focused-fix
Deep-dive feature repair — systematically fix an entire feature/module across all its files and dependencies. Usage: /focused-fix <feature-path>
Inspect, trace, and adopt the tiered agent-memory store (status | why | contested | adopt | forget)
$ npx -y skills add alirezarezvani/claude-skills --agent claude-codeHow it fires
How this command gets triggered: by you, by Claude, or both.
/cs-memoryContext preview
What this command does when you run it.
Inspect, trace, and adopt the tiered agent-memory store (status | why | contested | adopt | forget)
description: Inspect, trace, and adopt the tiered agent-memory store (status | why | contested | adopt | forget) argument-hint: "status | why \"<claim>\" | contested | adopt | forget \"<claim>\""
Argument: `$ARGUMENTS` (default: `status`)
Scripts live at `engineering/agent-memory/skills/agent-memory/scripts/`. All are stdlib-only and read-only except where stated.
---
1. Run `memory_inspect.py --tier L3`, `--tier L2`, `--tier L1`. 2. Read `.memory/staged/promotions.json` if it exists. 3. Read the last 7 days of `.memory/errors.log` if it exists — **surface any entry**. That file is where silently dropped writes are recorded, and a log nobody is pointed at is the same as no log.
Report, in this order: what is always loaded (L3), what this project loads (L2), how many candidates are waiting and what is blocking each, what is staged for adoption, and any dropped writes.
**Do not adopt anything here.** `status` is read-only.
---
Run `memory_inspect.py --why "<claim>"`.
Report the full provenance: observation count, distinct sessions, distinct calendar days, first and latest transcript back-pointers, whether each resolves, and the quoted source line when exactly one transcript matched.
If the resolution status is **`ambiguous`**, say so plainly and print no source line. Two projects can hold a transcript of the same basename; guessing attaches a real claim to the wrong session, and a wrong citation is worse than none.
---
Run `memory_inspect.py --contested`.
For each pair, present both claims with their dates and sources side by side and ask the user which governs. **Do not pick.** Do not merge them. Do not mark one resolved on your own judgement — resolution is a human decision by design.
---
The only command in this file that writes. Six steps, in order, no skipping:
1. Run `memory_promote.py --stage` to refresh `.memory/staged/promotions.json`. 2. **Back up both `CLAUDE.md` files** (project and global) with a timestamped copy. Do this before writing anything, every time. 3. Walk the staged list **one atom at a time**. For each, show the claim, the evidence (sessions, days, sources), and the target file. Wait for the user. 4. **Refuse outright** any atom with `redacted: true` — no amount of evidence substitutes for the human reading the original. Explain why and move on. 5. **Refuse** any atom whose citation does not resolve. 6. Append accepted atoms to the target `CLAUDE.md` under a clearly marked `<!-- agent-memory: adopted -->` section, and log each to `.memory/adopted.log`.
Never write to a `CLAUDE.md` outside this flow. Never batch-accept.
---
1. Locate the atom with `memory_inspect.py --why "<claim>"`. 2. Show the user exactly what will be removed, from which tier, and whether it was already adopted into a `CLAUDE.md`. 3. On confirmation, remove it from `.memory/atoms.jsonl` and, if it was adopted, remove the corresponding line from the `CLAUDE.md` — after backing that file up.
Removing an atom does **not** prevent re-learning. If the marker fires again in a future session, it returns. That is correct: forgetting is not a permanent veto, and saying so avoids a confusing surprise later. To stop it returning, change the underlying fact or state the correction — a correction is itself a high-confidence observation.
---
with the hooks installed; nothing is wrong.
are changed in the open, in `DESIGN.md`, not per-claim. Offer to record the case as evidence the threshold is wrong.
`engineering/memory-engineering`. This skill *is* a memory system; that one audits any of them, this one included.
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Repo: alirezarezvani/claude-skills
Deep-dive feature repair — systematically fix an entire feature/module across all its files and dependencies. Usage: /focused-fix <feature-path>
Clean up merged branches locally and on remote, keeping only main, dev, and gh-pages.
Stage, commit, and push the current branch following git governance rules.
Comprehensive audit pipeline for skills, plugins, agents, and commands. Validates structure, quality, security, marketplace compliance, cross-platform…