Borrowing it
Nothing to install: this file belongs to pskoett/measuring-ai-proficiency. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/pskoett/measuring-ai-proficiency/main/.claude/skills/learning-aggregator/SKILL.mdgit clone --depth 1 https://github.com/pskoett/measuring-ai-proficiencyWrote 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/pskoett/measuring-ai-proficiency/learning-aggregator)<a href="https://agentmods.dev/skills/pskoett/measuring-ai-proficiency/learning-aggregator"><img src="https://agentmods.dev/badge/skills/pskoett/measuring-ai-proficiency/learning-aggregator/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/pskoett/measuring-ai-proficiency/learning-aggregator"><img src="https://agentmods.dev/badge/skills/pskoett/measuring-ai-proficiency/learning-aggregator.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 | $0.00085 | $0.03434 |
| Opus 5.5 | $0.00034 | $0.01374 |
| Sonnet 5.5 | $0.00017 | $0.00687 |
| Haiku 4.5 | $0.00009 | $0.00343 |
Grade A, and why
learning-aggregator 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 2mo 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 — 306 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Learning Aggregator
Reads accumulated .learnings/ files across all sessions, finds patterns, and produces a ranked list of promotion candidates. This is the outer loop's inspect step.
Without this skill, .learnings/ is a write-only log. Patterns accumulate but nobody synthesizes them. The same gap resurfaces two weeks later because no one looked.
When to Use
- Weekly cadence — scheduled or manual, review accumulated learnings
- Before major tasks — check if the task area has known patterns
- After a burst of sessions — consolidate findings from a sprint or incident
- When self-improvement flags
promotion_ready— verify the flag with full context
What It Produces
A gap report — a ranked list of patterns that have crossed (or are approaching) the promotion threshold, with evidence and recommended actions.
Step 1: Read All Learning Files
Read these files in .learnings/:
| File | Contains |
|---|---|
LEARNINGS.md |
Corrections, knowledge gaps, best practices, recurring patterns |
ERRORS.md |
Command failures, API errors, exceptions |
FEATURE_REQUESTS.md |
Missing capabilities |
Parse each entry's metadata:
Pattern-Key— the stable deduplication keyRecurrence-Count— how many times this pattern has been seenFirst-Seen/Last-Seen— date rangePriority— low / medium / high / criticalStatus— pending / promotion_ready / promoted / dismissedArea— frontend / backend / infra / tests / docs / configRelated Files— which parts of the codebase are affectedSource— conversation / error / user_feedback / simplify-and-hardenTags— free-form labels
Step 2: Group and Aggregate
Group entries by Pattern-Key. For each group:
- Sum recurrences across all entries with the same key
- Count distinct tasks — how many different sessions/tasks encountered this
- Compute time window — days between First-Seen and Last-Seen
- Collect all related files — union of all entries' file references
- Take highest priority across entries in the group
- Collect evidence — the Summary and Details from each entry
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.
- 2mo ago First seen · 306 lines · 85 tokens per session scan A ed22477d8cc6
learning-aggregator is a skill published in the GitHub repository pskoett/measuring-ai-proficiency (13 stars, last pushed 3mo ago), licensed MIT. It adds 85 tokens to every session and 3,434 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.
learning-aggregator compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| learning-aggregatorpskoett/measuring-ai-proficiency | Skill | 13 | 3mo ago | MIT | A | 85 | Claude Code |
| knowledge-ingestopenclaw/openclaw | Skill | 394k | yesterday | MIT | A | 80 | Claude Code, Codex |
| mem0-oss-to-platformmem0ai/mem0 | Skill | 68k | 2d ago | Apache-2.0 | A | 217 | Claude Code, Codex |
| forgetasgeirtj/system_prompts_leaks | Skill | 71k | 2d ago | CC0-1.0 | A | 74 | Claude Code, Codex |
| Cortexdanielmiessler/LifeOS | Skill | 20k | yesterday | MIT | A | 184 | Claude Code |
| memoryopensquilla/opensquilla | Skill | 7.1k | 6d ago | Apache-2.0 | A | 44 | Claude Code, Codex |
| ha-data-storesshiwenwen/hope-agent | Skill | 1.9k | yesterday | MIT | A | 115 | Claude Code, Codex |