Borrowing it
Nothing to install: this file belongs to walm00/business-context-os. 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/walm00/business-context-os/main/.claude/skills/learning/SKILL.mdgit clone --depth 1 https://github.com/walm00/business-context-osWrote 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/walm00/business-context-os/learning)<a href="https://agentmods.dev/skills/walm00/business-context-os/learning"><img src="https://agentmods.dev/badge/skills/walm00/business-context-os/learning/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/walm00/business-context-os/learning"><img src="https://agentmods.dev/badge/skills/walm00/business-context-os/learning.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 | $0.00054 | $0.01233 |
| Opus 5.5 | $0.00022 | $0.00493 |
| Sonnet 5.5 | $0.00011 | $0.00247 |
| Haiku 4.5 | $0.00005 | $0.00123 |
Grade A, and why
learning 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 — 95 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/learning — Self-learning ladder management
Surface for inspecting and editing the rules BCOS has learned from your resolution clicks.
How learning works (one paragraph)
Every dashboard card click and every headless action writes a row to .claude/quality/ecosystem/resolutions.jsonl via record_resolution.py. The promote_resolutions.py script reads that log, groups by (finding_type, action_taken), and promotes pairs that meet tier thresholds into .claude/quality/ecosystem/learned-rules.json. The cockpit reads learned-rules.json to add a "✨ suggested" badge on cards where you've consistently picked the same action.
Tier thresholds:
| Tier | Required signal | Behavior |
|---|---|---|
| preselect (P5, current) | N≥3 + consistency=1.0 | Pre-fills the radio button on the matching card. Doesn't apply anything. |
| auto-apply (P7, future) | N≥5 + consistency≥0.9 + 14d span | Applies after a 10-second undo countdown. |
| silent (P8, gated) | N≥10 + consistency≥0.95 + 6w auditor-clean + opt-in | Fires without a card. Per-rule user click required to promote into this tier. |
learning-blocklist.json is the user's veto: rules in there are excluded from learned-rules.json regardless of evidence.
Subcommands
/learning list
Show all currently-learned rules grouped by tier with their evidence counts.
Reads: .claude/quality/ecosystem/learned-rules.json
Side effects: none
Output: table of {rule_id, tier, n, consistency, last_observed, calendar_span_days}.
If learned-rules.json is missing or empty, say so and suggest /learning regenerate.
/learning forget <rule-id>
Add a rule to learning-blocklist.json and regenerate learned-rules.json. The rule disappears from the cockpit's "✨ suggested" badges immediately and won't be relearned even if more supporting evidence accumulates.
Required arg: <rule-id> in <finding_type>::<action_taken> form (e.g. inbox-aged::inbox-aged-archive).
Mechanism: call promote_resolutions.add_to_blocklist(rule_id) then promote_resolutions.regenerate(). Both are idempotent.
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 · 95 lines · 54 tokens per session scan A d04513fffdf8
learning is a skill published in the GitHub repository walm00/business-context-os (22 stars, last pushed 5mo ago), licensed MIT. It adds 54 tokens to every session and 1,233 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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