Agent skill

Report

by automagik-dev in automagik-dev/genie

Investigate a failure to its root cause with grounded evidence, hand the diagnosis to fix, and create a GitHub issue only when asked.

MITAuto-check passedDevelopment

Install Report

skills CLI
$ npx skills add automagik-dev/genie --skill report -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install automagik-dev/genie report --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/automagik-dev/genie.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/report .claude/skills/report && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
report
GitHub stars
345
Token cost
~2k tokens
SKILL.md length
1,214 words
Files
3 (incl. references)
Skills in repo
19
Repo updated
First seen
Licence
MIT

At a glance

Investigate a failure to its root cause with grounded evidence, hand the diagnosis to fix, and create a GitHub issue only when asked.

  • Works in 6 steps: Collect symptoms: the description… → Build a red loop before forming a… → Minimize the reproduction: with the loop… → …
  • Tasks that involve Root cause analysis
  • SKILL.md covers When to use, Investigate, Diagnosis format and GitHub issue (only when asked), plus 1 more section
  • Calls git and rg

What it does

Report is an agent skill from automagik-dev/genie. Investigate a failure to its root cause with grounded evidence, hand the diagnosis to fix, and create a GitHub issue only when asked.

Its SKILL.md is about 2k tokens, which your agent loads only when the skill is triggered. The skill folder holds 4 other files, including reference files (for example `agents/openai.yaml` and `references/issue-template.md`).

It sits in Development, covering Root cause analysis. It works with GitHub. The repository describes itself as: Wishes in, PRs out. CLI agent that interviews you, plans the work, dispatches parallel agents in isolated worktrees, and reviews code before you see it. The licence is MIT.

When your agent uses it

  • Tasks that involve Root cause analysis

Example prompts

  • “/report”

Workflow steps

6 steps, taken from the first numbered list in SKILL.md.

  1. Collect symptoms: the description (required), plus error text, stack traces, logs, URL, and expected versus actual behavior when offered…
  2. Build a red loop before forming a theory: name one command that fails right now for the reason under investigation, run it at least once…
  3. Minimize the reproduction: with the loop red, cut one thing at a time — an input, a caller, a configuration value, a data row, a step…
  4. Trace: reproduce, hypothesize, and isolate the root cause with read-only tools: search and non-mutating commands only, no edits, staging…
  5. Instrument without mutating what you are investigating: keep probes in scratch files outside the working tree. When a temporary in-tree…
  6. Compile: every statement traces to tool output from this investigation. Include the supporting evidence the project already offers — a…

What it can do on your machine

Read from SKILL.md and the folder at commit c4f8788. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Shell commands in SKILL.md call:

    • git
    • rg

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md. Its commands use git, which can reach the network depending on how they are called.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Report loads about 2k tokens when it runs, and up to ~2.3k if it reads all its reference files. Until then it costs about 35 tokens; SKILL.md has 1,214 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~35
When it runs · the whole SKILL.md, loaded when a task matches
~2k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~2.3k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from automagik-dev/genie at commit c4f8788, republished under its MIT licence (© automagik-dev). 1,214 words, ~2,010 tokens.

Download SKILL.mdSave it as .claude/skills/report/SKILL.md (or your agent's skills folder). This skill also uses 2 other files; get the full folder from GitHub.
name
report
description
Investigate a failure to its root cause with grounded evidence, hand the diagnosis to fix, and create a GitHub issue only when asked.
category
investigation
mutates
documents

Report

Investigate; never fix. The deliverable is a diagnosis another agent can act on without reproducing the failure. Source edits belong to fix; creating an issue is a separate external write that happens only when the request asks for it or the user confirms it.

When to use

  • A failure exists and the cause is unknown, or the error points nowhere obvious.
  • review or fix needs a root cause before spending a repair attempt.
  • Someone wants a self-contained bug report, with or without a GitHub issue.
  • A QA criterion in a wish failed after merge; map the failure to the criterion it violates before tracing.

