Skill: sirsws/dae-persona-context-injector (version 1.0.3)

--- name: "dae-persona-context-injector" description: "Build a reusable persona profile for AI agents before planning, writing, coding, research, or advisory work. DaE uses structured dialogue and context injection to reduce generic answers and improve downstream collaboration." --- # DaE Persona Context Injector DaE is an upstream profiling skill for the agent era. It does not provide strategy, coaching, or direct advice. Its job is narrower and more useful: build a reusable `PersonaProfile` that downstream agents can read before they do any real work. ## Quick Reference | Item | Details | | --- | --- | | Primary outcome | reusable `PersonaProfile` | | Best use case | when downstream agents keep giving generic answers because they lack operator context | | Public source | `https://github.com/sirsws/dae-persona-context-injector` | | Public benchmark | `https://github.com/sirsws/dae-persona-context-injector/blob/main/benchmark/Steve-Jobs.md` | | Research paper | `https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5961054` | ## Public Links - GitHub repository: `https://github.com/sirsws/dae-persona-context-injector` - Benchmark write-up: `https://github.com/sirsws/dae-persona-context-injector/blob/main/benchmark/Steve-Jobs.md` - Benchmark profile: `https://github.com/sirsws/dae-persona-context-injector/blob/main/benchmark/Steve-Jobs-profile.md` - SSRN paper: `https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5961054` ## Installation ClawHub / OpenClaw: - install from this listing, or - use the source package from [REDACTED_SECRET] Skills CLI: ```bash npx skills add https://github.com/sirsws/dae-persona-context-injector --skill dae-persona-context-injector ``` ## What This Skill Changes Without DaE, downstream models often start work before they understand the operator. With DaE, the model works from a reusable profile asset first: - less generic output - better operator fit - more stable downstream collaboration ## When to use Use this skill when: - the user wants downstream agents to stop giving generic answers - the user needs a reusable profile file for repeated AI collaboration - the user wants structured elicitation before planning or execution - the task requires understanding the operator's goals, constraints, trade-offs, tensions, and decision style Do not use this skill when: - the user only wants a quick answer - the user wants direct life advice from the skill itself - the task is pure execution and a valid `PersonaProfile` already exists ## Core rule **Profile first. Then collaborate.** DaE is a front-loaded context injector. Other skills execute. DaE prepares the context they execute against. ## Operating boundary DaE only does elicitation and profiling. DaE actively challenges self-descriptions. It asks for concrete events, tests claimed values against actual trade-offs, and flags gaps rather than accepting vague answers. If the user asks for strategy or recommendations during the dialogue, respond with: `That question belongs to the downstream advisor after the profile is complete. For now, we are still building the profile.` ## Workflow 1. State the opening contract briefly: - this is a cognitive intake, not casual chat - sensitive questions can be skipped - skipped data must be marked, never guessed - the final output is meant to be reusable by other agents 2. Run the four phases: - `Phase 1`: quick baseline intake - `Phase 2`: structured deep dive on the most critical current issue - `Phase 3`: whole-profile sweep - `Phase 4`: final output 3. Default outputs: - human-readable profile summary - long-form `PersonaProfile` 4. Generate JSON only when the user explicitly wants machine-readable output or says it will be loaded into another agent or system 5. If the user stops early, output the partial profile with explicit `Insufficient` or `UserWithheld` markers ## Output contract The final profile must cover all of these fields: - `Background` - `Capabilities` - `Resources` - `Constraints` - `Drives` - `Goals` - `DecisionStyle` - `Weaknesses` - `Tensions` - `Challenges` - `Lessons` - `AlignmentCheck` Every major judgment should carry: - `evidence` - `confidence` - `status` Allowed `status` values: - `Confirmed` - `Inferred` - `UserWithheld` - `Insufficient` ## Safety and trust - local profile generation only - no hidden exfiltration logic - no unrelated system privileges - no credential collection - no shell execution requirement - public demos should use non-sensitive or historical subjects Real user profiles should be treated as local private configuration assets. ## Files to load Read these references before running the skill: - `references/DaE_Skill_Prompt_en.md` - `references/DaE_v2_acceptance_criteria_en.md` Use the prompt file as the execution source. Use the acceptance file as the quality gate. ## Benchmark summary Public benchmark subject: `Steve Jobs` Headline test: **How should Steve Jobs rebuild Apple after returning to the company?** Observed effect: - without profile: generic turnaround framing - with DaE profile: control-first, trade-off-aware reasoning This is the point of DaE: not to make the model smarter in general, but less generic for a specific operator.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)