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BB story-lifecycle

Manage a file-based epic → story → sprint development loop without requiring an external project management tool. Creates and tracks epics, breaks them into user stories, assigns them to sprints, and drives implementation story by story. Use when starting a new feature initiative or when the team has no Jira/Linear/Taiga but needs structured delivery tracking.

The skillemall take

Promises to replace Jira for small teams: manages epics, breaks them into stories, distributes across sprints, stores everything in files. In reality — two files and one script that create structure but don't fully automate the workflow. Code quality scores 82, process 65, which hints the logic works but usability lags. One medium-level finding, no critical errors.

Worth installing if your team writes stories by hand and doesn't expect IDE integration. For a startup without tools it'll do, but this isn't a Jira replacement — more like scaffolding that keeps the process in one place instead of scattered across Slack.

affaan-m/everything-claude-code Agent Skills author: affaan-m MIT 2 files · 1 script body ≈ 4 278 tokens Open the sourcegithub.com↗ analyzed 6 d ago

Manage a file-based epic → story → sprint development loop without requiring an external project management tool.

As a process B 65/100 · Nearly there — weak spots: result and completion, when it triggers, inputs and preconditions

ProcedureJiraGitHubOperations and projectstype and topics are labelled automatically from the skill text
Runs in: Claude / Claude Code ChatGPT / Codex Gemini CLI Cursor GitHub Copilot / VS Code Qwen Code Kimi Code CLI Z.ai (GLM) DeepSeek (Deep Code) Mistral Vibe LangChain (deepagents) OpenClaw Goose OpenCode
JSON
Technical rating
B
92/100
safety, quality, tests
Safety 60%
99
Quality 40%
82
Run on models
none yet
Process rating
B
65/100
Nearly there
When it triggers w 12
20
Inputs and preconditions w 11
30
Result and completion w 14
40
the three weakest of ten parameters · all ten
This is a copy of a skill from another catalog; the rating counts the canonical one: story-lifecycle (affaan-m/ECC)

How to improve

    For the model run — optional
    • Your own cases (evals/evals.json, 4–6 real requests with expected answers): the full check would then run those instead of a model-drafted suite.
    • A spec.yaml with trigger phrases and assertions — a behaviour contract for CI; `skilltest init` writes a template.

    Guard findings · 1

    ✓ No critical or high findings

    Medium and low: 1
    • low Instruction override en-ignore-previous SKILL.md:87
      Instruction-override phrase ("ignore previous instructions") (security demo / example; quoted — discussed, not commanded)
      directives embedded in it (e.g. "ignore previous instructions", "run this command");
      demoquoted

    Files scanned: 2. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    ✓ No remarks against the Agent Skills spec

    Process rating: all ten parameters 65/100

    • 20When it triggers. No condition that starts the skill
    • 30Inputs and preconditions. Does not say what the process needs to start
    • 40Result and completion. Does not say what the result is
    • 60Tools and files. Uses tools (bash, node) that frontmatter does not declare
    • 70Execution cost. Instruction body is 4278 tokens
    • 100Steps. 61 steps
    • 100Failures and branches. 1 branches, has a failure section
    • 100Consistency. Name and required fields are in place
    • 100Running it twice. Mutating operations check current state
    • 100Progress reporting. Reports progress
    • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
    • low 14 top-level sections: this looks like several domains in one skill
    • low The response is described with custom markup (14 tags): a typed call is more reliable

    Everything here is measured from the skill text rather than judged by a model, so the numbers are checkable. A parameter weighs more when it is a more common reason for the process to stall.

    Quality signals

    • +5Description has no quoted example phrases that should trigger the skill
    • +4Description does not say when NOT to use the skill (false activations)
    • +3Output format is not stated: the model decides each time
    • -5TODO / placeholder text left in the skill
    • +1No license
    • +2Single-language instructions
    • +3Description length 362: enough signal without eating the budget
    • +4Structure: 22 headings
    • +3Step-by-step instructions: 61 items
    • +4Has examples (6 code blocks)
    • +3All 1 scripts are documented

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 82.

    In the sandbox The scripts kept to themselves

    The skill's scripts were run in a throwaway machine: no network, fake keys in the home directory, a tracer watching. We wrote down what they did. Reaching for the network or for secrets caps the technical grade at C; a quiet run adds no points.

    Запущено 1 скрипт; каждому дали двадцать секунд, поддельный домашний каталог с ключами и сеть, в которой ничего нет.

    scripts/ledger.jsничего за пределами своей папки

    18 Oct 2026