Investigate

  1. Collect symptoms: the description (required), plus error text, stack traces, logs, URL, and expected versus actual behavior when offered. Ask only for what the investigation needs.
  2. Build a red loop before forming a theory: name one command that fails right now for the reason under investigation, run it at least once, and keep its output. The loop must be red-capable (it drives the failing path and asserts the reported symptom, not merely "something errored"), deterministic, fast, and runnable unattended. Build it from whatever is cheapest that still reaches the failure: a failing test at any seam, a request script against a running service, a CLI invocation diffed against known-good output, a headless browser script, a replay of a captured trace, a bisection run over the history between a known-good and a known-bad state, a differential loop that puts one input through two versions, configurations, providers or datasets, a property or fuzz loop when the wrong output only appears across a broad input space, or a throwaway harness that boots the smallest slice of the system that still fails. Tighten it before trusting it — sharper assertion, pinned time and seed, fewer seconds. When the failure refuses to fire every time, raise the reproduction rate rather than chase determinism: repeat the trigger, run copies in parallel, add load, narrow the timing window, pin the clock, and seed every source of randomness. Say the rate out loud, because it decides what happens next — a failure that fires about half the time is debuggable, and one that fires once in a hundred runs is a measurement problem to solve before it is a bug to trace. If you catch yourself reading code to build a theory before this command exists, stop; jumping to a hypothesis is the failure this gate prevents. When no loop can be built at all, say so, list what was tried, and name what would unblock it (access to an environment that reproduces, a redacted capture, permission to instrument) rather than hypothesizing without one.
  3. Minimize the reproduction: with the loop red, cut one thing at a time — an input, a caller, a configuration value, a data row, a step — and re-run after every cut, keeping only what the failure needs. You are done when removing anything that remains turns the loop green. What survives is the smallest true statement of the bug, and it is usually the regression test fix should land.
  4. Trace: reproduce, hypothesize, and isolate the root cause with read-only tools: search and non-mutating commands only, no edits, staging, commits, or publication. Investigate directly when one bounded investigation suffices; delegate a read-only scout through the runtime's native delegation surface when independent searches can run in parallel or the investigation needs isolated context, giving it the symptoms, relevant files, and the report format below, and steering it with follow-up messaging rather than starting a duplicate. If the failure cannot be reproduced, the report says so. Show 3-5 ranked falsifiable hypotheses before testing any of them — each stating what change would make the failure disappear or worsen — and then test one variable at a time against the loop. Generate hypotheses cheaply from code in this repository that does the same job and works: list every difference from the failing path, however small, and treat "that cannot matter" as an untested assumption rather than a conclusion.
  5. Instrument without mutating what you are investigating: keep probes in scratch files outside the working tree. When a temporary in-tree probe is genuinely unavoidable, tag every added line with one unique marker such as [DEBUG-a4f2], remove them all before handing off, and prove the tree is clean with git status --porcelain. Instrumentation is never a fix, and an untagged probe is a leak.
  6. Compile: every statement traces to tool output from this investigation. Include the supporting evidence the project already offers — a browser, console or network capture where a URL or dev server exists, recent related errors from monitoring the project actually configures — and where expected evidence could not be captured, say so with the reason. Never present a planned capture as evidence.
Show full SKILL.md (424 more words)Show less

Diagnosis format

Root cause: <what is broken — file, line, condition>
Evidence: <reproduction steps, traces, proof>
Causal chain: <root cause → intermediate effects → observed symptom>
Recommended correction: <what to change, where, why>
Affected scope: <other files or features impacted>
Confidence: <high / medium / low>

Give file paths and line numbers for every claim. Verify every symbol named in the correction against the real file with rg -n '<symbol>' <path> and cite the matching line; a wrong name sends fix into a failing type-check. When more than one system is at fault, report each with its own confidence. An inconclusive trace is reported as "investigation incomplete" with the evidence gathered so far.

