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.
npx agentmods add commands/taoidle/plan-cascade/memory-doctorgit clone --depth 1 https://github.com/Taoidle/plan-cascadeWhat 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 | $0.00035 | $0.00972 |
| Opus 5 | $0.00017 | $0.00486 |
| Sonnet 5 | $0.00007 | $0.00194 |
| Haiku 4.5 | $0.00003 | $0.00097 |
Grade A, and why
memory-doctor scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 3d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 113 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Memory Doctor — 决策健康诊断
You are running a full diagnosis on all Architecture Decision Records (ADRs) across the project's design documents.
Step 1: Collect and Diagnose All Decisions
CRITICAL: Use Bash to run the memory doctor script in full diagnosis mode:
uv run python "${CLAUDE_PLUGIN_ROOT}/skills/hybrid-ralph/scripts/memory-doctor.py" \
--mode full \
--project-root "$(pwd)"
This script collects all decisions from every design_doc.json in the project (root, worktrees, feature directories) and uses LLM to detect conflicts, superseded entries, and semantic duplicates.
Exit code handling:
- Exit 0: No issues found, or no decisions to check — display "No issues found" and stop here
- Exit 1: Diagnosis issues found — proceed to Step 2
- Exit 2 (or script crash/traceback): Infrastructure error. Common causes:
- No API key configured — tell the user to set
ANTHROPIC_API_KEY,OPENAI_API_KEY, orDEEPSEEK_API_KEY - No
design_doc.jsonfiles found in the project - Display the error message and stop here
- No API key configured — tell the user to set
Step 2: Display Diagnosis Report
Display the full diagnosis report from Step 1 output. The report groups findings by type:
- 🔴 CONFLICT: Contradictory decisions on the same concern
- 🟠 SUPERSEDED: A newer decision covers the scope of an older one
- 🟡 DUPLICATE: Semantically identical decisions with different wording
Step 3: Interactive Resolution
CRITICAL: For each diagnosis finding, use AskUserQuestion to let the user choose an action:
For CONFLICT findings:
- Deprecate — Mark the older decision as
deprecated(recommended) - Skip — Keep both decisions as-is
For SUPERSEDED findings:
- Deprecate — Mark the superseded decision as
deprecated(recommended) - Skip — Keep both decisions as-is
For DUPLICATE findings:
- Merge — Keep one decision, remove the duplicate (recommended)
- Skip — Keep both decisions as-is
Present each finding with its explanation and suggestion from the diagnosis report. Example question:
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.
- 3d ago First seen · 113 lines · 35 tokens per session scan A d0b9d358f63a
memory-doctor is a command published in the GitHub repository Taoidle/plan-cascade (131 stars, last pushed 5mo ago), licensed MIT. It adds 35 tokens to every session and 972 once invoked, about $0.0002 per session on Opus 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 2026-08-30.
Other commands, from other repositories
/release
Release a new version — updates CHANGELOG, pyproject.toml, creates git tag, and pushes.
delegate
Delegate a task to any engine and model through the anymodel-runner subagent.
choose
Pick an AnyModel action, provider, and model through an interactive semantic chain.
delegate-with
Delegate through an interactive provider and model selection chain.
review-with
Run an adversarial review through an interactive provider and model selection chain.
setup
Check local engine and provider setup, and optionally toggle the stop-time review gate.