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 choiyounggi/groundwork --skill consolidategit clone --depth 1 https://github.com/choiyounggi/groundworkWrote 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/choiyounggi/groundwork/consolidate)<a href="https://agentmods.dev/skills/choiyounggi/groundwork/consolidate"><img src="https://agentmods.dev/badge/skills/choiyounggi/groundwork/consolidate/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/choiyounggi/groundwork/consolidate"><img src="https://agentmods.dev/badge/skills/choiyounggi/groundwork/consolidate.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 | $0.00085 | $0.02504 |
| Opus 5.5 | $0.00034 | $0.01002 |
| Sonnet 5.5 | $0.00017 | $0.00501 |
| Haiku 4.5 | $0.00009 | $0.00250 |
Grade A, and why
consolidate 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 — 194 lines — stays where its author put it; the contents beside it link to each section on GitHub.
memory-loop: consolidate
Capture and expiry keep memory flowing; nothing keeps it coherent. Over many
sessions the index bloats, two files describe the same thing, an old fact
survives next to the newer one that disproved it, and "yesterday" is frozen in a
note whose yesterday is long gone. This skill is the operate-stage cleanup: a
reflective pass that merges long-tier memory into durable, well-organized form —
and, like remember, never writes without your confirmation.
Adapted from xai-org/grok-build's "dream" consolidation pass (Apache-2.0), reframed around memory-loop's save gate and tier model.
When to run
It is a manual skill — there is no forced trigger. Run it when:
MEMORY.mdhas grown large (~120+ lines).signals.jsonlhas accumulated correction signals worth reviewing (see the signals pass below).- A big piece of work finished and left several related memories behind.
- The SessionStart upkeep check (
memory-staleness-check.sh) reportedhabits over budget,habits over rule cap, orhabits over split threshold— or a write to HABITS.md was denied byhabits-budget-guard.sh. - The SessionStart upkeep check (
memory-staleness-check.sh) listed re-verification candidates, index drift, orphans or broken index links — it is the closest thing this skill has to a trigger, and it only ever reports.
Scope — what it may touch
Read MEMORY.md, the tier: long memory files beside it, and the habit file
(habitsPath, default ~/.claude/groundwork/HABITS.md). Exclude:
| Excluded | Why |
|---|---|
Files without a tier key |
Outside the lifecycle (the compatibility contract) — never touch them |
tier: short memories |
The expiry sweep owns these; consolidation leaves them alone |
Identity files (the user/assistant names from setup) |
Continuity anchors — changed only through the identity skill, never here |
Anything already under archived/ |
Already retired |
The five verbs
Review each memory or group through five lenses (the consolidation prompt):
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 Changed · +28 lines 2b5182ec5846
- 1mo ago Changed · +77 lines · +20 tokens per session f44b47910732
- 1mo ago First seen · 89 lines · 65 tokens per session scan A e16ae501412b
consolidate is a skill published in the GitHub repository choiyounggi/groundwork (3 stars, last pushed 2d ago), licensed MIT. It adds 85 tokens to every session and 2,504 once invoked, about $0.0003 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 skills, from other repositories
reflect
Per-project self-improvement - reads the .harness ledger and feedback memories, then proposes gated rule/threshold/ADR changes so the project stops repeating mistakes. Run periodically.
remember
Review auto-memory entries and propose promotions to CLAUDE.md, CLAUDE.local.md, or shared memory. Also detects outdated, conflicting, and duplicate entries across memory layers.
dream
Memory consolidation - review, merge, prune, and index memory files. Run periodically to keep memories organized and up-to-date.
ucai-patterns
Use when the user asks about Claude Code best practices, how to write agents, how to use hooks, how to manage context, or how to work effectively with Claude Code's native systems.
audit-context
Measures where a project's Claude context is going before applying any of clean-context's levers. Reports what Glob discovers with and without ignore files applied, the twenty largest files in the repo and which are generated, the cumulative size of every CLAUDE.md loaded for this working directory, and how many MCP…
self-improvement
How an autonomous AI engineer improves its OWN definition (its engineering contract, agent definitions, and skills) over time — capturing operational learnings from every run and distilling them into evidence-based, guard-railed draft PRs self-promoted on genuine readiness. Use at the end of every run (to log…