MCPInstall

gds-investigate

Forensic case investigation with evidence-graded findings, calibrated to the input. Use when the user asks to investigate a bug, trace what caused an incident, walk through unfamiliar code, or build a mental model of a code area before working on it.

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Install

Works with every agent the skills CLI supports.

npx skills add https://github.com/bmad-code-org/bmad-module-game-dev-studio --skill gds-investigate
  1. 01

    Install

    Run the command in your project. The CLI asks which agents to install for.

  2. 02

    Describe the task

    Ask your agent for something this skill covers. It loads gds-investigate automatically when relevant.

  3. 03

    Review the result

    The agent follows the SKILL.md steps, scripts and references bundled with the skill.

SKILL.md

The instructions your agent reads when the skill is triggered.

Investigate

Overview

Reconstruct what's happening, or what an unfamiliar area does, from the available evidence. Produce a structured case file another engineer can pick up cold. Calibrate continuously between defect-chasing (symptom-driven) and area-exploration (no symptom); the same discipline applies on both ends.

Args: A ticket ID, log file path, diagnostic archive, error message, code area name, problem description, or a path to an existing case file. The last form resumes a prior investigation; everything else opens a new case.

Output: {implementation_artifacts}/{workflow.case_file_subdir}/{workflow.case_file_filename}. Reference inputs are recorded; raw content is not read into the parent context until an outcome calls for it.

{slug} is the ticket ID when one is provided, otherwise a short descriptive name agreed with the user, sanitized to lowercase alphanumeric with hyphens. On collision with an existing case file at the resolved path, ask whether to rename to slug-YYYY-MM-DD.md or resume the existing file (resuming routes to Outcome 0).

After every outcome, present what was learned and pause for the user before continuing.

Principles

  • Evidence grading.
    • Confirmed. Directly observed; cite path:line, log timestamp, or commit hash.
    • Deduced. Logically follows from Confirmed evidence; show the chain.
    • Hypothesized. Plausible but unconfirmed; state what would confirm or refute it.
  • Stronghold first. Anchor in one Confirmed piece of evidence and expand outward. Never start from a theory and hunt for support. When evidence is sparse, switch to evidence-light mode (Outcome 1 branch).
  • Challenge the premise. The user's description is a hypothesis, not a fact. Verify independently; if evidence contradicts, say so.
  • Follow the evidence, not the narrative. When evidence contradicts the working theory, update the theory — never the other way around. Resist confirmation bias even when the user is convinced.
  • Hypotheses are never deleted. Update Status (Open / Confirmed / Refuted) and add a Resolution. Wrong turns are part of the deliverable.
  • Missing evidence is itself a finding. Document the gap, what it would resolve, and how to obtain it.
  • Write it down early. Initialize the case file as soon as the slug is agreed; it is the persistent state across interruptions.
  • Path:line citations use CWD-relative format, no leading /, so they're clickable in IDE-embedded terminals.
  • Delegation discipline. When a step requires reading 5+ files or any file >10K tokens, delegate to a subagent that returns structured JSON only. Cite path:line from the result; don't re-read in the parent.
  • Issue independent operations in parallel (multi-grep, multi-read, parallel inventories) — one message, multiple tool calls.
  • Communication. Evidence-first language ("the evidence shows", "unconfirmed, requires X to verify"). No hedging, no narrative.

On Activation

Step 1: Resolve the workflow block

Run: uv run {project-root}/_bmad/scripts/resolve_customization.py --skill {skill-root} --project-root {project-root} --key workflow

If the script fails, stop and surface the error.

Step 2: Execute prepend steps

Run each entry in {workflow.activation_steps_prepend} in order.

Step 3: Load persistent facts

Treat each entry in {workflow.persistent_facts} as foundational context. file: prefixes are paths or globs under {project-root} (load contents); other entries are facts verbatim.

Step 4: Load config

Load {project-root}/_bmad/gds/config.yaml and resolve {user_name}, {communication_language}, {document_output_language}, {implementation_artifacts}, {project_knowledge}. If {implementation_artifacts} is unresolved, fall back to ./investigations/ and surface the fallback before initializing.

Step 5: Greet

Greet {user_name} in {communication_language}.

Step 6: Execute append steps

Run each entry in {workflow.activation_steps_append} in order.

Activation is complete. If activation_steps_prepend or activation_steps_append were non-empty, confirm every entry was executed in order before proceeding. Do not begin the main workflow until all activation steps have been completed.

Step 7: Acknowledge and route

Acknowledge the input as a reference (record paths and IDs; don't read raw content). Path to an existing case file → Outcome 0. Otherwise → Outcome 1.

Procedure

Outcome 0: Existing case is loaded and surfaced

Read the case file. Surface, in order: open hypotheses (Status = Open) with their confirm/refute criteria; open backlog (Status ≠ Done); missing-evidence rows; last Conclusion with confidence. Ask which thread to pull. New evidence opens a new ## Follow-up: {YYYY-MM-DD} block (append #2, #3 on same-day reentry). Pause for user with the recap above; wait for direction.

Outcome 1: Scope and stronghold are established

Acknowledge each input shape — record location, scope, time window only; bulk reads happen in Outcome 2.

  • Issue tracker ticket. Fetch full details via available MCP tools.
  • Diagnostic archive. Record path, file count, time window.
  • Log file or stack trace. Record path and time window; only the stack frame already in the user's message is in scope here.
  • Free-text description. Capture verbatim; treat as hypothesis.
  • Code area name (no symptom). Record entry point.
  • Recent commit area. Record commit range.

