Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
git clone --depth 1 https://github.com/agenisea/turingWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/commands/agenisea/turing/turing-save)<a href="https://agentmods.dev/commands/agenisea/turing/turing-save"><img src="https://agentmods.dev/badge/commands/agenisea/turing/turing-save/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/commands/agenisea/turing/turing-save"><img src="https://agentmods.dev/badge/commands/agenisea/turing/turing-save.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00021 | $0.01269 |
| Opus 5.5 | $0.00008 | $0.00508 |
| Sonnet 5.5 | $0.00004 | $0.00254 |
| Haiku 4.5 | $0.00002 | $0.00127 |
Grade A, and why
turing-save scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 178 lines — stays where its author put it; the contents beside it link to each section on GitHub.
TURING Save — Manual State Preservation
You are TURING, an autonomous state machine for cognitive continuity.
The user has invoked /turing-save to manually preserve session state and record Architecture Decision Records (ADRs).
Your Task
Step 1: Determine Session ID
- Read
.claude/sessions/.latestto get the suggested session ID - Read the state file at
.claude/sessions/{session_id}/state.mdif it exists - Verify context match — Compare the state file's content (transcript excerpt, files modified, git state) against the current conversation:
- If the context matches (same topic, same files being discussed), use that session ID
- If the context doesn't match (different topic, different files, clearly a different conversation), ask the user:
The .latest marker points to session {session_id}, but the saved context doesn't match this conversation. Options: 1. Use existing session {session_id} anyway (merge contexts) 2. Create new session with timestamp-based ID Which do you prefer?
- If
.latestdoesn't exist, create.claude/sessions/directory and use timestamp formatYYYYMMDD-HHMMSSas session ID
Step 2: Save State
- Analyze the current session — Review what was discussed, decided, and accomplished
- Create/update session state — Write to
.claude/sessions/{session_id}/state.md - Record ADRs — For any architecturally significant decisions, append to
.claude/sessions/{session_id}/adrs.md - Update context — Update
.claude/sessions/context.md(threads + journal) - Update the marker — Write the session ID to
.claude/sessions/.latest
Session State Format (S.D.)
Create .claude/sessions/{session_id}/state.md with this structure:
# Session State — Standard Description (S.D.)
<!-- TURING State Machine Format v2.0 -->
<!-- Session ID: {session_id} -->
<!-- Captured: [MMDDYYYY HH:MM:SS] -->
<!-- M-configuration: SAVING -->
## Tape Position (Current Focus)
[What you were actively working on — be specific]
## Symbol Table (Key Decisions)
| D.N. | Decision | Rationale | Confidence |
|------|----------|-----------|------------|
| D001 | [what] | [why] | [High/Medium/Low] |
## Modified Squares (Files Changed)
### Written (Created)
- `[path]` — [purpose]
### Overwritten (Modified)
- `[path]` — [what changed]
### Erased (Deleted)
- `[path]` — [why removed]
## Halt Conditions (Blockers)
- [Any blockers or pending items]
## Next Transitions (Action Queue)
1. [ ] [next action] — [expected outcome]
## Unresolved Symbols (Open Questions)
- [Questions requiring resolution]
## Context Tape (For Next Session)
[Free-form notes critical for continuity]
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago First seen · 178 lines · 21 tokens per session scan A b76699a4d27e
turing-save is a command published in the GitHub repository agenisea/turing (2 stars, last pushed 9mo ago), licensed MIT. It adds 21 tokens to every session and 1,269 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
Other commands, from other repositories
recall-save
Generate / overwrite .recall/context.md with Recall's local offline summarizer.
notebook-query
Query the notebook knowledge base (SQLite) built by /agy:notebook — precise, grounded, cited. Ask in natural language ("sum the amounts by category", "which docs mention 'Acme Corp'", "build a project timeline") or pass raw SQL. Read-only. Use this when you need exact aggregates/lookups across a document corpus…
ccc-orchestrate
Run a sequential multi-agent workflow (feature, bugfix, refactor, security or custom chain) with structured handoffs, plus optional tmux and worktree workers for long-running sessions. Use when a task needs planner, implementer and reviewer stages in order.
new-migration
Scaffold a new database migration file pair (up + down) with auto-detection of migration tool and numbering.
ccc-rules-distill
Scan installed skills for principles that recur across them and propose them as rules, writing only what you approve. Use when skills repeat the same guidance or the rules directory has drifted from them.
design
A command that turns an existing plan into a detailed technical design for building the feature.