conversation-checkpoint
Skill3 starsChanged 2 months agoMITView on GitHub
Use when a long or repetitive conversation has no source file and must be converted into a recoverable task state before continuation, handoff, or compaction.
In this repository6 other files it gives its agents.
- context-auditSkill
- context-benchmarkSkill
- context-onpremiseSkill
- context-orchestratorSkill
- context-packSkill
- context-economySkill
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
- Distinctive: line 16
Create a state snapshot with these required sections:
- Very specific: line 44
Run python scripts/tikaz_context.py checkpoint --source <transcript.md> --output <checkpoint.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
Conversation Checkpoint
Designed, integrated, independently refactored, and continuously maintained by TIKAZ.
Input
Accept a long or repetitive conversation transcript and, when known, the next task or handoff audience. Use this Skill when no authoritative source file already captures the current state.
Workflow
Create a state snapshot with these required sections:
- Goal
- Confirmed Constraints
- Decisions
- Completed
- Remaining
- Evidence
- Open Questions
Preserve the latest user instruction, approvals, rejected directions, file paths, identifiers, numbers, URLs, commands, errors, and objective verification. Separate completed facts from proposed work. Remove conversational repetition only after its surviving decision or constraint is recorded.
Validate the snapshot against the available transcript. Missing protected facts must be restored or listed as omissions. A concise snapshot is not proof that the underlying work succeeded.
Output contract
Return one self-contained Markdown checkpoint containing the seven required sections, the latest user instruction, objective verification state, unresolved conflicts, protected identifiers, and visible omissions. Separate confirmed completion from proposals and agent assumptions.
Validation and fallback
Compare the checkpoint with the transcript using validate-snapshot. Restore missing numbers, paths, URLs, commands, approvals, rejections, or error text. If the transcript is incomplete or contradictory, preserve the conflict instead of choosing silently. Never mark work complete solely because the conversation said it was complete.
Example
Create a recoverable checkpoint from this conversation before handoff. Preserve decisions, rejected directions, completed evidence, file paths, commands, numbers, and open questions.Run python scripts/tikaz_context.py checkpoint --source <transcript.md> --output <checkpoint.md>. Use validate-snapshot when checking an edited checkpoint against its original transcript.
Does it work?
Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.
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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.

