memory-bootstrap

memory-bootstrap is a skill for Claude Code, Codex from jscott3201/aionforge-memory. It costs 71 tokens per session (1,504 once invoked), scanned A, original, Apache-2.0.

A one-time setup process for Aionforge Memory, an external store for project context and tracked work, when a project has little or no saved history.

In plain words
What is it for?
Prepare a new project for ongoing memory recall, establish its backlog, and verify that the saved context can be retrieved.
Why use it?
It gives later sessions reliable starting context by recording evidence-based project facts, conventions, decisions, and initial work items.

Skill for Claude CodeCodex

Written for Claude Code and Codex: shipped in a Claude Code plugin, but also agents/openai.yaml present.

Part of the aionforge-memory plugin — 7 skills, 3 commands, 1 hook shipped together

Good fit Prepare a new project for ongoing memory recall, establish its backlog, and verify that the saved context can be retrieved.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/jscott3201/aionforge-memory/memory-bootstrap
Install

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.

Any agent
npx skills add jscott3201/aionforge-memory --skill memory-bootstrap
Clone the repo
git clone --depth 1 https://github.com/jscott3201/aionforge-memory

Made for: Claude Code, Codex.

Or install aionforge-memory, the plugin that ships this one along with the rest of its 7 skills, 3 commands, 1 hook.

Wrote 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.

agentmods badge for memory-bootstrap

README.md
[![agentmods](https://agentmods.dev/badge/skills/jscott3201/aionforge-memory/memory-bootstrap/github.svg)](https://agentmods.dev/skills/jscott3201/aionforge-memory/memory-bootstrap)
Your own site
<a href="https://agentmods.dev/skills/jscott3201/aionforge-memory/memory-bootstrap"><img src="https://agentmods.dev/badge/skills/jscott3201/aionforge-memory/memory-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.

agentmods 80×15 button for memory-bootstrap

Your own site · 80×15
<a href="https://agentmods.dev/skills/jscott3201/aionforge-memory/memory-bootstrap"><img src="https://agentmods.dev/badge/skills/jscott3201/aionforge-memory/memory-bootstrap.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 71 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,504 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00071 $0.01504
Opus 5.5 $0.00028 $0.00602
Sonnet 5.5 $0.00014 $0.00301
Haiku 4.5 $0.00007 $0.00150

Measured 1mo ago against content hash 417df3a0083b, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

memory-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.

plugins/aionforge-memory/skills/memory-bootstrap/SKILL.md · 59 lines

How it starts

The opening of the file, as written. The whole thing — 59 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Memory Bootstrap

Requires an enabled Aionforge Memory MCP server.

Use this skill once per project to turn an empty store into a useful substrate, so the very next session recalls real context instead of starting cold. The ongoing per-task cadence (recall → capture → track work) lives in memory-loop; this skill fills the gap before that loop has anything to recall.

It is idempotent by protocol — recall before you write, so a second run updates the substrate instead of doubling it. The two node kinds get there differently: a re-capture of byte-identical content is dropped automatically, and a reworded fact is updated by passing the original's id as supersedes (otherwise a near-duplicate is still written); work items cannot be superseded, so the backlog stays single-copy only by querying for it before creating (step 7). Aim for a focused starter set — roughly fifteen to twenty precise records — not an exhaustive dump.

Seed facts, never invent them

Every seeded memory must come from real evidence: the repository (README, docs, CI config, Cargo.toml/package.json, the commit history) or the user. Do not capture aspirations, guesses, or anything you have not checked against the repo or confirmed with the user. A bootstrapped substrate full of plausible-but-wrong facts is worse than an empty one — future agents will trust it.

Route each item to the right node (this is the most common mistake):

  • A durable fact, decision, convention, or architectural choice → capture (it becomes a decaying episode).
  • A task, milestone, backlog item, or follow-up → work_create → work_advance (a persistent, status-tracked work item).
  • There is no "note" to store directly — notes are derived by consolidate from episodes. A free-floating "note" is either a fact (capture) or a thing to do (work_create).

