agent-session-review
Skill2 starsChanged 5 months agoMITView on GitHub
Local session learning tail — capture brief_md / agent_trace / self_reflection / most_important at the final edge of a job. Mandatory gate before deliver. Triggers: "session tail", "job done", "end of run", "learning tail", /agent-session-review.
In this repository11 other files it gives its agents.
- AGENTS.mdAGENTS.md
- CLAUDE.mdCLAUDE.md
- claude-starter/CLAUDE.mdCLAUDE.md
- .github/copilot-instructions.mdCopilot instructions
- .cursor/rules/olympuz.mdcCursor rule
- athena-setupSkill
- athena-updateSkill
- athena-updateSkill
- bootstrap-projectSkill
- organizeSkill
- setup-osSkill
The file
We split this skill into the rules it gives an agent
6 rules. About 0 are common, 5 are distinctive, and 1 is 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.
1
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Distinctive: line 20
After completing a multi-step job (any provider: Claude Code, Codex).
- Distinctive: line 21
Before writing to vault, before handoff, before the deliver gate fires.
- Distinctive: line 28
All four must be present. Missing any block → gate fails.
- Distinctive: line 48
Not every tool — only inflection points where direction was chosen or changed.
- Distinctive: line 74
Must fit in one sentence — this is what survives context compression.
- Very specific: line 85
Write the tail to ~/.agents/reports/<run-id>-session-tail.md or append to the job outbox note.
≈ “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 Session Review (learning tail)
The final edge of every job. A job is not done with a text answer — it is done when the tail is written.
Karpathy method: synthesize on write, not store raw. Short, precise, survives context compression.
Runs at the end of every meaningful agent session before handoff / deliver gate.
Mandated by the final-learning-tail gate in handoff-graph.yaml.
Trigger
- After completing a multi-step job (any provider: Claude Code, Codex).
- Before writing to vault, before handoff, before the deliver gate fires.
- Gate edge
final-learning-tailchecks all four blocks are present.
Four required blocks
All four must be present. Missing any block → gate fails.
brief_md
3–5 bullets. What was actually done — artifacts produced, decisions made.
Describe what changed, not what was asked.
## Session Brief
- [artifact]: [one concrete line]
- [decision]: [one concrete line]agent_trace
Chronology of key tool calls and decision points.
Not every tool — only inflection points where direction was chosen or changed.
## Agent Trace
1. [tool / action] → [outcome or decision]
2. ...self_reflection
One short paragraph each.
## Self-Reflection
**Went well:** ...
**Went wrong / could be better:** ...
**Next time:** ...most_important
Single most important thing not to lose: a constraint, a lesson, a warning, a pattern.
Must fit in one sentence — this is what survives context compression.
## Most Important
[one sentence]Output
Write the tail to ~/.agents/reports/<run-id>-session-tail.md
or append to the job outbox note.
Optionally pass to the postrun report:
node ~/.agents/registry/scripts/athena-postrun-report.mjs \
--results-json <results.json> \
--run-id <run-id>Gate check (smoke/agent-contract.sh)
Smoke verifies this file exists and contains all four block names:brief_md, agent_trace, self_reflection, most_important.
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.

