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 yeaight7/agent-powerups --skill strategic-context-compactiongit clone --depth 1 https://github.com/yeaight7/agent-powerupsWrote 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/yeaight7/agent-powerups/strategic-context-compaction)<a href="https://agentmods.dev/skills/yeaight7/agent-powerups/strategic-context-compaction"><img src="https://agentmods.dev/badge/skills/yeaight7/agent-powerups/strategic-context-compaction/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/yeaight7/agent-powerups/strategic-context-compaction"><img src="https://agentmods.dev/badge/skills/yeaight7/agent-powerups/strategic-context-compaction.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 | $0.00059 | $0.00944 |
| Opus 5 | $0.00030 | $0.00472 |
| Sonnet 5 | $0.00012 | $0.00189 |
| Haiku 4.5 | $0.00006 | $0.00094 |
Grade A, and why
strategic-context-compaction 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 yesterday.
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 — 80 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Purpose
Compact at logical boundaries to preserve high-value context while clearing noise. Arbitrary or mid-task compaction loses critical state -- file paths, variable names, partial reasoning -- that is expensive to reconstruct.
When to Use
- The context window is filling and you are at a phase boundary (research, planning, implementation, debugging)
- A failed approach left dead-end reasoning that pollutes the next attempt
- You are starting a conceptually distinct task in the same session
- A prior mid-task compaction dropped state you then had to rebuild
Inputs
- The current session state and which phase transition is approaching
- Knowledge of what is already saved durably (task list, files, git, memory) versus only in conversation
Workflow
-
Decide whether to compact by transition. Use the boundary table; default to NOT compacting mid-work.
Transition Compact? Reason Research -- Planning Yes Research context is bulky; the plan is the distilled output Planning -- Implementation Yes Plan is saved in tasks/files; context is free to reset Implementation -- Testing Maybe Keep if tests reference recent code; compact if switching focus area Debugging -- Next feature Yes Debug traces pollute unrelated work Mid-implementation No Losing file paths, variable names, partial state is costly After a failed approach Yes Clear dead-end reasoning before trying a new approach -
Save anything you cannot reconstruct cheaply before compacting.
- Write the plan to a task list or file before compacting after research
- Commit or stash work-in-progress code before compacting after debugging
- Note key file paths in the next prompt if they will be needed again
-
Know what survives versus what is lost. Anything in the Lost column must be persisted in step 2 first.
Survives Lost CLAUDE.md / AGENTS.md instructions Intermediate reasoning Task list (TodoWrite) File contents read in session Files on disk Tool call history Git state Verbally stated preferences Memory files Multi-step conversation context
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.
- yesterday First seen · 80 lines · 59 tokens per session scan A 3e3293f94859
strategic-context-compaction is a skill published in the GitHub repository yeaight7/agent-powerups (6 stars, last pushed 2d ago), licensed Apache-2.0. It adds 59 tokens to every session and 944 once invoked, about $0.0003 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 2026-09-14.
Other skills, from other repositories
persistent-notes
Save notes locally to /mnt/workspace/notes.json file. Use when user wants to "save a note" or "remember something".
session-summaries
What the chat right-panel session summary shows, what it costs, and how to make a session summarize well. Load when the user asks about the session summary panel, why a summary looks wrong or empty, or how to turn it on.
narco-check
Memory integrity audit. Detects hallucinations, circular confirmations, and state poisoning. Runs automatically after 2 consecutive failures or at nightly deep dive. Uses Opus 4.6 as the auditor model.
openlore
Query and publish to an OpenLore knowledge base over SSH using ordinary shell commands. Use when a task needs project documentation, runbooks, shared team knowledge, or a place to publish findings.
systematic-debugging
4-phase root cause debugging: understand bugs before fixing.
github-code-review
Review PRs: diffs, inline comments via gh or REST.