reproduction-experiment-investigator
Skill8 starsChanged 6 months agoMITView on GitHub
Specialized investigator for testing hypotheses through reproduction attempts. Designs experiments, executes controlled tests, and reports whether a hypothesis can be confirmed or rejected. Dispatched by debug-conductor.
In this repository129 other files it gives its agents.
- AGENTS.mdAGENTS.md
- guidance/AGENTS.mdAGENTS.md
- CLAUDE.mdCLAUDE.md
- guidance/CLAUDE.mdCLAUDE.md
- .github/copilot-instructions.mdCopilot instructions
- blast-radius-checkSkill
- brainstormingSkill
- branch-flow-gateSkill
- branch-sync-gateSkill
- codebase-reconSkill
- codeowners-drift-auditSkill
- code-review-batterySkill
- cognitive-complexity-refactoringSkill
- debateSkill
- debug-conductorSkill
- dispatching-parallel-agentsSkill
- domain-buildSkill
- evidence-adjudicatorSkill
- executing-plansSkill
- external-cli-auditSkill
- feature-developmentSkill
- field-rename-verificationSkill
- finishing-a-development-branchSkill
- git-branch-conventionsSkill
- gitlab-cliSkill
- hotfix-charterSkill
- implementation-trackerSkill
- incident-revert-reviewSkill
- infra-config-investigatorSkill
- investigation-stateSkill
- kernel-splitSkill
- llm-behavior-investigatorSkill
- llm-skill-reviewSkill
- merge-authorization-gateSkill
- micro-harsh-reviewSkill
- output-verificationSkill
- pre-commit-gateSkill
- progressive-code-review-gateSkill
- progressive-harsh-reviewSkill
- providing-code-reviewSkill
- pr-triage-gateSkill
- push-authorization-gateSkill
- receiving-code-reviewSkill
- requesting-code-reviewSkill
- requirements-validation-pmSkill
- requirements-validationSkill
- scope-tripwireSkill
- session-handoffSkill
- skills-hierarchy-tuningSkill
- sp-bughuntSkill
- state-consistency-investigatorSkill
- subagent-driven-developmentSkill
- systematic-debuggingSkill
- test-driven-developmentSkill
- timeline-trace-investigatorSkill
- token-estimationSkill
- unified-commit-gateSkill
- using-git-worktreesSkill
- using-superpowersSkill
- verification-before-completionSkill
- The first 60 shown.
The file
We split this skill into the rules it gives an agent
5 rules. About 0 are common, 5 are distinctive, and 0 are very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈5
Distinctive · estimate
This author's own choices. Worth a look.
0
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Distinctive: line 55
Document ALL differences — any could explain failure to reproduce
- Distinctive: line 56
If significant differences exist:
- Distinctive: line 58
If not, document the gap as a limitation
- Distinctive: line 85
If failure disappears → strong confirmation
- Distinctive: line 86
If failure persists → hypothesis may be wrong or incomplete
≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
Reproduction & Experiment Investigator
Role: Test debugging hypotheses through controlled reproduction attempts. Confirm or reject with evidence. Dispatched by:
debug-conductor— never invoked directly by user. Evidence type:ExperimentEvidence(seeskills/_shared/evidence-schema.md)
When to Use
Dispatched by debug-conductor when a hypothesis needs testing — controlled reproduction, environment comparison, or A/B verification of a suspected root cause.
Investigation Protocol
Step 1: Receive and Refine Hypothesis
From the conductor, receive:
- Hypothesis: "The failure is caused by [X] when [condition Y] is present"
- Predicted outcome: "If hypothesis is correct, we expect [Z] when we [action]"
- Environment: Where to reproduce (staging, local, isolated sandbox)
Refine into a testable experiment:
- Define exact steps to reproduce
- Define exact success/failure criteria (not subjective)
- Identify minimum reproduction conditions (strip unnecessary variables)
Step 2: Environment Assessment
- Compare reproduction environment to production:
- Same versions? Same config? Same data? Same load?
- Document ALL differences — any could explain failure to reproduce
- If significant differences exist:
- Can we close the gap? (deploy same version, copy config)
- If not, document the gap as a limitation
Step 3: Execute Controlled Reproduction (3+ attempts)
For each attempt:
- Reset environment to clean state
- Apply the hypothesized condition
- Execute the triggering action
- Record: did the expected failure occur?
- Record: any unexpected behavior?
Minimum 3 attempts — intermittent bugs need statistical confidence.
| Attempts | Reproductions | Confidence |
| 3/3 | 3 | High (>0.8) — hypothesis strongly supported |
| 2/3 | 2 | Medium (0.5–0.8) — likely correct but intermittent |
| 1/3 | 1 | Low (0.3–0.5) — possible but unreliable |
| 0/3 | 0 | Very Low (<0.3) — hypothesis likely wrong OR environment mismatch |
Step 4: Control Experiment
If reproduction succeeded:
- Remove the hypothesized condition
- Re-run the same triggering action
- If failure disappears → strong confirmation
- If failure persists → hypothesis may be wrong or incomplete
Step 5: Produce Evidence
{
"hypothesis": "Event ordering bug in async pipeline under load",
"steps": [
{ "action": "Set event processing to async mode", "result": "Config applied", "success": true },
{ "action": "Send 50 concurrent call events", "result": "Events arrived out of order in 12/50 cases", "success": true },
{ "action": "Verify call state machine diverged", "result": "3 calls in disconnected state prematurely", "success": true }
],
"outcome": "reproduced",
"reproduced": true,
"attempts": 3,
"successRate": 1.0,
"environmentDiff": "Staging uses lower load (50 concurrent vs 500 in prod); reproduction rate may differ"
}Stop Conditions
- Hypothesis confirmed or rejected with ≥3 attempts
- Reproduction rate established
- Environment assessment shows unbridgeable gap (cannot reproduce here)
- Token budget exhausted
- Wall-clock limit (5 minutes)
Escalation Conditions
- 0/3 reproduction despite matching environment → hypothesis may be wrong; tell conductor
- Intermittent reproduction (<50%) → flag as timing-dependent; may need more attempts or different conditions
- Environment mismatch prevents reproduction → flag as "cannot test here"; need production access or closer replica
Common Patterns This Investigator Detects
| Pattern | Evidence Shape |
| Deterministic bug | 3/3 reproduction, 0/3 without condition → confirmed |
| Load-dependent bug | Reproduces only above certain concurrency threshold |
| Environment-specific | Reproduces in prod-like environment but not staging → config/infra difference |
| Intermittent / race condition | 1–2/3 reproduction → timing-dependent |
| Hypothesis disproven | 0/3 reproduction even with condition → reject hypothesis |
Failure Modes
| Mode | Symptom | Recovery |
| Incomplete isolation | Test affected by shared state | Reset environment between experiments |
| False confirmation | Coincidental success in reproduction | Run multiple trials |
| Wrong variable | Testing irrelevant hypothesis | Verify hypothesis matches symptoms |
Does it work?
Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.
No reviews yet
People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.
- It worked
- Partly
- It didn't
Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.
Your trail
Files you open will collect here, so you can find them again.
Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.
Now do this for your own.
Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.

