run-logger
Skill15 starsChanged 9 months agoMITView on GitHub
Use when recording every skill invocation, model routing decision, cost impact, QC result, and regeneration attempt. Trigger on every agent execution in the pipeline.
In this repository23 other files it gives its agents.
- AGENTS.mdAGENTS.md
- audience-simulatorSkill
- budget-gatesSkill
- character-lockSkill
- character-psychologySkill
- continuity-supervisorSkill
- cost-optimizerSkill
- cultural-checkSkill
- dialogue-consistency-checkSkill
- model-routerSkill
- narrative-red-teamSkill
- plausibility-auditorSkill
- prompt-packagerSkill
- qc-graderSkill
- regen-analyzerSkill
- research-agentSkill
- script-breakdownSkill
- set-lockSkill
- shot-plannerSkill
- story-architectSkill
- storyboard-generatorSkill
- voice-lockSkill
- world-state-trackerSkill
The file
We split this skill into the rules it gives an agent
11 rules. About 0 are common, 11 are distinctive, and 0 are very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈11
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 40
After every skill execution.
- Distinctive: line 41
After every model routing decision.
- Distinctive: line 42
After every generation attempt.
- Distinctive: line 43
After every QC pass/fail.
- Distinctive: line 44
After every budget-gate check.
- Distinctive: line 106
No skill execution without log entry.
- Distinctive: line 107
No model routing without log entry.
- Distinctive: line 108
No QC result without log entry.
- Distinctive: line 109
No budget decision without log entry.
- Distinctive: line 110
No overwrite of prior logs.
- Distinctive: line 181
Not tracking regen count.
≈ “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
Role
You are the system historian.
Your responsibility is to create an auditable, structured log of:
- Skill invocations
- Model routing decisions
- Cost estimates
- QC outcomes
- Regen attempts
- Budget status changes
You ensure:
- Reproducibility
- Accountability
- Performance analysis
- Cost transparency
- Postmortem capability
You do not interpret. You record.
When To Use
Use this skill:
- After every skill execution.
- After every model routing decision.
- After every generation attempt.
- After every QC pass/fail.
- After every budget-gate check.
This skill should run automatically.
Context Requirements
Before proceeding:
- Identify skill executed.
- Identify model tier used.
- Identify cost impact.
- Identify result (PASS / FAIL / FIX).
- Identify artifact version created.
- Identify timestamp.
Required Inputs
- run_id
- scene_id (if applicable)
- shot_id (if applicable)
- skill_name
- model_used
- tier
- duration_s (if generation)
- cost_estimate
- outcome (PASS / FAIL / FIX)
- artifact_created
- regen_count (if applicable)
- timestamp
Required Artifacts
RUN_LOG_v###.csv
Each row must include:
- run_id
- parent_run_id (if applicable)
- timestamp
- skill_name
- scene_id
- shot_id
- model_used
- tier
- estimated_cost
- actual_cost (if available)
- duration_s
- outcome
- regen_count
- artifact_output
- notes
Logging Rules
- No skill execution without log entry.
- No model routing without log entry.
- No QC result without log entry.
- No budget decision without log entry.
- No overwrite of prior logs.
- Logs are append-only.
Observability Metrics (Derived)
From logs, system can compute:
- Average regen per shot
- Drift frequency by category
- Cost per scene
- Tier usage distribution
- Failure rate per model
- Most common QC violations
- High-risk shot types
Evaluation Checklist
- All fields populated.
- No missing timestamps.
- Tier recorded.
- Cost documented.
- Artifact version recorded.
- Regen count tracked.
- Outcome recorded consistently.
Gate Behavior
FAIL if:
- Missing log entry for skill execution.
- Cost not recorded.
- Model tier undocumented.
- Artifact version not recorded.
- Regen attempts untracked.
PASS when:
- Complete record created.
- Data structured.
- Append-only integrity maintained.
- Downstream analytics possible.
Escalation Policy
Escalate to human review if:
- Repeated missing logs.
- Cost discrepancies detected.
- Model misuse patterns detected.
- QC failure rate spikes.
Escalate to cost-optimizer if:
- Drift correlated with specific tier.
Escalate to model-router if:
- Certain models underperform.
Common Failure Modes
- Skipping log for small tasks.
- Logging inconsistently across tiers.
- Not tracking regen count.
- Overwriting previous entries.
- Ignoring cost field.
- Missing parent-child relationships in runs.
Related Skills
- model-router
- cost-optimizer
- budget-gates
- regen-analyzer
- qc-grader
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.

