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 skills add backchainai/backchain-plugins --skill workinggit clone --depth 1 https://github.com/backchainai/backchain-pluginsWrote 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/skills/backchainai/backchain-plugins/working)<a href="https://agentmods.dev/skills/backchainai/backchain-plugins/working"><img src="https://agentmods.dev/badge/skills/backchainai/backchain-plugins/working/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/skills/backchainai/backchain-plugins/working"><img src="https://agentmods.dev/badge/skills/backchainai/backchain-plugins/working.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 | $0.00097 | $0.01183 |
| Opus 5.5 | $0.00039 | $0.00473 |
| Sonnet 5.5 | $0.00019 | $0.00237 |
| Haiku 4.5 | $0.00010 | $0.00118 |
Grade A, and why
working 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 — 93 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Working Memory
Manage ephemeral .memory/ directories for persisting structured conversation state across multi-day Claude Code sessions. Three commands cover the full lifecycle:
| Command | Purpose |
|---|---|
/engram:working checkpoint |
Create or update .memory/ with current conversation state |
/engram:working promote |
Move .memory/ artifacts to permanent knowledge locations |
/engram:working cleanup |
Delete promoted content, preserve the directory |
The skill is invoked as /engram:working (the plugin-qualified skill name) followed by the subcommand. Natural-language phrasings like "checkpoint working memory" trigger the same dispatch.
Working memory vs auto memory
This skill complements Claude Code's native auto memory. They serve different purposes:
| Working memory (this skill) | Auto memory (native) | |
|---|---|---|
| Trigger | User-initiated (/engram:working checkpoint) |
Automatic (Claude decides what's worth remembering) |
| Content | Structured work state — todos, decisions, questions | Implicit learnings — corrections, preferences, patterns |
| Lifecycle | Ephemeral: checkpoint → promote → cleanup | Persistent until manually deleted |
| Scope | Per-directory (.memory/ in CWD) |
Per-project (~/.claude/projects/<project>/) |
| Promotion path | Explicit: ADRs, issue-tracker entries, docs | Implicit: stays in MEMORY.md / topic files |
Use working memory for structured in-flight work that warrants a deliberate promotion gate. Let auto memory handle preferences, build insights, and behavioral corrections that Claude discovers organically.
During checkpoint, do not capture items that auto memory already handles — user preferences, build commands, debugging shortcuts, code style. Focus on actionable work state.
During promote, decisions too small for a full ADR but worth persisting can be written into auto memory using its standard frontmatter format. Once written there, they are auto memory's responsibility; working does not re-modify them.
What ships with it
8 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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 · 93 lines · 97 tokens per session scan A b2c6b1dfc121
working is a skill published in the GitHub repository backchainai/backchain-plugins (4 stars, last pushed 2mo ago), licensed Apache-2.0. It adds 97 tokens to every session and 1,183 once invoked, about $0.0004 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.
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