agent-run-supervision
Skill2 starsChanged 8 days agoMITView on GitHub
Supervise a live tool-calling agent run with ceilings, no-progress detection, and a human barrier. Use when asked to watch, cap, or stop a worker that calls tools. Do not use for session retrospectives, skill-file review, or a pentest runner.
In this repository21 other files it gives its agents.
- AGENTS.mdAGENTS.md
- agent-artifactSkill
- agent-discoverySkill
- api-contract-migrationSkill
- changelog-updateSkill
- change-reviewSkill
- ci-reviewSkill
- claims-auditSkill
- dependency-auditSkill
- derivation-auditSkill
- durable-cacheSkill
- engineering-auditSkill
- infer-conventionsSkill
- operational-signal-intakeSkill
- planned-releaseSkill
- preferred-stackSkill
- pressure-benchmarkingSkill
- public-surface-reconSkill
- rfc-processSkill
- security-auditSkill
- session-reviewSkill
The file
We split this skill into the rules it gives an agent
2 rules. About 0 are common, 1 is distinctive, and 1 is very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈1
Distinctive · estimate
This author's own choices. Worth a look.
1
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Very specific: line 40
Write durable findings under docs/issues per docs-conventions.
- Distinctive: line 49
When a named skill is unavailable, preserve the finding and name the deeper review.
≈ “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
Agent-run supervision
Supervise a live tool-calling run. This skill does not replace session-review or agent-artifact. It owns ceilings and barriers while the worker is running.
Quick reference
name isolation -> set tool-call and wall-clock caps -> detect identical calls -> request approval or stop -> measure supervision costCeilings
Set hard tool-call and wall-clock ceilings. Detect identical calls and lack of progress. Stop gracefully when a cap is hit. Bound generated plans. Refine after each completed step.
When the worker may invoke agents, set a total invocation budget before work starts. Count retries and nested agents against the same budget. Reserve the final portion for an independent critic or validator; hunting may not consume that reserve. Default to the smallest budget that covers one reconnaissance pass, disjoint work owners, and one validator. A malformed result gets at most one bounded retry.
Barriers
Distinguish the human-approval state (ask) from the terminal state (done). State whether supervision is enabled by default. Record each supervisor's measurements and cost. Claims that extra supervisors improve quality by a multiplier stay inference until a benchmark demonstrates the effect.
Also distinguish complete from terminal incomplete. A cap, missing approval, malformed result after the retry, unavailable validator, or lost authorization ends as incomplete with the reason and unfinished work. Do not rename an orderly stop as successful completion.
Isolation
Name what the worker can touch when prompt injection succeeds: host runtime socket, extra capabilities, host network, downloaded binaries. A container is not isolation if it holds the runtime socket. Untrusted target content can steer tools.
Context compaction can drop constraints, authorization, or a do-not-touch rule. Ask whether compacted history is still the contract.
Record
Write durable findings under docs/issues per docs-conventions. Record the invocation maximum, used count, reserved validation count, retries, terminal state, and incomplete reason. Tool-call and token counts belong in engineering-audit resource-and-budget.
Routing
agent-artifactfor published skill files and MCP configs.engineering-auditlearned-systems for in-product agents.session-reviewafter the run, over transcripts.public-surface-reconfor authorized blackbox HTTP; do not chain this skill to exploit tools.
When a named skill is unavailable, preserve the finding and name the deeper review.
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.

