consume - flat JSON, markdown index, or an OKF (Open Knowledge Format) bundle.

eugeniughelbur/obsidian-second-brain · 32 tokens

obsidian-retrieval-eval

Measure how well vault search finds the right note for a natural-language question - recall@k and MRR, with the concrete failures.

eugeniughelbur/obsidian-second-brain · 26 tokens