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Tirth8205/Review Changes — measured AI agent skill | DecimalAI

tirth8205/review-changes

NewSkillScoreMaintained

Perform a structured code review using change detection and impact

by tirth8205

Links this skill to your agents and keeps it current with the author’s updates.

Automatic

Coding pack

decimalai skills pull tirth8205-review-changes

Get this skill — curl · raw file

curl -fsSL https://app.decimal.ai/s/tirth8205-review-changes/SKILL.md

Raw file: /s/tirth8205-review-changes/SKILL.md ↗ · machine-readable summary at /s/tirth8205-review-changes.json ↗

In productionGlobal

how this skill behaves on real traffic · last 30 days

No production data for this skill yet — usage numbers appear here once agents start running it.

Anonymized aggregate across every org using this skill — proof from real production use, not a demo.

Does this skill help? · Benchmark: with vs. withoutsign in to run this on your agents

22 test cases from the publisher’s eval.yaml · each run twice on gemini-3.6-flash — with the skill injected, then without · last run 15h ago

—-29% tokens0% turns27% of whole cases pass with skill (6/22)

Test case	Without → With	Effect	Δ tokens	Δ turns
case-04	✗→✓	▲ Improved	-32%	0%
case-07	✗→✓	▲ Improved	-65%	0%
case-08	✗→✓	▲ Improved	-61%	0%
case-10	✓→✗	▼ Worse	-81%	0%
case-21	✓→✗	▼ Worse	-52%	0%

Showing 5 of 22 cases

SkillSafety

✓ 3 of 3 passed

Code scan15 of 15 checks clean

0 critical · 0 warning · 0 info — scanner v4 — 8/3/2026

live_secretremote_code_executionreverse_shelldata_exfiltrationssrf_metadataagent_snoopinganti_refusaldestructive_commandsobfuscated_unicodeunicode_homoglyphopaque_blobsuspicious_urltool_overreachtrigger_abuseprompt_injection_phrasing

IntentSafe

LLM judge · does the skill do what it claims

ContentClean

LLM judge · policy & harmful-content review

Source

Imported

tirth8205 / code-review-graph

skills/review-changes/SKILL.md

View on GitHub ↗

License MITImported 8/3/2026Version v1

Skill contents

SKILL.md

Review Changes

Perform a thorough, risk-aware code review using the knowledge graph.

Steps

Run detect_changes_tool to get risk-scored change analysis.
Run get_affected_flows_tool to find impacted execution paths.
For each high-risk function, run query_graph_tool with pattern="tests_for" to check test coverage.
Run get_impact_radius_tool to understand the blast radius.
For any untested changes, suggest specific test cases.

Output Format

Provide findings grouped by risk level (high/medium/low) with:

What changed and why it matters
Test coverage status
Suggested improvements
Overall merge recommendation

Token Efficiency Rules

ALWAYS start with get_minimal_context(task="<your task>") before any other graph tool.
Use detail_level="minimal" on all calls. Only escalate to "standard" when minimal is insufficient.
Target: complete any review/debug/refactor task in ≤5 tool calls and ≤800 total output tokens.

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Other measured skills in the registry, with their headline benchmark lift.