agentleFS
BuddhiliveBuddhilive/buddhi-cli/.agents/skills/debug/SKILL.md

debug

Skill1 starsChanged 13 days agoMITView on GitHub

Systematically investigate a bug, error, or unexpected-behavior report, grounded in this codebase's actual docs and code graph, and produce a confirmed root-cause explanation with a fix plan without applying the fix. Use when investigating issues with /debug or diagnosing errors.

192 other projects use a version of this.

Adopted by

Most recently taken up by

Used with

Projects with it also use add-discord, add-slack and 10 more.

Other files people use alongside this one.

Files found in the same public projects as a version of this one, more often than they turn up anywhere else. Each project counts once, large collections count for less, and this file's own repository is left out.
  • MicrosoftSkill

    add-discord

    Add Discord bot channel integration via Chat SDK.

    Also in 11 projects that use this

  • MicrosoftSkill

    add-slack

    Add Slack channel integration via Chat SDK.

    Also in 11 projects that use this

  • +10 more from microsoft

The file

We split this skill into the rules it gives an agent

3 rules. 0 are common, 1 is distinctive, and 2 are very specific to this project.

  • 0

    Common

    Most of the 175 edited versions we read keep these.

  • 1

    Distinctive

    This author's own choices. Worth a look.

  • 2

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Distinctive:

    No code writing in this skill — only investigation and a fix plan.

    line 14
  2. Very specific:

    Update code graph: Run buddhi generate via the terminal-runner agent to ensure the code graph (.buddhi/graphs/tree-graph.db and …

    line 19
  3. Very specific:

    Before reading raw source, consult .buddhi/docs/ and use Buddhi MCP tools (buddhi_search, buddhi_read, see the okf-context skill) or query …

    line 27

“Common” is measured: kept by at least 6 in 10 of the 175 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

/debug

$ARGUMENTS

No code writing in this skill — only investigation and a fix plan. Find the real root cause in this codebase, not a plausible-sounding guess.

Steps

  1. Update code graph: Run buddhi generate via the terminal-runner agent to ensure the code graph (.buddhi/graphs/tree-graph.db and tree-graph.json) is up to date before investigation.
  2. Gather the symptom from $ARGUMENTS. If any of the following is missing, ask one question at a time — the error/symptom, reproduction steps, expected vs. actual behavior, and what changed recently (check via git log, run through the terminal-runner agent) — stopping and waiting for the answer before asking the next. Do not batch questions.
  3. Before reading raw source, consult .buddhi/docs/ and use Buddhi MCP tools (buddhi_search, buddhi_read, see the okf-context skill) or query .buddhi/graphs/tree-graph.db / tree-graph.json for the subsystem the symptom points to, per the harness's docs/graph-before-source convention.
  4. Form a ranked list of candidate root causes, most likely first, based on what step 3 turned up. If the failing area spans domains ambiguously enough that a /plan-style breakdown would help, optionally dispatch the relevant specialist agent(s) under agents/ to narrow the candidates — this is a judgment call, not a required step.
  5. Investigate systematically rather than jumping at the first plausible hypothesis: reproduce the failure via terminal-runner where possible, inspect real logs/output, and eliminate candidates one at a time using that evidence.
  6. Once the root cause is confirmed, report it with file:line references drawn from the graph/docs (not vague descriptions), plus a concrete fix plan — what needs to change and where. Do not write or apply the fix. Suggest running /verify once the fix has actually been applied, to prove it worked.

Download now (ZIP)

Does it work?

Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.

No reviews yet

People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.

  • It worked
  • Partly
  • It didn't
What did you use it for? e.g. Reviewing pull requests in a Rails app

Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.

Your trail

Files you open will collect here, so you can find them again.

Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.

Now do this for your own.

Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.