Two further outcomes are first-class, not failures to hide. No correct seam: when no seam exercises the real failure pattern as it occurs at the call site, a regression test placed there would give false confidence — report that as the finding and route it to wish, because the architecture, not the bug, is what blocks the lock-down. Architecture, not hypothesis: when repair attempts keep surfacing a new problem somewhere else and the group's configured repair budget is spent, stop counting failed hypotheses and report a wrong architecture; a further attempt is not authorized.

GitHub issue (only when asked)

  1. Search existing issues first; link an identical open issue instead of duplicating it.
  2. Compose the body from references/issue-template.md, with only the evidence that applies and a note for expected evidence that could not be captured.
  3. Present repository, title, labels (bug plus labels the repository already uses), and the body summary; create it through the GitHub connector when available, otherwise gh with the body passed as a file or stdin, never interpolated into a shell command — that is the rule for every payload this skill hands to a command line, not a detail of this one.
  4. Read the issue back and believe only the read-back: a create call that exits zero is not evidence that the body, the labels, or the target repository stored as you sent them, because permission to create an issue is routinely wider than permission to label one. A write that times out or answers ambiguously is not proof that nothing happened — find the record before retrying, or the retry files the issue twice. This holds for any external write, not only for an issue.
  5. If creation fails or authentication is missing, return the full report for manual submission.

The bug can also go on the Genie board with genie task create --title "bug: <title> (gh#<n>)" --agent <roster agent> --why "<reason>"; skip it when there is no .genie/genie.db.

Handoff

Pass the diagnosis to fix or to the caller. Your final message is the completion signal; it carries the diagnosis and names any expected evidence that could not be captured.

© automagik-dev, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 2 other files (references) in skills/report of automagik-dev/genie.

  • SKILL.md
  • agents/openai.yaml
  • references/issue-template.md

Open the folder on GitHubat commit c4f8788

Compare with similar skills

Report next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Report compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Report this skillautomagik-dev/genie345—~2kAutomated safety check: PassMIT
Octocode Code Researchbgauryy/octocode946—~1.5kAutomated safety check: PassMIT
PRP Implementation PlannerWirasm/prp2.3k—~4.1kAutomated safety check: PassMIT
PRP PlanWirasm/prp2.3k—~4kAutomated safety check: PassMIT
Triagearcee-ai/nac279—~2kAutomated safety check: PassApache-2.0
OpenROAD Bug FixerThe-OpenROAD-Project/OpenROAD3.2k—~784Automated safety check: PassBSD-3-Clause

Similar skills

  • Octocode Code Research

    bgauryy/octocode

    Researches code with evidence: traces callers, imports and cross-repo links, diagnoses failures and reports findings with exact file and line references and a confidence label.

    946 GitHub stars~1.5k tokensUpdated 4 days ago
    DevelopmentAuto-check passed
  • Turns a PRD, issue or description into an implementation-ready plan grounded in codebase evidence, adding root-cause analysis for bugs and publishing issue plans back to the issue.

    2.3k GitHub stars~4.1k tokensUpdated 5 days ago
    DevelopmentAuto-check passed
  • PRP Plan

    Wirasm/prp

    Writes an implementation-ready plan for a feature, bug fix, refactor or chore from a PRD, issue or description, grounded in codebase evidence, and can post it back to the source issue.

    2.3k GitHub stars~4k tokensUpdated 5 days ago
    DevelopmentAuto-check passed
  • Triage

    arcee-ai/nac

    Triage a GitHub repository's open issues by finding exact duplicates, rejecting evidenceably off-base requests, requesting concrete clarification, applying only existing labels, and opening a linked…

    279 GitHub stars~2k tokensUpdated today
    DevelopmentAuto-check passed
  • OpenROAD Bug Fixer

    The-OpenROAD-Project/OpenROAD

    Fixes an OpenROAD bug from a GitHub issue or error code: finds the root cause, implements the fix, adds a regression test and prepares a signed-off commit.

