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/radar

@e307415
by shingo imotasimota/agent-skills85 stars
15

Adding edge-case tests, repairing flaky tests, and improving coverage. Use when test gaps need filling or regressions need guarding. Supports JS/TS, Python, Go, Rust, and Java.

Use this Skill: https://skilld.dev/gh/simota/agent-skills/radar

This session only. Nothing lands on disk.

referencetest-dataanonymization.md

≈1.6k tokens on demand. Your agent reads this file only when SKILL.md points to it.

Test-Data De-identification Contract

Read when a fixture or replay dataset might contain production-derived or identifying data. Prefer schema-generated fixtures; production-derived replay requires the admission and release gates below.

Admission and Ownership

  • Classify source columns, free text, nested objects, metadata, and quasi-identifier combinations before export. Production access or an existing dump does not authorize transfer to CI, an LLM, a laptop, or a third party.
  • Transform inside the approved protected boundary. Project an allowlisted output schema; never spread a real record and replace only known PII fields. Omit unclassified fields until reviewed.
  • Radar owns the test dataset and utility evidence. Unresolved privacy design → Cloak; regulatory conclusions → Canon; load-volume generation → Siege. No masking method, synthetic label, or metric alone authorizes public sharing or proves legal anonymization.
  • Suppress real credentials, password hashes, government identifiers, payment PANs, and unnecessary sensitive fields. Use reserved/provider test values for validators; do not send real or transformed production payment details to a processor.

Select the Minimum Sufficient Transformation

Test need Mechanism Required check
No production-specific behavior Generate from schema and explicit edge cases No real input rows or unapproved training data; valid constraints
Stable PK/FK relationships Keyed HMAC tokenization with a consistent namespace per join domain Canonical input encoding, stable key version, collision detection, all related tables transformed together
Display-only fields Synthetic replacements in the required locale Reserved contact destinations; no outbound delivery; distribution sufficient for the tests
Unused sensitive field Suppression No passthrough through JSON, logs, snapshots, or free text
Aggregate behavior Generalization or a reviewed statistical mechanism Explicit utility tolerance and measured re-identification risk
Strict shape validator Reserved synthetic values first; otherwise reviewed domain-aware tokenization Length/charset and checksum constraints; reversibility and key access documented

HMAC is pseudonymous linkage, not a claim of anonymous data. Plain hashes, public salts, partial email/phone masks, and normalization do not establish a privacy floor. Keep keys outside fixtures and logs; key rotation requires re-issuing the linked dataset. Do not silently truncate tokens without a collision budget and rejection test.

Do not copy an FPE implementation or choose a cipher from this file. Check the current NIST specification and the installed audited library for the required domain; FPE does not inherently preserve check digits. The NIST revision-1 second public draft removed FF3/FF3-1; this is a draft status, not a claim that a final revision has been published.

Statistical Release Gates

Preserve the repository's screening defaults only as local risk-review inputs, not legal guarantees:

Metric Local screening default Measure, do not infer
k-anonymity k ≥ 5 internal; k ≥ 10 external candidate Minimum equivalence-class size across the joint quasi-identifier set; an age bucket width is not k
l-diversity l ≥ 3 Sensitive-value diversity within each equivalence class
t-closeness t ≤ 0.2 Declared distance metric and reference distribution; do not compare unlike definitions

External release still requires approval and a linkage/re-identification assessment. A dataset passing these screens can remain identifying. For differential privacy, require a reviewed mechanism, adjacency definition, bounded contribution/sensitivity, randomness implementation, declared ε/δ, and composition accounting. A toy noise function, Faker output, or an LLM-generated dataset is not DP evidence. Do not invent a universal ε budget.

Validation Before Release

  • Verify the allowlisted output schema and scan all fields, free text, logs, artifacts, and snapshots. Check known source identifiers and join-based leakage; a regex or a known-domain check alone cannot prove absence of PII.
  • Validate PK/FK/unique constraints, token collisions, nullability, and consuming assertions. Record the utility floor and measured distribution/edge-case coverage; do not preserve identifiable outliers merely to match a histogram.
  • Verify no transformation secret, reversible lookup table, raw source sample, or real contact endpoint ships with the dataset. Keep any necessary mapping separately under the source boundary's access controls.
  • Reject on unresolved privacy evidence, expired retention, or failed utility tests; do not weaken the privacy gate to make a fixture pass. Escalate a utility/privacy conflict to Cloak.

Retention and Handoff

These are repository defaults; a stricter approved source policy wins. Every dataset carries generated_at, expires_at, source authorization, schema version, transformation/key version identifiers (never key values), per-column technique, validation evidence, and permitted recipients/use. Consumers reject expired datasets.

Horizon Storage control
≤ 24 hours In-memory only; no disk or backup
≤ 7 days Encrypted volume, automatic deletion, no laptop copies
≤ 30 days Encrypted, access-logged storage and periodic rescan
> 30 days Explicit privacy-owner approval with a new expiry; re-masking alone does not extend permission

Handoff includes the fixture set, utility limitations, privacy-test definitions/results, retention policy, and deletion responsibility. Siege may scale only the approved output; Canon receives evidence, not an unsupported compliance verdict.

Canonical Checks

Source entrypoints checked 2026-09-17. Offline: retain the declared controls and report missing specification/release evidence; never infer approval.

Source: SKILL.md on GitHub

1 warning13d5 checks · Risk SAFE
  • Gen Agent Trust Hub13d

    The 'radar' skill is a comprehensive testing assistant designed to improve code reliability through various testing methodologies such as unit, integration, and mutation testing. It follows industry best practices and uses standard tooling. A low-severity finding is noted regarding the inherent risk of indirect prompt injection when the agent processes project source code, which is mitigated by strong requirements for test isolation and independent verification.

  • Socket13d

    No alerts

  • Snyk13d

    Risk: LOW · No issues

  • Runlayer6mo

    4/14 files flagged

  • ZeroLeaks5mo

    Score: 93/100 · 2 sections analyzed

Signed by skilld at e307415. This ties the file your Agent reads to that commit on GitHub. It does not review the instructions.

Last checked against GitHub 2 days ago.

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