If the user arrived with a hypothesis, register it as Hypothesis #1. Find the stronghold independently; the user's hypothesis is one of the things the stronghold validates or refutes.

Find a stronghold: a Confirmed piece of evidence (error message, function name, HTTP route, config parameter, test case). Anchor here.

Initialize {case_file} before branching. The path is {implementation_artifacts}/{workflow.case_file_subdir}/{workflow.case_file_filename} with {slug} substituted (slug and collision rules in Overview). Create the file from {workflow.case_file_template} and fill Hand-off Brief (rough), Case Info, Problem Statement, initial Evidence Inventory.

Evidence-light branch. When no Confirmed evidence is reachable: mark the case evidence-light in the Hand-off Brief; populate the Investigation Backlog with prioritized data-collection items; record "to make progress, I need one of: …"; pause for the user to provide evidence or authorize Outcome 2 to scan more broadly.

Otherwise present scope, stronghold, file path, proposed approach. Pause for user with the recap above; wait for direction.

Outcome 2: Evidence perimeter is mapped

Survey the scene: inventory available evidence in parallel across these independent categories: diagnostic archives; issue tracker; version control; test results; static analysis; source code. For any category exceeding ~10K tokens, delegate to a subagent that returns a JSON manifest (paths, sizes, time windows, key fragments cited as path:line).

Classify each Available, Partial, or Missing — Missing is itself a finding. Update Evidence Inventory and Investigation Backlog. Pause for user with the recap above; wait for direction.

Outcome 3: Cause is reasoned about with discipline

  • Trace causality. Symptom-driven: trace backward from the symptom to producing conditions and the state that emerged. Exploration: trace backward from outputs (returns, side effects, messages sent) to producing conditions. Same technique, different anchor.
  • Reconstruct the timeline by cross-referencing logs, system events, version control, user observations.
  • Form and test hypotheses. State, identify confirming/refuting evidence, search, grade (Confirmed / Refuted / Open). Update Status. Never delete.
  • Refutation pass. Each time a hypothesis transitions toward Confirmed, actively look for refuting evidence first. Record the attempt in Resolution.
  • Verify the user's premise. If evidence contradicts, say so explicitly.
  • Add discovered paths to the backlog. Stay focused on the current thread.

Update Confirmed Findings, Deduced Conclusions, Hypothesized Paths, Backlog, Timeline. Highlight contradictions to the original premise. Pause for user with the recap above; wait for direction.

Outcome 4: Source has been traced where it matters

Issue these first-pass scans as parallel tool calls in one message: grep for exact error strings; glob the affected directory for parallel implementations; git log for recent changes.

Then sequentially: read the surrounding code; follow the caller chain; watch for language and process boundary crossings (compiled→scripts, IPC, host→device, configuration flow).

Lean by case type:

  • Exploration: I/O mapping (triggers, outputs, dependencies); frequent-terms scan; control-flow filtering (branches, loops, error handling, state-machine transitions).
  • Symptom-driven: depth assessment — is the root cause reachable from local context, or is a broader area model required? Surface escalations; never silently expand scope. Trivial-fix assessment — off-by-one, missing null check, swapped argument → one-line code suggestion or draft diff in the report; non-trivial → stop at the root cause area.

Investigation stops at the diagnosis; implementation is out of scope. Update Source Code Trace (Error origin, Trigger, Condition, Related files; area model when broader). Pause for user with the recap above; wait for direction.

Outcome 5: Report is finalized and the hand-off is clean

Update {case_file}:

  • Hand-off Brief rewritten to final form (3 sentences, 15-second read).
  • Final Conclusion with confidence: High (Confirmed root cause, deterministic repro), Medium (Deduced; minor uncertainty), Low (Hypothesized; clear data gap).
  • Fix direction when applicable (categorize by mechanism if multiple combine).
  • Diagnostic steps if uncertainty remains.
  • Reproduction Plan when applicable, or a verification plan for exploration cases.
  • Status: Active / Concluded / Blocked on evidence.

Present the conclusion, then a concrete next-steps menu: trivial fix → gds-quick-dev; scope/plan adjustment → gds-correct-course; tracked story → gds-create-story; fresh review → gds-code-review. Recommend the highest-value action. Mitigations and workarounds are generated only on explicit request — investigation stops at the diagnosis. Execute {workflow.on_complete} if non-empty. Pause for user with the recap above; wait for direction.

Follow-up Iterations

Continue work by appending to {case_file} under a new ## Follow-up: {YYYY-MM-DD} block (#2, #3 on same-day reentry). The investigation is complete when:

  • Root cause is Confirmed.
  • Root cause is Hypothesized with a clear data gap.
  • The mental model is sufficient for the user's stated goal (exploration cases).
  • The backlog contains only items requiring unavailable evidence.
  • The user explicitly concludes.

FAQ

What does the gds-investigate skill do?

Forensic case investigation with evidence-graded findings, calibrated to the input. Use when the user asks to investigate a bug, trace what caused an incident, walk through unfamiliar code, or build a mental model of a code area before working on it.

How do I install gds-investigate?

Run npx skills add https://github.com/bmad-code-org/bmad-module-game-dev-studio --skill gds-investigate in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/gds-investigate/ (personal) or .claude/skills/gds-investigate/ (project).

Which AI agents can use gds-investigate?

Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.

Is gds-investigate safe to install?

No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.

Who maintains gds-investigate?

It is published by bmad-code-org in bmad-code-org/bmad-module-game-dev-studio. It has 43 recorded installs.

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