Individual skill

portfolio-env-analysis

Answer analytics questions against the shared read-only "portfolio" HTTP environment (work items, mix targets, SLA policy, releases, milestones, blockers, dependencies) and return a single strict-JSON answer matching a provided answer_template.json. Use for the three recurring task families: (1) quarterly portfolio-mix reviews/readouts (category mix vs a mix_targets row), (2) SLA aging/breach audits for reliability & security work, and (3) release-readiness assessments. Triggers whenever a task references <TASK_ENV_BASE_URL> / environment_access.md plus /api/work-items and asks for category mix, SLA/overdue/breach, or ship-readiness, with a payloads/ answer_template.json to conform to.

experiments/claude_code_opus_4_8_xhigh/reports/skills/task_group_024/self/self_attempt_01/SKILL.md

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Source commit 5082a777379156a048881e94940d640702e5e09a

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Last checked: Nov 6, 2026

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Repository stars (not skill ratings): 121
Repository last updated: Oct 29, 2026
License: Apache-2.0

Observed agent integration: No specific integration evidence. Standard SKILL.md installation does not imply verified compatibility.

agent Repository topic
ai Repository topic
benchmark Repository topic
llm Repository topic
self-evolving Repository topic
self-learning Repository topic
skills Repository topic
data Derived category
security Derived category

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SKILL.mdcore · 10,986 bytes
references/data-model.mddoc · 8,282 bytes

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# Portfolio environment analysis

You are given a task prompt, an `environment_access.md` with the base URL/endpoints/token,
and an answer template at `payloads/answer_template.json` (prompts reference it as
`input/payloads/answer_template.json`). Query the shared environment, apply the operating
rules below, and emit **only** the JSON the template requires — no prose.

Read `references/data-model.md` first: it is the field-authority map, category
conventions, enum vocabularies, and distractor catalog that the rules below depend on.

## The one principle that runs through every task

The environment is seeded with **decoy fields and adversarial signals**. Winning means
using authoritative fields and ignoring the decoys, consistently: