Nw Investigation TechniquesSAFE
AI agents that guide you from idea to working code, with you in control at every step.
Overview
AI agents that guide you from idea to working code, with you in control at every step.
1a864ec81cdeOBSERVED · 2026-10-14What it tells the agent
The instruction file, verbatim from the audited commit — this is the text the model reads, and the surface the audit's instruction layer examines. Quoted here so you can judge it without cloning anything.
--- name: nw-investigation-techniques description: Evidence collection methods, problem categorization, analysis techniques, and solution design patterns user-invocable: false disable-model-invocation: true --- # Investigation Techniques ## Problem Categorization ### Technical Problems | Category | Sub-Category | Common Symptoms | |----------|-------------|-----------------| | System Failures | App crashes, memory leaks, deadlocks, data corruption | Service unavailability, resource exhaustion, integrity errors | | System Failures | Hardware, network, database, security | Connectivity loss, capacity limits, access failures | | Performance | Response time: slow queries, latency, algorithmic inefficiency | High p95/p99, user-reported slowness | | Performance | Throughput: thread pool exhaustion, connection limits, queue backlog | Reduced capacity, growing queues | | Integration | Internal: component comms, data format, version conflicts | Interface errors, serialization failures | | Integration | External: third-party availability, API changes, auth failures | Timeouts, contract violations | ### Operational Problems | Category | Common Symptoms | |----------|-----------------| | Deployment: script failures, config drift, migration errors | Failed releases, environment inconsistencies | | Monitoring: alerting gaps, backup failures, incident response | Missed incidents, slow recovery | | Human factors: communication gaps, knowledge silos, skill gaps | Repeated mistakes, slow onboarding | ## Evidence Collection ### Technical Evidence Sources **Logs**: application (timestamp correlation) | system/infrastructure | database | network traces **Metrics**: performance/resource utilization | error rates/response time trends | user behavior/transaction patterns | infrastructure health/capacity **Configuration**: system/deployment settings | code changes/VCS history (git log, blame) | env vars/dependencies | security/access controls ### Evidence Validation 1. **Cross-reference**: verify from multiple independent sources 2. **Timestamp validation**: confirm event sequence accuracy 3. **Completeness check**: identify data gaps/corruption 4. **Correlation vs causation**: distinguish co-occurrence from causation ## Analysis Techniques ### Quantitative - **Trend**: time series of metrics, error pattern frequency - **Distribution**: response time percentiles, error rate across components - **Pattern recognition**: log anomalies, behavior patterns, error clustering ### Qualitative - **Timeline reconstruction**: detailed incident timeline, correlate changes with symptoms - **Process analysis**: workflow disruptions, communication flow, decision chains - **Environmental**: recent changes, system load, external factors, related incidents ## Solution Design Patterns ### Immediate Mitigations (restore service) Quick fixes | workarounds to minimize impact | emergency procedures | monitoring enhancements ### Permanent Fixes (prevent recurrence) Architecture modifications | code quality/defensive programming | config management/environment consistency | testing/validation improvements ### Early Detection (catch faster) Leading indicators | anomaly detection/predictive alerting | automated quality gates | threshold tuning from learnings ### Solution Prioritization Matrix | Priority | Criteria | Action | |----------|----------|--------| | P0 | Active incident, users impacted | Immediate mitigation, hours | | P1 | Root cause fix for recurring issue | Permanent fix, current sprint | | P2 | Prevention for potential issues | Next sprint | | P3 | Systemic improvement | Backlog with evidence |
Trust audit
SAFEgrade B · trust 89/100 Nothing in the source contradicts what it says it does. Grade A is reserved for packages that have also passed the behavioural sandbox.
| Layer | What it checks | Result |
|---|---|---|
| L0 | Provenance & inventory | PASS |
| L1 | Static analysis of the code | NA |
| L2 | Instruction surface (what it tells the agent) | PASS |
| L3 | Class-specific surface | PASS |
| L4 | Behavioural (sandbox) | SKIPPED |
What the source does
- Filesystem
- none-observed
- Network
- none-observed
- Shell
- none-observed
- Dependencies
- pinned
- Secrets in source
- none-found
Findings (0)
No findings outside the package's declared scope.
Gates applied: no_behavioural_pass.
1a864ec81cdefull audit observations/trust-audit/skill/nwave-ai__nw-investigation-techniques.json · Report an issue / request a re-scanAudit history
Every audit this skill has had.
| Date | Source | Verdict | Grade | Score | Change |
|---|---|---|---|---|---|
| 2026-10-14 | 1a864ec81cde | SAFE | B | 89 | first audit |
Questions
What does the Nw Investigation Techniques skill do?
AI agents that guide you from idea to working code, with you in control at every step.
Is Nw Investigation Techniques safe to install?
The audit found nothing in the source that contradicts what it says it does, and graded it B (89/100). Grade A is held back for packages that have also passed a sandboxed behavioural run, which is why a clean skill reads B.
What can Nw Investigation Techniques access on my machine?
The audit observed no filesystem, network or shell use at all in its source.
How current is this page?
The grade is for one exact copy of the source (1a864ec81cde), read on 2026-10-14. The repository is watched, and a new audit runs when it changes — this is the first audit.