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/zeeshanhanif/agentic-sdlc-kit/feature-implementationnpx skills add zeeshanhanif/agentic-sdlc-kit --skill feature-implementationgit clone --depth 1 https://github.com/zeeshanhanif/agentic-sdlc-kitWrote 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/zeeshanhanif/agentic-sdlc-kit/feature-implementation)<a href="https://agentmods.dev/skills/zeeshanhanif/agentic-sdlc-kit/feature-implementation"><img src="https://agentmods.dev/badge/skills/zeeshanhanif/agentic-sdlc-kit/feature-implementation.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 | $0.00214 | $0.02300 |
| Opus 5 | $0.00107 | $0.01150 |
| Sonnet 5 | $0.00043 | $0.00460 |
| Haiku 4.5 | $0.00021 | $0.00230 |
Grade A, and why
feature-implementation 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 3d 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 — 177 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Feature Implementation
The construction step of the per-feature loop: documents become code.
tasks.md is the program; this skill is the interpreter. It executes the
feature's tasks in order against technical-design.md (contracts, schema,
components), ui-design.md + the design manifest (screens), and the live
codebase — producing working, tested, committed code that replaces the
skeleton's stubs, ending developer-done: every task checked, including the
final verification task. The independent check is downstream
(acceptance verification); this skill never grades its own homework alone.
Seven disciplines make it rock-solid. Each targets a named failure mode of agentic implementation:
- The program is fixed; improvisation is escalation. Implement what
tasks.md says, per the design documents. Reality will sometimes beat the
design — that has two paths, never a third: small and design-consistent →
fix and record; anything changing a contract, schema, or acceptance
criterion → escalate to the design's amendment path (see
references/failure-and-escalation.md). The design documents stay true, or they change through their owner. (Kills: quietly designing around divergence.) - Fresh-context safety — disk is the memory. Every iteration is executable in a brand-new session: read tasks.md (checkbox state = exact position), read the designs, read the code, do one task, update state on disk, commit. No iteration depends on chat history. (Kills: context loss mid-feature; enables loop orchestration.)
- Done-when is demonstrated, never asserted. A checkbox flips only after the task's done-when actually ran and passed — the migration applied clean, the tests passed, the flow's E2E spec passed. And the anti-fake-green rule: tests are never weakened, skipped, deleted, or edited to make them pass; a failing test means the code or the design is wrong, and each has its path (fix, or escalate). (Kills: the single most corrosive agent failure — manufacturing green.)
- Bounded fix-loops — never thrash. Diagnose, fix, re-run — at most 3 attempts per task (per-project override: see Inputs). Exhausted → stop, leave the box unchecked, record the failure state honestly, WIP- commit, surface it. (Kills: infinite loops and desperation hacks.)
- Scope discipline — this feature only. Touch what the tasks require. No drive-by refactors, no "while I'm here" improvements, no reformatting neighboring code. Genuine tech-debt discoveries are recorded (the feature's escalations section), never acted on. (Kills: scope creep and unreviewable diffs.)
- Convention conformance by construction. Code follows the conventions technical-design §2 surveyed. UI realizes screens from ui-design.md/the manifest through the design system — tokens, never raw values; design.md's do/don'ts are binding — with the screenshot loop for code-native screens (bounded, per discipline 4). Boundary/lint rules run as part of every task's verification, not at the end. (Kills: convention drift and design-system forks.)
- Git is the checkpoint mechanism. Commit per completed task —
FEAT-004 T3: implement POST /auth/login contract— so every task is a recovery point, history is a readable execution log, and an orchestrator resumes from durable state. A blocked task WIP-commits, explicitly marked (FEAT-004 T4: WIP — blocked, see tasks.md note); a failing state is never committed as if green. (Kills: lost work and unrecoverable sessions.)
What ships with it
3 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.
- 3d ago First seen · 177 lines · 214 tokens per session scan A f73a2f2c7500
feature-implementation is a skill published in the GitHub repository zeeshanhanif/agentic-sdlc-kit (5 stars, last pushed 7d ago), licensed Apache-2.0. It adds 214 tokens to every session and 2,300 once invoked, about $0.0011 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 2026-08-31.
Other skills, from other repositories
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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.
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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…