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 agentmods add skills/capitalone/context-specs/learnnpx skills add capitalone/context-specs --skill learngit clone --depth 1 https://github.com/capitalone/context-specsWhat 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 | $0.00120 | $0.03869 |
| Opus 5 | $0.00060 | $0.01935 |
| Sonnet 5 | $0.00024 | $0.00774 |
| Haiku 4.5 | $0.00012 | $0.00387 |
Grade B, and why
learn scanned grade B with 1 finding 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 2d 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.
Enumerates other installed skillsmediumAgent snooping
Other skills' SKILL.md files reveal prompts, capabilities and secrets that should be invisible to peers.
files (`ls .claude/skills/expert/references/*.md` plus their contents); the How it starts
The opening of the file, as written. The whole thing — 251 lines — stays where its author put it; the contents beside it link to each section on GitHub.
learn
This is how the project gets better on every merge. When code lands on main,
/learn reconciles the project's long-term memory with what just landed: it
updates the Expert (procedural + semantic memory, pulled on demand) and the
AGENTS.md map (eager memory, loaded as agents traverse the repo), discovers
project invariants, and drafts candidate lints (the highest-value memory,
because a lint is a rule the agent cannot ship past).
You run headless, invoked by the memory loop (scripts/learn-dispatch.sh in the harness repo, driven
by the context-specs supervisor on its own interval) as the post-merge step — in a dedicated
../<repo>-harness-learn worktree on a learn/<sha> branch off origin/main, never
in the developer's clone. The memory loop runs independently of the
feature/build loop, so a from-scratch Expert bootstrap blocks neither. Your output is
a single reviewable PR — never an auto-merge. Humans steer at merge.
What memory is (read this first)
The memory is the developer's. It holds facts about the code as it is and decisions, direction, and aspirations the developer has written that the code hasn't caught up to. Your input is the merged diff — that's why you run post-merge — and your job is to reconcile memory with it: add what the merge taught, update what it invalidated, and advance any direction it touches (fulfilled → rewrite as cited fact; contradicted → edit and note it). Direction is never stale merely because it isn't observable in code yet.
You are the automated writer; the developer writes memory directly, any time, and their edits are authoritative (P7). STUCK features are handled by the human directly (their first job there is to identify the context defect that misled the agent, correct it on the feature branch, then fix the code); their corrections ride into main with the feature merge, and you observe them in the diff you read.
The philosophy
- P1 — Write from the merged diff. Your own additions are evidence-based: cite the diff. But memory as a whole is not diff-shaped — developer-written direction lives beside your facts, and you maintain it (advance or close it when a merge fulfills or contradicts it), never delete it for lacking a code anchor.
- P2 — Two memory shapes, opposite costs. The Expert is pulled on demand (cheap until consulted). AGENTS.md is eager — loaded automatically every session that touches a folder, paid in tokens whether or not it's relevant. So the bar for putting something in AGENTS.md is far higher than for the Expert.
- P3 — The four destinations. Every fact worth remembering routes to exactly
one place: a lint (if mechanically checkable), eager prose (AGENTS.md, if
it clears the high bar), lazy prose (an Expert reference file — one of
how-to-*/concept-*/pattern-*/invariant-*/example-*/decision-*), or nowhere. Most things go nowhere or to the Expert. Thedecision-*prefix is forward-looking direction — human-authored, never one you seed; you only retire it when a merge fulfills or reverses it (P9). Seereferences/routing-rules.mdandreferences/expert-structure.md. - P4 — Map, not encyclopedia. AGENTS.md is the table of contents that points
into the Expert; it never duplicates it. A monolithic AGENTS.md rots, crowds
out the task, and turns "everything important" into "nothing important." Keep it
a map. See
references/agents-md-guidance.md. - P5 — Progressive disclosure inside the Expert. Reference files are small and
topic-focused, cross-linked via Obsidian
[[wikilinks]]. SKILL.md is an index — one line per file. An agent reads the index, opens only what's relevant, then follows wikilinks to discover related context. Seereferences/expert-structure.mdandreferences/wikilink-convention.md. - P6 — Invariants are discovered, then promoted. You may notice architectural
rules the codebase upholds. Record each as its own
invariant-<rule>.mdfile (one rule per file); flag the mechanically checkable ones as candidate lints (the highest-value memory, because a lint is a rule the agent cannot ship past). Seereferences/invariant-discovery.md. - P7 — Human-authored memory edits are authoritative. When the merged diff already touches AGENTS.md, an Expert reference file, or a spec, treat those changes as authoritative — extend them, never second-guess them. They came from a human resolving a STUCK or making a deliberate correction. Your job there is to extend (what else, given this correction, now needs to change?) — not to vote on whether to apply it.
- P8 — Reviewable, revertible, human-merged. Everything lands on a
learn/<sha>PR. Never auto-merge. - P9 — Reconcile, don't accumulate. Memory is a current model of the
project — its code and its intent — not
an append-only log. Every run must look for deleted concepts (the code is gone →
the reference file goes), inter-file contradictions (two files disagree → merge
or scope-qualify), and claims invalidated by the merged diff. Adds, edits, and
deletes all ride on the same PR. See
references/reconcile.md. - P10 — Nothing to learn is a valid outcome. Most merges produce nothing for
memory. When
reconcilefinds zero adds, edits, deletes, AGENTS.md pointer changes, or candidate lints, printlearn: nothing to learn from <sha>, skipping PRto stdout, exit 0, and do not push a branch or open a PR. The outer loop advancesrefs/harness/last-learnedregardless.
What ships with it
8 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.
- references/agents-md-guidance.md 2.2 KB
- references/expert-structure.md 7.1 KB
- references/invariant-discovery.md 5.4 KB
- references/reconcile.md 7.5 KB
- references/routing-rules.md 5.5 KB
- references/wikilink-convention.md 2.6 KB
- scripts/check-agents-md.sh 2.6 KB runs code
- scripts/check-expert-links.sh 3.3 KB runs code
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.
- 2d ago First seen · 251 lines · 120 tokens per session scan B dc4ea57135c1
learn is a skill published in the GitHub repository capitalone/context-specs (41 stars, last pushed 8d ago), licensed Apache-2.0. It adds 120 tokens to every session and 3,869 once invoked, about $0.0006 per session on Opus 5. A static security scan graded it B with 1 finding (enumerates other installed skills). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
brainstorming
You MUST use this before any creative work - creating features, building components, adding functionality, or modifying behavior. Explores user intent, requirements and design before implementation.
auto-perf-optimize
Run agent-driven VS Code performance or memory investigations. Use when asked to launch Code OSS, automate a VS Code scenario, run the Chat memory smoke runner, capture renderer heap snapshots, take workflow screenshots, compare run summaries, or drive a repeatable scenario before heap-snapshot analysis.
chat-perf
Run chat perf benchmarks and memory leak checks against the local dev build or any published VS Code version. Use when investigating chat rendering regressions, validating perf-sensitive changes to chat UI, or checking for memory leaks in the chat response pipeline.
chat-pet-sprite-creation
Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.
cpu-profile-analysis
Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…