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 digital-stoic-org/agent-skills --skill save-contextgit clone --depth 1 https://github.com/digital-stoic-org/agent-skillsWrote 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/digital-stoic-org/agent-skills/save-context)<a href="https://agentmods.dev/skills/digital-stoic-org/agent-skills/save-context"><img src="https://agentmods.dev/badge/skills/digital-stoic-org/agent-skills/save-context.svg" alt="Measured on agentmods" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 2 findings, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high Anti-Refusal · line 58 Skill instructs the agent to omit warnings, disclaimers, or ethical commentary. Stripping safety caveats hides risk from the user and is a common jailbreak preamble.Fix: Remove instructions that suppress warnings, disclaimers, or ethical commentary. Let the agent surface safety-relevant caveats to the user.
- medium Excessive Agency · line 6 Skill selects an external model or provider that may use a different account or billing plan than the operator expects. Undisclosed model switches can cause unexpected cost or quota consumption.Fix: Remove the model/provider override or disclose it prominently and require explicit operator approval before invoking an external coding CLI or billed model.
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 | $0.00048 | $0.01576 |
| Opus 5 | $0.00024 | $0.00788 |
| Sonnet 5 | $0.00010 | $0.00315 |
| Haiku 4.5 | $0.00005 | $0.00158 |
Grade A, and why
save-context 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 8d 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 — 100 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Save Context
Save current session state to CONTEXT-{stream}-llm.md with LLM-optimized format.
Target: 2500-3000 tokens MAX | Speed: 3-5 seconds (Not a throwaway summary: the working memory of a multi-session stream, holding accumulating per-role lines.)
⚠️ AskUserQuestion Guard
CRITICAL: After EVERY AskUserQuestion call, check if answers are empty/blank. Known Claude Code bug: outside Plan Mode, AskUserQuestion silently returns empty answers without showing UI.
If answers are empty: DO NOT proceed with assumptions. Instead:
- Output: "⚠️ Questions didn't display (known Claude Code bug outside Plan Mode)."
- Present the options as a numbered text list and ask user to reply with their choice number.
- WAIT for user reply before continuing.
⚠️ VERBATIM RULE
CRITICAL: Never rewrite the text after the colon of a carried-forward line. Never touch a line whose role is not yours — copy it byte-for-byte. A Haiku pass once reworded a user's exact phrasing while explicitly asked to quote it verbatim — hence Sonnet here, not Haiku.
Performance Rules
- Use
rtkfor ALL shell commands - Parallel tool calls — ALL independent calls in one message
- Minimize round-trips — gather all data phase 1, reason phase 2, write phase 3
Workflow
Phase 1: Gather Data (parallel)
Bash: rtk ls openspec/changes/ + rtk ls -t CONTEXT-*llm.md
Read: existing CONTEXT-{stream}-llm.md (if present) — needed to carry forward owned lines and to read the `saved:` field for the Phase 3 concurrency check
Stream resolution: First word of $ARGUMENTS = stream name (^[a-zA-Z0-9_-]{1,50}$), rest = description. Empty → reuse prior /load-context stream or AskUserQuestion.
Role resolution: --as <role> flag in $ARGUMENTS > role recorded by the last /load-context for this stream > none (solo session — untagged line format, unchanged behavior).
Phase 1b: Detect Thinking Artifacts (parallel with Phase 1)
If $PRAXIS_DIR is set:
Bash: ls -t "$PRAXIS_DIR/thinking"/*/{project}/ 2>/dev/null | head -10
Where {project} = current project folder name. Collect recent artifact paths written during this session (match conversation timestamps/topics).
What ships with it
1 file 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.
- 8d ago First seen · 100 lines · 48 tokens per session scan A ba56e154a453
save-context is a skill published in the GitHub repository digital-stoic-org/agent-skills (20 stars, last pushed 2d ago), licensed MIT. It adds 48 tokens to every session and 1,576 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.
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