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 Autoloops/greplica --skill greplica-fast-session-bootstrapgit clone --depth 1 https://github.com/Autoloops/greplicaWrote 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/autoloops/greplica/greplica-fast-session-bootstrap)<a href="https://agentmods.dev/skills/autoloops/greplica/greplica-fast-session-bootstrap"><img src="https://agentmods.dev/badge/skills/autoloops/greplica/greplica-fast-session-bootstrap/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/autoloops/greplica/greplica-fast-session-bootstrap"><img src="https://agentmods.dev/badge/skills/autoloops/greplica/greplica-fast-session-bootstrap.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
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 →
- medium Memory Poisoning · line 62 Skill attempts to fill the context window with filler content, displacing legitimate instructions and safety constraints. This can degrade agent performance or bypass safety boundaries.Fix: Implement context-window management that detects and rejects padding or stuffing attempts. Prioritize system instructions over user-injected content.
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 | $0.00052 | $0.02201 |
| Opus 5.5 | $0.00021 | $0.00880 |
| Sonnet 5.5 | $0.00010 | $0.00440 |
| Haiku 4.5 | $0.00005 | $0.00220 |
Grade A, and why
greplica-fast-session-bootstrap 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 — 136 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Fast Session Bootstrap
Input: a Markdown bundle from:
greplica transcript bundle --platform codex|claude --file <path> [--file <path>...] --out <bundle.md>
Goal: seed useful repo memory from prior sessions. Read the whole bundle, extract every high-signal durable candidate from that bundle, and store focused memories that change what a future agent should do next time. Do not store a broad transcript digest.
Do not store what happened in the session. Store what changes what a future agent should do next time in this repo.
Operating Budget
- Read the whole bundle before choosing what to store.
- Build a candidate inventory across all sessions, not just the first obvious flow.
- Store a complete focused set of durable memories from the bundle. Do not optimize for a fixed memory count.
- Keep each claim narrow and reusable. Split unrelated implementation facts, decisions, constraints, rationale, rejected alternatives, risks, and future work.
- Use
greplica graph contextqueries only to dedupe, reuse existing names, or identify stale memory for bundle-supported candidates. Do not use graph context to discover extra memories that are not in the bundle. - Use targeted code reads sparingly, only when the code surface itself is the durable memory a future agent needs to navigate.
- Leave
supersedes[]empty unless the user explicitly asked to replace stale memory.
Extract Durable Candidates
Build a scratch candidate inventory before writing JSON. Put candidates into these buckets:
- durable repo decisions and constraints;
- component or flow behavior that future agents would otherwise grep to reconstruct;
- non-obvious boundaries, ownership, or similarly named concepts;
- user corrections and gotchas;
- rationale and trade-offs;
- rejected alternatives;
- planned, reverted, or exploratory work that must not be mistaken for implemented behavior;
- explicit future-work boundaries;
- diagnostic/error-message behavior and install-vs-use boundaries;
- evidence/provenance model rules, especially rejected evidence representations;
- accountability gaps where a workflow appears to run but does not clearly mark durable completion;
- environment or installed-binary-vs-checkout gotchas that could mislead future debugging;
- guidance placement and query-shape rules that change how future agents should use Greplica;
- old memory that may need superseding.
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 · 136 lines · 52 tokens per session scan A 7beb37841225
greplica-fast-session-bootstrap is a skill published in the GitHub repository Autoloops/greplica (439 stars, last pushed 2mo ago), licensed MIT. It adds 52 tokens to every session and 2,201 once invoked, about $0.0002 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 2026-08-30.
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