harness-router
Skill0 starsChanged 7 months agoMITView on GitHub
Use when deciding where session learnings, user corrections, agent workflow improvements, or harness enhancement proposals should be persisted across agent instructions, skills, docs, work records, memory, checks, and structured local artifacts.
In this repository11 other files it gives its agents.
- AGENTS.mdAGENTS.md
- CLAUDE.mdCLAUDE.md
- attack-architectureSkill
- convention-engineeringSkill
- go-scriptingSkill
- paper-vettingSkill
- skill-builderSkill
- skill-cli-authoringSkill
- slock-epic-protocolSkill
- taskfile-authoringSkill
- work-cliSkill
The file
We split this skill into the rules it gives an agent
6 rules. About 0 are common, 4 are distinctive, and 2 are very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈4
Distinctive · estimate
This author's own choices. Worth a look.
2
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Distinctive: line 13
Use this skill when the task is to:
- Distinctive: line 22
Do not use this skill for:
- Very specific: line 43
If the request mines agent session history for reusable value, read references/session-workflow-packaging.md.
- Distinctive: line 56
Ask for approval before changing instructions, skills, hooks, CI, evals, or memory.
- Distinctive: line 76
Default output is a reviewable proposal in a hybrid layout — a narrow Recommendations index table followed by one section per …
- Very specific: line 86
Use the full layout (index table + per-recommendation sections + Not Promoted table) from references/proposal-format.md.
≈ “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
Harness Router
Route useful session learnings into durable, reviewable agent knowledge surfaces without silently rewriting them.
Scope
Use this skill when the task is to:
- Decide where new agent/harness knowledge should live after a session.
- Turn user corrections or repeated agent mistakes into an enhancement proposal.
- Propose updates across agent instruction files, skills, docs, work records, memory, hooks, CI, evals, or structured local artifacts.
- Review whether a learning is durable enough to promote from chat/session context into a versioned artifact.
Do not use this skill for:
- Directly editing a requested target when the destination is already clear.
- Generic research summarization without a persistence decision.
- Creating or refactoring a skill's content; use
skill-builder. - Operating
.work/; usework-cli. - Changing repo convention contracts; use
convention-engineering.
MCP is out of scope as a durable destination for this skill.
First Actions
- Identify the session learning, correction, or proposed enhancement.
- Gather only the nearby evidence needed to classify it:
- user correction or session summary
- changed files and tests
- active work item or research space
- relevant agent instruction files
- related or used skills
- Read
references/externalization-model.md, thenreferences/routing-taxonomy.md. - If the request mines agent session history for reusable value, read
references/session-workflow-packaging.md. - Produce a proposal before making durable edits.
Operating Loop
- Extract candidate learnings in compact form.
- Classify the externalized burden and artifact class using
references/externalization-model.md. - Classify authority, scope, durability, load policy, consumer, enforcement need, sensitivity, provenance, and cost.
- Pick candidate target surfaces using
references/target-surfaces.md. - Format recommendations with
references/proposal-format.md. - Ask for approval before changing instructions, skills, hooks, CI, evals, or memory.
Routing Rule
- Route by externalized burden first, then by durable surface.
- Required always-on repo behavior goes to the repo agent instructions; use harness adapter files only when runtime-specific guidance is needed.
- Repeatable procedures go to skills or skill reference files.
- Interaction contracts go to protocol owners such as CLI contracts, schemas, adapters, Taskfiles, generated-surface rules, or convention docs.
- Bulky durable knowledge goes to docs or research pages.
- Active follow-up goes to
.workrecords and spaces. - Personal low-authority context goes to memory.
- Hard guarantees go to hooks, settings, CI, lint, or evals.
- Temporary state stays in session context, compaction, sandbox/workspace files, or run logs unless it proves durable value.
Output
Default output is a reviewable proposal in a hybrid layout — a narrow Recommendations index table followed by one section per recommendation:
- short summary of the durable lesson
- recommended destination(s)
- externalized burden and artifact class
- compact proposed change
- compact evidence and reasoning tied to routing dimensions
- confidence and risks
Use the full layout (index table + per-recommendation sections + Not Promoted
table) from references/proposal-format.md. Add the structured YAML block
only when another tool or agent will parse the result.
Gaps
This is a proposal skill. It does not yet own a script, CLI, load verifier, or automatic mutation workflow. Add code only after the proposal pattern has worked across multiple real sessions.
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.

