task-target-alignment-eval

A project-local evaluation tool for checking whether an agent correctly matches tasks to their intended targets and safety levels.

In plain words
What is it for?
It helps run fresh-context evaluations, compare task-alignment cases, validate fixtures, and score results.
Why use it?
It provides a repeatable way to test agent decisions instead of judging them informally.

Skill for Claude CodeCodex

Part of the csl plugin — 33 skills, 2 commands, 5 agents, 10 hooks shipped together

Install

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.

agentmods
npx agentmods add skills/ssbun/csl-agent-kit/task-target-alignment-eval
Any agent
npx skills add SSBun/csl-agent-kit --skill task-target-alignment-eval
Clone the repo
git clone --depth 1 https://github.com/SSBun/csl-agent-kit

Made for: Claude Code, Codex.

Or install csl, the plugin that ships this one along with the rest of its 33 skills, 2 commands, 5 agents, 10 hooks.

Per session 40 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 830 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5 $0.00040 $0.00830
Opus 5 $0.00020 $0.00415
Sonnet 5 $0.00008 $0.00166
Haiku 4.5 $0.00004 $0.00083

Measured 3d ago against content hash 1a3d1acd75a9, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

task-target-alignment-eval 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.

evals/skills/task-target-alignment-eval/SKILL.md · 56 lines

How it starts

The opening of the file, as written. The whole thing — 56 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Task Target Alignment Eval

Operate this project-local suite without shared Skill distribution.

Workspace Boundary

  1. Resolve the workspace with git rev-parse --show-toplevel.
  2. Read <workspace>/evals/README.md, <workspace>/evals/task-target-alignment/README.md, and the authoritative protocol at <workspace>/skills/meta/csl-tasks/shared/protocols/task-target-alignment.md.
  3. Treat <workspace>/evals/ as canonical. The .agents/skills/task-target-alignment-eval entry is discovery-only and must remain a relative symlink to this package.
  4. Never copy this Skill into <workspace>/skills/, a global Skill directory, package manifests, or installer enumeration.

Workflow

  1. Run node <workspace>/evals/scripts/check-project-evals.js, then run the evaluator's validate command before relying on the suite.
  2. For fixture work, keep stable ASCII case IDs, versioned schemas, provisional oracle labels, two-variant contrast scenarios, allowedDecisions, risk, commitment-difference dimensions, reason requirements, and Safety Overlay. Keep gateMode report-only until human adjudication.
  3. Keep deterministic validation and scoring separate from model execution. Use prepare to create oracle-free request JSONL and score or compare only with observed prediction/report files; never fabricate eval results.
  4. For Pi model runs, use one parent-owned async workflowScript with the exact model under test and fresh-context evaluator children. For the 64-case baseline, split oracle-free packets into 16 unrelated batches of four and run three fresh repeats (48 children), using waves when the effective spawn or concurrency cap is lower. Randomize batch order; never let one child process the entire corpus or expose gold labels.
  5. Save generated predictions and reports only under the suite's ignored results/ directory unless the user explicitly approves a durable artifact.
  6. Report under-guard, over-guard, L2 checkpoint, L3/L4 mismatch, Safety Overlay, reason completeness, transition, family, and stability metrics separately; never hide opposing regressions behind one aggregate score or present a provisional report as release approval.

Read the full file on GitHub · 56 lines

Changes

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.

  1. 3d ago First seen · 56 lines · 40 tokens per session scan A 1a3d1acd75a9

Subscribe to this mod's changes

task-target-alignment-eval is a skill published in the GitHub repository SSBun/csl-agent-kit (10 stars, last pushed 4d ago), licensed MIT. It adds 40 tokens to every session and 830 once invoked, about $0.0002 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.

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