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/address-feedbacknpx skills add capitalone/context-specs --skill address-feedbackgit clone --depth 1 https://github.com/capitalone/context-specsWrote 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/capitalone/context-specs/address-feedback)<a href="https://agentmods.dev/skills/capitalone/context-specs/address-feedback"><img src="https://agentmods.dev/badge/skills/capitalone/context-specs/address-feedback.svg" alt="Measured on agentmods" 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 | $0.00123 | $0.02214 |
| Opus 5 | $0.00062 | $0.01107 |
| Sonnet 5 | $0.00025 | $0.00443 |
| Haiku 4.5 | $0.00012 | $0.00221 |
Grade A, and why
address-feedback 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 4d 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 — 145 lines — stays where its author put it; the contents beside it link to each section on GitHub.
address-feedback
The responder half of the PR review cycle. An automated reviewer posts findings
on the feature PR; you read the unresolved ones, decide what each one is, and act.
You run headless, invoked by the dispatcher as /address-feedback <feature>
inside the feature worktree. One focused pass, then exit.
By the time you run, the feature already works — ./prds/<f>/run-prd-test.sh exits 0
and a PR is open against main. Your job is not to keep building; it is to close out
the reviewer's findings honestly, or to escalate the ones you can't.
The one-way model (read this first — it shapes everything)
The reviewer is non-conversational. Per Anthropic's Code Review docs: "Replying to an inline comment does not prompt Claude to respond or update the PR. To act on a finding, fix the code and push." Two consequences drive this whole skill:
- A Clear fix signals back through the diff, not a thread reply. You fix the cause, commit, push. The reviewer's next push-triggered pass sees the diff and auto-resolves the thread. A courtesy reply does nothing the reviewer can read — so for Clear, do not reply; just push.
- Only a push shrinks the reviewer's unresolved set. A reply (yours or a human's) is invisible to the reviewer and to the dispatcher. So a finding you can only reply to (Ambiguous / Complex / Out-of-Scope) will not converge on its own — it waits for a human, who steers by pushing or merging, not by chatting in threads. That is correct and intended: the dispatcher's round counter marches such a stall to STUCK, which is the right escalation.
You are the agent's self-correction sensor for the bot reviewer. Act only on the automated reviewer's findings. Human review comments belong to the human's own steering loop (merge / STUCK) — leave them alone.
Ground yourself before classifying (you are a coordinator, not a scope authority)
Two sources govern every triage call. Load both before you classify anything:
What ships with it
2 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.
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.
- 4d ago First seen · 145 lines · 123 tokens per session scan A 2aa829bda849
address-feedback is a skill published in the GitHub repository capitalone/context-specs (41 stars, last pushed 10d ago), licensed Apache-2.0. It adds 123 tokens to every session and 2,214 once invoked, about $0.0006 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 2026-08-30.
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…