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SKILL verified MIT Self-run

Debug With Evidence

skill-himanshi-28-coding-agent-skills-debug-with-evidence · by himanshi-28

Explicit-invocation workflow for systematic debugging that reproduces failures, forms falsifiable hypotheses, collects runtime evidence, proves root causes, adds regression tests, and verifies targeted fixes. Use only when the user directly invokes the debug-with-evidence skill.

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Install

$ agentstack add skill-himanshi-28-coding-agent-skills-debug-with-evidence

✓ scanned · ✓ verified, works with Claude Code, Cursor, and more.

Security review

✓ Passed

No 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.

View the full security report →

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Reliability & compatibility

Security review passed
0 installs to date
no reviews yet
2mo ago

Declared compatibility

Claude CodeClaude Desktop

Compatibility is declared by the source manifest. End-to-end runtime verification is coming, see below.

Preview Execution monitoring

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About

Debug with Evidence

Find and prove the root cause before changing behavior. A disappearing symptom is not sufficient evidence of a correct fix.

Workflow

  1. Define the failure.
  • Record observed behavior, expected behavior, exact inputs, environment, frequency, timeline, and recent relevant changes.
  • Separate reported facts from assumptions.
  1. Establish a reliable reproduction.
  • Reproduce with the smallest safe case available.
  • Preserve the failing command, request, data shape, or user steps.
  • If reproduction is impossible, identify the missing evidence instead of guessing.
  1. Trace the relevant code path.
  • Inspect applicable instructions, entry points, state transitions, dependencies, error handling, and existing tests.
  1. Form ranked hypotheses.
  • Make each hypothesis specific and falsifiable.
  • State the observation that would support or reject it.
  1. Collect targeted evidence.
  • Use existing logs, stack traces, test output, browser state, query plans, profiles, metrics, or version history.
  • Add minimal temporary instrumentation at suspected boundaries when needed.
  • Change one investigative variable at a time.
  1. Identify the root cause.
  • Explain the causal chain from triggering condition to observed failure.
  • Show why the evidence favors it over competing hypotheses.
  • Reject symptom-masking fixes such as an unexplained null check or broad retry.
  1. Lock a regression test.
  • Add the smallest test that reproduces the bug and confirm it fails for the proven reason before applying the fix.
  • Use the lock-tests-first skill when the contract requires broader coverage.
  1. Apply a targeted fix.
  • Correct the cause with the smallest coherent change.
  • Preserve unrelated behavior and remove temporary instrumentation unless it provides lasting observability value.
  1. Verify.
  • Re-run the reproduction and regression test, then relevant surrounding checks.
  • Test boundary and failure cases suggested by the root cause.
  • Explain why the fix prevents recurrence rather than hiding the symptom.

Output

Provide the reproduction, evidence, root cause, rejected hypotheses, fix, regression protection, verification results, and residual uncertainty. Never claim a cause solely because a patch made the immediate error disappear.

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

Install and usage instructions live in the source repository linked above.

Reviews

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Versions

  • v0.1.0 Imported from the upstream source.