cause and effect timeline analyzer
Reconstruct fragmented logs and events into a rigorous, evidence-based causal timeline.
$5
Works with the AI Agents you already use
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Grok Bot+18 agentscause and effect timeline analyzer
Example session with this skill installed
Analyze these events: 09:00 system update, 09:05 DB latency spikes, 09:10 dev team decides to restart, 09:12 site down, 09:15 rollback starts. Some say the update caused it, others blame the DB.
- Read your context and instructions
- Compiled the cause and effect
I have reconstructed the event sequence and analyzed the causal relationships. The primary driver appears to be the system update acting as a trigger for existing database vulnerabilities.
Causal Analysis Report
| Order | Timestamp | Event | Role | Source | Confidence | Connected To | Nature |
|---|---|---|---|---|---|---|---|
| 1 | 09:00 | System Update | Trigger | Input | High | 2 | Direct Causation |
| 2 | 09:05 | DB Latency Spikes | Consequence | Input | High | 3 | Contributing Factor |
| 3 | 09:10 | Decision to Restart | Decision | Input | High | 4 | Direct Causation |
| 4 | 09:12 | Site Down | Consequence | Input | High | 5 | Sequence |
| 5 | 09:15 | Rollback Starts | Turning Point | Input | High | - | - |
Causal Chain Summary
Primary Causal Chain: The System Update (1) directly triggered DB Latency Spikes (2). The decision to Restart (3) while the DB was under stress led to the Site Down (4) state. The Rollback (5) was the corrective turning point.
- Alternative Explanations:
- Hypothesis A: The DB spike was coincidental and caused by an external load. Rejection: The timing immediately following the update makes this statistically unlikely (post hoc ergo propter hoc, but supported by proximity).
- Hypothesis B: The Restart caused the crash, not the update. Retention: The restart is a high-probability direct cause for the final outage, whereas the update was the trigger for the initial instability.
Evidence Gaps: Missing specific DB logs to confirm if the update changed a specific schema or config that the DB rejected.
*
Confidence Assessment:
High. The temporal sequence is tight and the roles of each actor are clearly defined in the input.
Next steps
- Review DB logs from 09:00 to 09:05 to identify the specific query or config change that triggered the latency.
- Evaluate the "Restart" protocol to determine why it resulted in a total site outage rather than a graceful recovery.
- Verify if the Rollback successfully cleared the latency spikes.
Connects securely to your tools. The creator never sees your data.
About this skill
The problem
Fragmented logs, conflicting testimonies, and incomplete event data make it difficult to determine why a failure occurred. Teams often confuse temporal sequence with actual causation, leading to ineffective post-mortems and recurring incidents.
What it does
- Normalizes messy event data into a unified, UTC-aligned temporal anchor list.
- Categorizes events as Decisions, Triggers, Contributing Factors, Consequences, or Turning Points.
- Maps relationships between events, distinguishing between sequence, correlation, and direct causation.
- Tests alternative hypotheses and applies Occam's razor to rank the most plausible causal chains.
- Identifies evidence gaps and explicitly flags the confidence level of every causal claim.
Why this beats prompting it yourself
Generic prompts often hallucinate connections to create a cohesive story. This skill enforces a multi-phase analytical rigor that prioritizes evidence over narrative. It forces the identification of gaps and the evaluation of alternative explanations that simple prompts usually skip.
Use cases
- Conducting technical incident retrospectives or root cause analyses (RCA).
- Reconstructing historical project sequences from scattered Slack messages and Jira tickets.
- Analyzing complex system failures where multiple contributing factors overlap.
- Auditing decision-making processes to identify critical turning points in a project lifecycle.
Known limitations
Requires an initial set of raw event data or logs to function. It cannot invent missing evidence but will explicitly flag where data is absent.
Requires raw event data or logs to begin. Cannot verify external truth, only internal consistency and evidential fit. Confidence levels depend entirely on source quality.
How to use
- 1
Connect your agent to the Agensi MCP
Copy the prompt and paste it into Claude, ChatGPT, Cursor or any agent that supports MCP. It sets up the connection for you. Manual setup
- 2
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- 3
Ask for what you need
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