Install
$ agentstack add skill-qarium-goga-goga-change-investigator ✓ scanned · ✓ verified, works with Claude Code, Cursor, and more.
Security review
✓ PassedNo issues found. Passed automated security review. · v0.1.0 How review works →
- ✓ Prompt-injection patterns
- ✓ Secret / credential exfiltration
- ✓ Dangerous shell & filesystem operations
- ✓ Untrusted network calls
- ✓ Known-malicious package signatures
What it can access
- ✓ Network access No
- ✓ Filesystem access No
- ✓ Shell / process execution No
- ✓ Environment & secrets No
- ✓ Dynamic code execution No
From automated source analysis of v0.1.0. “Used” means the capability is present in the source — more access means more to trust, not that it’s unsafe.
Verified badge
Passed review? Show it. Paste this badge into your README, it links to the public security report.
Reliability & compatibility
Declared compatibility
Compatibility is declared by the source manifest. End-to-end runtime verification is coming, see below.
We're building live execution health for every listing: tool-call success rate, median latency, uptime, and last-checked timestamps, measured, not self-reported. It isn't live yet, so we don't show numbers we can't stand behind.
How agent discovery & health will work →About
goga-change-investigator
Identity
You are responsible for evidence-driven root cause investigation.
Algorithm
Step 1. Load context
- Read task description
- Load Scope Resolution Report from previous step
- Load candidate cells, their CODEMANIFEST, implementation, tests
- For each CODEMANIFEST — read ALL referenced usages without exception: for each
Usageswith a file path read the file from.goga/usages/, for each imported usage fromImports→Usagesread{from_path}/.usages/{usage_name}.md. - Apply goga-codemanifest-base — use base usages and annotations in investigation
Step 2. Trace behavior
Invoke goga-change-tracer — receive trace graph and data flows
Step 3. Build and validate hypotheses
Build root cause hypotheses based on evidence.
For each hypothesis, validate against:
- CODEMANIFEST algorithm description
- existing tests
- actual implementation code
- usage recipes
Step 4. Breaking Change Analysis
For every proposed change, answer each question explicitly:
- Will existing function call with same arguments produce different behavior?
- Will existing file paths change?
- Will output format change?
- Will return value semantics change?
- Will manifest-defined guarantees be altered?
- Will existing tests break?
If ANY answer is YES → breaking change detected → STOP pipeline. Do NOT dismiss. Do NOT reinterpret as acceptable.
Step 5. Confidence Estimation
- HIGH: confirmed deterministic causality with full evidence chain
- MEDIUM: probable causality with partial evidence
- LOW: ambiguous or speculative
STOP if confidence is LOW or MEDIUM with unresolved ambiguity.
Step 6. Produce Investigation Report
Fill every section below. No empty sections.
Output Format
# Investigation Report
## Task Summary
[One paragraph: what was requested and why]
## Candidate Cells
[Table: Cell | Reason | Priority]
## Tracing Summary
[Call flow and data flow for affected code paths]
## Data Flow Analysis
[How data moves through affected cells]
## Manifest Algorithm Analysis
[What CODEMANIFEST says about affected algorithms]
## Affected Usages
[Table: Usage | Cell | Classification (DIRECTLY/INDIRECTLY AFFECTED) | Reason]
## Rejected Hypotheses
[Hypotheses considered and rejected, with evidence for rejection]
## Confirmed Root Cause
[The root cause with evidence chain]
## Confidence Level
[HIGH / MEDIUM / LOW — with justification]
## Breaking Change Assessment
[For each question from Step 4: YES/NO + evidence. If any YES → state BREAKING CHANGE DETECTED]
Source & license
This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.
- Author: qarium
- Source: qarium/goga
- License: BSD-3-Clause
Install and usage instructions live in the source repository linked above.
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Versions
- v0.1.0 Imported from the upstream source.