Procedure

  1. Bootstrapping writes many memories, so confirm the user wants it. Check server_status if the MCP connection is uncertain; if it is unavailable, say so and stop.
  2. Resolve identity once: prefer AIONFORGE_AGENT_ID; otherwise use the stable agent UUID from the user or project instructions. If none exists, ask once — do not silently mint a throwaway namespace, or the substrate you seed will be orphaned from the next session. capture takes the bare UUID as agent_id; recall and work tools take the namespace form agent:<uuid>.
  3. Recall first, so re-runs do not duplicate. Search the store for what may already be there ("project conventions", "architecture decisions", "dev workflow", and the project name), and work_query for an existing backlog. Seed only the gaps; when you are updating a fact that already exists, pass its id as supersedes rather than writing a near-duplicate.
  4. Gather the ground truth: read the repo's README, docs, CI/workflow config, and dependency manifests, and ask the user for anything not written down (the merge model, release cadence, review norms, hard constraints). This is the evidence the next steps capture from.
  5. Seed conventions as captures — one focused capture per durable convention: dev workflow, branch/merge model, CI gates, coding standards, test/review expectations, release process. Use role: assistant and a higher trust for curated foundational facts (e.g. trust: 0.9); include the project, the source (repo file or user), and the date when useful.
  6. Seed architecture and product direction: capture the key decisions and their rationale, the high-level component map, and the product's purpose and direction — each as its own record, sourced from the repo or the user.
  7. Stand up the backlog skeleton, querying before creating. Work items have no supersede or dedup, so a blind work_create on a re-run would mint a second epic and duplicate tasks. First work_query for an existing epic (filter level: epic, then match on title in the results — the query filters by status and level, not title). Create the project epic only if none exists, then add the handful of missing initial task children under it via parent_id, reusing the existing epic's id as the parent. New items start at todo, giving the next session a backlog to work_query instead of re-deriving.
  8. Optionally open a shared space: if the user authorizes a team scope for shared project feedback, capture the cross-cutting conventions and decisions into it with target_namespace: team:<name> (and the matching teams). Default to private otherwise.
  9. Verify the substrate is retrievable: do a recall pass — search the seeded topics back and work_query the backlog to confirm they return. Bootstrapping is not done until recall proves it took.
  10. Summarize what you seeded: the categories covered, the work-item ids for the epic and tasks, and any receipt ids worth keeping for later audit, supersession, or forget. After several writes, check consolidation_status; run consolidate only when the approval policy and user/project rules allow mutating derived memory.

Read the full file on GitHub · 59 lines

Files

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.

Changes

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.

  1. 1mo ago First seen · 59 lines · 71 tokens per session scan A 417df3a0083b

Subscribe to this mod's changes

memory-bootstrap is a skill published in the GitHub repository jscott3201/aionforge-memory (11 stars, last pushed 3mo ago), licensed Apache-2.0. It adds 71 tokens to every session and 1,504 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.

Related

Other skills, from other repositories

brain-write

How to write to the INITE Brain knowledge graph from an agent loop — recordfact (with the conversationId / evidence[] grounding inputs), ingestdocument, linkentities, retractfact, recordfeedback, and the detectcontradiction preflight. Covers confidence picking, claim grounding, retract vs forget semantics, identityof…

inite-ai/inite-brain-service · 97 tokens

brain-conflict

How to detect and resolve conflicting beliefs in the INITE Brain knowledge graph — the COMPETING fact status, getcompetingfacts, detectcontradiction preflight, and the human-in-the-loop adjudication workflow. Use when the timeline shows two facts disagreeing on the same predicate, or when an agent needs to decide what…

inite-ai/inite-brain-service · 78 tokens

brain-recall

Recall everything brain knows about one specific entity — current profile, full bitemporal timeline, graph neighbours, and unresolved disagreements. Use when the user names a person/company/thing and asks "tell me about them", "what's their history", or "what do we still disagree about?". For a single-shot LLM…

inite-ai/inite-brain-service · 81 tokens

brain-bitemporal

How to query the INITE Brain knowledge graph across time — the asOf parameter, validFrom/validUntil semantics, reading retracted facts, and the memorydiff "what changed between two cursors" surface. Use when the user's question has a temporal dimension ("on X date", "before Y", "what's new since last conversation").

inite-ai/inite-brain-service · 74 tokens

memory-review

Memory hygiene audit -- finds stale facts, contradictions, low-confidence entries, and consolidation candidates in Pensyve memory. Use periodically to maintain memory quality.

major7apps/pensyve · 33 tokens

session-memory

End-of-session memory capture -- classifies session signals using a tiered taxonomy and stores confirmed items via Pensyve. Use when ending a work session or when the user wants to capture what was learned.

major7apps/pensyve · 44 tokens