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 pantheon-org/tekhne --skill vault-consolidategit clone --depth 1 https://github.com/pantheon-org/tekhneWrote 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/pantheon-org/tekhne/vault-consolidate)<a href="https://agentmods.dev/skills/pantheon-org/tekhne/vault-consolidate"><img src="https://agentmods.dev/badge/skills/pantheon-org/tekhne/vault-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/pantheon-org/tekhne/vault-consolidate"><img src="https://agentmods.dev/badge/skills/pantheon-org/tekhne/vault-consolidate.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00084 | $0.00566 |
| Opus 5 | $0.00042 | $0.00283 |
| Sonnet 5 | $0.00017 | $0.00113 |
| Haiku 4.5 | $0.00008 | $0.00057 |
Grade A, and why
vault-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 11d 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.
What it actually says
vault-consolidate
Cluster related episodic memories and synthesise them into durable semantic notes.
Mindset
Think of --propose as generating a diff and --apply as committing the merge.
You never commit without reviewing the diff first. The human is always the final
gatekeeper — the agent's job is to surface candidates, not to decide what gets
promoted to long-term memory.
When to use
- After a long session with many captures
- When the user asks to "consolidate", "summarise", or "clean up" memories
- Periodic maintenance (e.g. end of week)
Workflow
Always propose before applying:
# Step 1 — generate proposals (written to vault inbox for review)
vault-cli consolidate --propose [--project <id>]
# Step 2 — open vault inbox, review, set status: approved or rejected
# (edit 00-inbox/consolidation-proposals.md in Obsidian)
# Step 3 — apply approved proposals
vault-cli consolidate --apply
If --propose outputs "No episodic memories found to consolidate", skip Step 3 —
there is nothing to apply.
What happens
--propose: clusters episodic memories by semantic similarity, calls the inference command to synthesise each cluster, writes proposals to00-inbox/consolidation-proposals.md--apply: reads the proposals file, writes approved ones as semantic notes, marks source episodics assuperseded, logs rejected ones to audit
Important
Source episodic memories are never deleted — only marked superseded.
Human edits in Obsidian are always respected and never overwritten.
Never
- Never run
--applywithout first running--propose— doing so bypasses human review and irreversibly promotes unreviewed clusters to semantic memory - Never run
--applywithout confirming the user has reviewed the proposals — always pause at Step 2 and wait for the user to confirm before proceeding - Never delete episodic source memories manually — they are preserved automatically and serve as audit history
What ships with it
18 files 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.
- evals/instructions.json 2.5 KB
- evals/scenario-1/capability.txt 13 B
- evals/scenario-1/criteria.json 1.4 KB
- evals/scenario-1/task.md 307 B
- evals/scenario-2/capability.txt 15 B
- evals/scenario-2/criteria.json 943 B
- evals/scenario-2/task.md 271 B
- evals/scenario-3/capability.txt 18 B
- evals/scenario-3/criteria.json 1.1 KB
- evals/scenario-3/task.md 279 B
- evals/scenario-4/capability.txt 21 B
- evals/scenario-4/criteria.json 1.1 KB
- evals/scenario-4/task.md 300 B
- evals/scenario-5/capability.txt 19 B
- evals/scenario-5/criteria.json 1.1 KB
- evals/scenario-5/task.md 287 B
- evals/summary_infeasible.json 58 B
- evals/summary.json 246 B
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.
- 11d ago First seen · 66 lines · 84 tokens per session scan A 3537721bed63
vault-consolidate is a skill published in the GitHub repository pantheon-org/tekhne (10 stars, last pushed 2d ago), licensed MIT. It adds 84 tokens to every session and 566 once invoked, about $0.0004 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 2026-08-31.
Other skills, from other repositories
context-engineering
Optimizes agent context setup. Use when starting a new session, when agent output quality degrades, when switching between tasks, or when you need to configure rules files and context for a project.
comet-memory
A review step for deciding whether information should become durable personal memory. It can keep, update, forget, or skip memory candidates based on bounded evidence.
recall-memory
Recall relevant long-term memories on demand. Given a topic or question, judges relevance from pre-loaded metadata, loads only relevant files, and returns a concise summary to the main agent.
relevance-coarse-filter
Cheap, high-recall first-pass filter that removes obvious junk from a detector candidate pool before expensive story-origin research and PR judgment. Decides keep, monitoronly, or reject — never ranks, writes angles, verifies dates, or decides whether to pitch.
self-improve
Extract lessons from the current session, or sweep the project's past sessions when asked, and route them to the appropriate knowledge layer (project AGENTS.md, auto memory, existing skills, or new skills). Use when the user asks to "self-improve", "distill this session", "distill past sessions", "sweep past…
catchup
Rebuild the human's lost context on a project from live state, in plain language: what needs them, what changed, what new words mean. Use when the human returns after a gap, says they can't follow the project anymore, asks what happened or what a term means, or before deciding what to do next when their mental model…