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 dean0x/devflow --skill apply-feature-knowledgegit clone --depth 1 https://github.com/dean0x/devflowWrote 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/dean0x/devflow/apply-feature-knowledge)<a href="https://agentmods.dev/skills/dean0x/devflow/apply-feature-knowledge"><img src="https://agentmods.dev/badge/skills/dean0x/devflow/apply-feature-knowledge/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/dean0x/devflow/apply-feature-knowledge"><img src="https://agentmods.dev/badge/skills/dean0x/devflow/apply-feature-knowledge.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 | $0.00018 | $0.00383 |
| Opus 5.5 | $0.00007 | $0.00153 |
| Sonnet 5.5 | $0.00004 | $0.00077 |
| Haiku 4.5 | $0.00002 | $0.00038 |
Grade A, and why
apply-feature-knowledge 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 6d 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
Apply Feature Knowledge
Iron Law
Pre-computed context, not a cage. Verify against current code — always.
A feature knowledge captures patterns AS THEY WERE when last written. Use it as a starting point, not gospel truth; when something feels off, Read the actual files. Code is authoritative.
3-Step Algorithm
- Read each
FEATURE_KNOWLEDGEblock (headed--- Feature knowledge: {slug} ---):Rules:holds anti-patterns, gotchas and invariants;KB:is the full file's path (underWORKTREE_PATHwhen given). - Apply them: check your work against every bullet and cite it as
{slug} KB-AP-n(a section-labelled bullet as{slug} {section}), never a bare ID. For architecture or integration context, Read that section on demand, withoffsetandlimit: its start line is on theHeadings:line, or comes fromcommand grep -n '^## ' "<KB path>". Quote KB text only from a Read view. - Verify against current code: where an assertion is outdated or contradicts the code, Read the source and trust it. Note a discrepancy in your output when it matters.
Skip Guard
When FEATURE_KNOWLEDGE is (none), empty, or not provided — skip this skill entirely.
Do not mention feature knowledge or its absence in your output.
Freshness Model
Feature knowledge is verify-on-read: check key assertions against current code, and when in doubt Read the file.
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.
- 6d ago Changed 698641117f27
- 7d ago Changed · -34 lines · -2 tokens per session 773a2ee0a49e
- 1mo ago First seen · 70 lines · 20 tokens per session scan A 584e83392c19
apply-feature-knowledge is a skill published in the GitHub repository dean0x/devflow (20 stars, last pushed today), licensed MIT. It adds 18 tokens to every session and 383 once invoked, about $0.0001 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.
Other skills, from other repositories
find-simplifications
Use for a periodic repo-wide sweep of qwen-code for accumulated excess surface — dead components and files, orphaned locale keys, exports nothing consumes, added-then-removed scaffolding — filing candidates on a tracking issue and landing only what a maintainer has said yes to. Repo-wide and evidence-first; every…
swarm-pr-review
Run a graph-guided, tool-augmented PR review using context packing, parallel exploration, mandatory repository-agnostic risk-family coverage with dispatch scaled to diff size and risk, independent reviewer validation, critic challenge, and metrics writeback. Use for deep pull request review with low false-positive…
pr-review-canvas
Create a OpenBitFun Canvas for reviewing a pull request, branch diff, or change set with Cursor-style diff cards, review maps, risk callouts, and focused reviewer flow. Use when the user asks for a PR review canvas, diff walkthrough, change-set overview, or visual review summary.
code-review
You are a senior code reviewer. You receive diffs via stdin.
review
Adversarial fresh-context review and risk check of an increment before it ships; every finding cites path:line and is re-verified. Use for "review", "qa", "quality check", "critique this".
tech-debt-ci-review
Codex adapter for deep technical-debt and CI-stability audits. Use when asked to find test theater, flaky tests, missing or mis-scoped tests, brittle CI/toolchain behavior, structural debt blocking green PRs, or a remediation order for opencode-swarm.