    3.2k GitHub stars~784 tokensUpdated yesterday
    DevelopmentAuto-check passed
  • CI Triage

    Mentra-Community/MentraOS

    Triage failing GitHub PR checks: list failures with gh, fetch capped Actions logs, skip non-Actions checks, and summarize root cause.

    2.4k GitHub stars~582 tokensUpdated today
    DevelopmentAuto-check passed

More from automagik-dev/genie

All 19 skills in this repo
  • Learn

    automagik-dev/genie

    Diagnose and fix agent behavioral surfaces when the user corrects a mistake — connects to Claude native memory.

    345 GitHub stars~720 tokensUpdated today
    Auto-check passed
  • Brainstorm

    automagik-dev/genie

    Explore an ambiguous idea with the user, settle scope and success criteria, and produce an independently reviewed design for wish.

    345 GitHub stars~2k tokensUpdated today
    Auto-check passed
  • Wish

    automagik-dev/genie

    Deliver one decided task end to end — admit it, work it in one worktree, gate, independent review, bounded repair, a merge-ready PR — or plan a multi-group wish when it is bigger than one task.

    345 GitHub stars~3.7k tokensUpdated today
    Auto-check passed
  • Work

    automagik-dev/genie

    Execute an approved wish in dependency order with scoped workers, independent review, bounded repairs, and verified completion.

    345 GitHub stars~2.5k tokensUpdated today
    Auto-check passed
  • Authoring

    automagik-dev/genie

    Write or revise a Genie skill so it survives the shipped contract — frontmatter, house size, starter card, and runtime-neutral voice.

    345 GitHub stars~1k tokensUpdated today
    Auto-check passed
  • Council

    automagik-dev/genie

    Assess a proposal through independent technical, product, risk, and dissenting lenses, then synthesize a decision without mutating unless explicitly requested.

    345 GitHub stars~976 tokensUpdated today
    Auto-check passed

Works with

Questions about Report

What does Report do?

Investigate a failure to its root cause with grounded evidence, hand the diagnosis to fix, and create a GitHub issue only when asked. Report is an agent skill from automagik-dev/genie. Investigate a failure to its root cause with grounded evidence, hand the diagnosis to fix, and create a GitHub issue only when asked.

When should I use Report?

Report fits situations like: tasks that involve Root cause analysis.

How do I install Report in Claude Code?

Run `npx skills add automagik-dev/genie --skill report -a claude-code`. Or copy the skill folder (skills/report in automagik-dev/genie) into .claude/skills/report in your project. Claude Code loads it when a task matches its description.

How do I install Report in Codex?

Run `npx skills add automagik-dev/genie --skill report -a codex`. Or copy the skill folder (skills/report in automagik-dev/genie) into .agents/skills/report in your project. Codex loads it when a task matches its description.

Can I use Report in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add automagik-dev/genie --skill report -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/report, .gemini/skills/report, .github/skills/report and .opencode/skills/report in your project.

What does Report need to run?

Going by SKILL.md and its folder, Report needs the command-line tools its instructions call (git and rg).

Does Report access the network?

SKILL.md contains no URLs. Its commands use git, which can reach the network depending on how they are called. This is read from the text; nothing was executed.

Is Report safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Report use?

Report is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Report use?

About 2k tokens (SKILL.md is roughly 8k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 295 tokens, read only when the agent opens those files.

What are the alternatives to Report?

Skills that share tags, products or a category with Report: Octocode Code Research (bgauryy/octocode, 946 stars), PRP Implementation Planner (Wirasm/prp, 2.3k stars), PRP Plan (Wirasm/prp, 2.3k stars) and Triage (arcee-ai/nac, 279 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Report?

automagik-dev (a GitHub organization) maintains it in automagik-dev/genie, which has 345 GitHub stars. The repository holds 19 skills in this directory. The repository was last updated on October 7, 2026.

Source: automagik-dev/genie on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.